EP1285244A4 - Appareil et procede de mesure et de correlation de caracteristiques de fruits avec un spectre visible/infrarouge proche - Google Patents
Appareil et procede de mesure et de correlation de caracteristiques de fruits avec un spectre visible/infrarouge procheInfo
- Publication number
- EP1285244A4 EP1285244A4 EP01918659A EP01918659A EP1285244A4 EP 1285244 A4 EP1285244 A4 EP 1285244A4 EP 01918659 A EP01918659 A EP 01918659A EP 01918659 A EP01918659 A EP 01918659A EP 1285244 A4 EP1285244 A4 EP 1285244A4
- Authority
- EP
- European Patent Office
- Prior art keywords
- sample
- light
- spectrum
- spectrometer
- detector
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000000034 method Methods 0.000 title claims abstract description 144
- 235000013399 edible fruits Nutrition 0.000 title claims abstract description 99
- 238000002329 infrared spectrum Methods 0.000 title claims description 11
- 238000001228 spectrum Methods 0.000 claims abstract description 333
- 238000005259 measurement Methods 0.000 claims abstract description 101
- 230000007547 defect Effects 0.000 claims abstract description 22
- 235000019804 chlorophyll Nutrition 0.000 claims abstract description 18
- 229910052739 hydrogen Inorganic materials 0.000 claims abstract description 7
- 239000000835 fiber Substances 0.000 claims description 86
- 238000005286 illumination Methods 0.000 claims description 63
- 238000005070 sampling Methods 0.000 claims description 58
- 238000001514 detection method Methods 0.000 claims description 43
- 238000000862 absorption spectrum Methods 0.000 claims description 40
- 238000012856 packing Methods 0.000 claims description 37
- 230000005540 biological transmission Effects 0.000 claims description 35
- 238000004590 computer program Methods 0.000 claims description 34
- 238000004422 calculation algorithm Methods 0.000 claims description 30
- 235000000346 sugar Nutrition 0.000 claims description 26
- 230000003595 spectral effect Effects 0.000 claims description 24
- 238000004458 analytical method Methods 0.000 claims description 21
- 229910052736 halogen Inorganic materials 0.000 claims description 20
- 229910052721 tungsten Inorganic materials 0.000 claims description 20
- 239000010937 tungsten Substances 0.000 claims description 20
- 230000033001 locomotion Effects 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 18
- 238000005096 rolling process Methods 0.000 claims description 18
- -1 tungsten halogen Chemical class 0.000 claims description 18
- 230000006870 function Effects 0.000 claims description 15
- 239000000463 material Substances 0.000 claims description 14
- 230000010287 polarization Effects 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 11
- 230000007246 mechanism Effects 0.000 claims description 10
- 238000002835 absorbance Methods 0.000 claims description 9
- 238000003780 insertion Methods 0.000 claims description 9
- 230000037431 insertion Effects 0.000 claims description 9
- 239000000126 substance Substances 0.000 claims description 9
- 238000012360 testing method Methods 0.000 claims description 9
- 238000003306 harvesting Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 7
- 239000002253 acid Substances 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 239000000470 constituent Substances 0.000 claims description 6
- 238000009499 grossing Methods 0.000 claims description 6
- 238000007689 inspection Methods 0.000 claims description 6
- 238000011545 laboratory measurement Methods 0.000 claims description 6
- 235000013311 vegetables Nutrition 0.000 claims description 6
- 230000001066 destructive effect Effects 0.000 claims description 5
- 230000000717 retained effect Effects 0.000 claims description 5
- 230000003287 optical effect Effects 0.000 claims description 4
- 230000009471 action Effects 0.000 claims description 3
- 125000003636 chemical group Chemical group 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 3
- 239000011368 organic material Substances 0.000 claims description 3
- 230000036961 partial effect Effects 0.000 claims description 3
- 229920000642 polymer Polymers 0.000 claims description 3
- 238000011002 quantification Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims description 2
- 235000009508 confectionery Nutrition 0.000 claims description 2
- 238000001816 cooling Methods 0.000 claims description 2
- 230000001939 inductive effect Effects 0.000 claims description 2
- 230000010354 integration Effects 0.000 claims description 2
- 238000011005 laboratory method Methods 0.000 claims description 2
- TVEXGJYMHHTVKP-UHFFFAOYSA-N 6-oxabicyclo[3.2.1]oct-3-en-7-one Chemical compound C1C2C(=O)OC1C=CC2 TVEXGJYMHHTVKP-UHFFFAOYSA-N 0.000 claims 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 abstract description 16
- 239000000049 pigment Substances 0.000 abstract description 11
- 229930002877 anthocyanin Natural products 0.000 abstract description 6
- 235000010208 anthocyanin Nutrition 0.000 abstract description 6
- 239000004410 anthocyanin Substances 0.000 abstract description 6
- 150000004636 anthocyanins Chemical class 0.000 abstract description 6
- 235000008210 xanthophylls Nutrition 0.000 abstract description 6
- 208000034656 Contusions Diseases 0.000 abstract description 2
- 206010053615 Thermal burn Diseases 0.000 abstract description 2
- KBPHJBAIARWVSC-RGZFRNHPSA-N lutein Chemical compound C([C@H](O)CC=1C)C(C)(C)C=1\C=C\C(\C)=C\C=C\C(\C)=C\C=C\C=C(/C)\C=C\C=C(/C)\C=C\[C@H]1C(C)=C[C@H](O)CC1(C)C KBPHJBAIARWVSC-RGZFRNHPSA-N 0.000 abstract description 2
- 229960005375 lutein Drugs 0.000 abstract description 2
- 230000037390 scarring Effects 0.000 abstract description 2
- KBPHJBAIARWVSC-XQIHNALSSA-N trans-lutein Natural products CC(=C/C=C/C=C(C)/C=C/C=C(C)/C=C/C1=C(C)CC(O)CC1(C)C)C=CC=C(/C)C=CC2C(=CC(O)CC2(C)C)C KBPHJBAIARWVSC-XQIHNALSSA-N 0.000 abstract description 2
- FJHBOVDFOQMZRV-XQIHNALSSA-N xanthophyll Natural products CC(=C/C=C/C=C(C)/C=C/C=C(C)/C=C/C1=C(C)CC(O)CC1(C)C)C=CC=C(/C)C=CC2C=C(C)C(O)CC2(C)C FJHBOVDFOQMZRV-XQIHNALSSA-N 0.000 abstract description 2
- 239000000523 sample Substances 0.000 description 434
- 241000220225 Malus Species 0.000 description 51
- 235000021016 apples Nutrition 0.000 description 21
- 230000009977 dual effect Effects 0.000 description 20
- 238000010586 diagram Methods 0.000 description 14
- 229930002875 chlorophyll Natural products 0.000 description 12
- ATNHDLDRLWWWCB-AENOIHSZSA-M chlorophyll a Chemical compound C1([C@@H](C(=O)OC)C(=O)C2=C3C)=C2N2C3=CC(C(CC)=C3C)=[N+]4C3=CC3=C(C=C)C(C)=C5N3[Mg-2]42[N+]2=C1[C@@H](CCC(=O)OC\C=C(/C)CCC[C@H](C)CCC[C@H](C)CCCC(C)C)[C@H](C)C2=C5 ATNHDLDRLWWWCB-AENOIHSZSA-M 0.000 description 12
- 230000005855 radiation Effects 0.000 description 12
- 238000013459 approach Methods 0.000 description 11
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 11
- 239000006260 foam Substances 0.000 description 9
- 241000167854 Bourreria succulenta Species 0.000 description 8
- 238000010521 absorption reaction Methods 0.000 description 8
- 235000019693 cherries Nutrition 0.000 description 8
- 238000004497 NIR spectroscopy Methods 0.000 description 7
- 241000219094 Vitaceae Species 0.000 description 6
- 230000000694 effects Effects 0.000 description 6
- 235000012055 fruits and vegetables Nutrition 0.000 description 6
- 235000021021 grapes Nutrition 0.000 description 6
- 230000000670 limiting effect Effects 0.000 description 6
- 239000001054 red pigment Substances 0.000 description 6
- 239000007787 solid Substances 0.000 description 6
- 239000001052 yellow pigment Substances 0.000 description 6
- 238000000149 argon plasma sintering Methods 0.000 description 5
- KRKNYBCHXYNGOX-UHFFFAOYSA-N citric acid Natural products OC(=O)CC(O)(C(O)=O)CC(O)=O KRKNYBCHXYNGOX-UHFFFAOYSA-N 0.000 description 5
- 238000007635 classification algorithm Methods 0.000 description 5
- 238000012937 correction Methods 0.000 description 5
- 230000001419 dependent effect Effects 0.000 description 5
- 239000001257 hydrogen Substances 0.000 description 5
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 4
- 150000001298 alcohols Chemical class 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 230000002596 correlated effect Effects 0.000 description 4
- 238000007726 management method Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 238000007789 sealing Methods 0.000 description 4
- 150000003735 xanthophylls Chemical class 0.000 description 4
- 241001672694 Citrus reticulata Species 0.000 description 3
- 241000196324 Embryophyta Species 0.000 description 3
- 241000238631 Hexapoda Species 0.000 description 3
- 206010061217 Infestation Diseases 0.000 description 3
- 230000000903 blocking effect Effects 0.000 description 3
- 230000015556 catabolic process Effects 0.000 description 3
- 229920001971 elastomer Polymers 0.000 description 3
- 238000001914 filtration Methods 0.000 description 3
- 235000021022 fresh fruits Nutrition 0.000 description 3
- 235000011389 fruit/vegetable juice Nutrition 0.000 description 3
- 239000001056 green pigment Substances 0.000 description 3
- 150000002367 halogens Chemical class 0.000 description 3
- 230000031700 light absorption Effects 0.000 description 3
- 239000007788 liquid Substances 0.000 description 3
- 230000007935 neutral effect Effects 0.000 description 3
- 230000035515 penetration Effects 0.000 description 3
- 230000000704 physical effect Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 239000005060 rubber Substances 0.000 description 3
- 150000008163 sugars Chemical class 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- WFKWXMTUELFFGS-UHFFFAOYSA-N tungsten Chemical compound [W] WFKWXMTUELFFGS-UHFFFAOYSA-N 0.000 description 3
- BJEPYKJPYRNKOW-REOHCLBHSA-N (S)-malic acid Chemical compound OC(=O)[C@@H](O)CC(O)=O BJEPYKJPYRNKOW-REOHCLBHSA-N 0.000 description 2
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 2
- 229930091371 Fructose Natural products 0.000 description 2
- RFSUNEUAIZKAJO-ARQDHWQXSA-N Fructose Chemical compound OC[C@H]1O[C@](O)(CO)[C@@H](O)[C@@H]1O RFSUNEUAIZKAJO-ARQDHWQXSA-N 0.000 description 2
- 244000061456 Solanum tuberosum Species 0.000 description 2
- 235000002595 Solanum tuberosum Nutrition 0.000 description 2
- 229920000995 Spectralon Polymers 0.000 description 2
- 244000107946 Spondias cytherea Species 0.000 description 2
- FEWJPZIEWOKRBE-UHFFFAOYSA-N Tartaric acid Natural products [H+].[H+].[O-]C(=O)C(O)C(O)C([O-])=O FEWJPZIEWOKRBE-UHFFFAOYSA-N 0.000 description 2
- 239000004809 Teflon Substances 0.000 description 2
- 229920006362 Teflon® Polymers 0.000 description 2
- 150000007513 acids Chemical class 0.000 description 2
- BJEPYKJPYRNKOW-UHFFFAOYSA-N alpha-hydroxysuccinic acid Natural products OC(=O)C(O)CC(O)=O BJEPYKJPYRNKOW-UHFFFAOYSA-N 0.000 description 2
- 239000012491 analyte Substances 0.000 description 2
- 235000013405 beer Nutrition 0.000 description 2
- 239000001569 carbon dioxide Substances 0.000 description 2
- 229910002092 carbon dioxide Inorganic materials 0.000 description 2
- 235000013339 cereals Nutrition 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 108091000085 chlorophyll binding Proteins 0.000 description 2
- 238000002790 cross-validation Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 229960002737 fructose Drugs 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 239000001630 malic acid Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 238000010238 partial least squares regression Methods 0.000 description 2
- 235000012015 potatoes Nutrition 0.000 description 2
- 108090000623 proteins and genes Proteins 0.000 description 2
- 102000004169 proteins and genes Human genes 0.000 description 2
- 230000002829 reductive effect Effects 0.000 description 2
- 238000007430 reference method Methods 0.000 description 2
- 238000000611 regression analysis Methods 0.000 description 2
- 230000000284 resting effect Effects 0.000 description 2
- 238000005204 segregation Methods 0.000 description 2
- 235000002906 tartaric acid Nutrition 0.000 description 2
- 239000011975 tartaric acid Substances 0.000 description 2
- PXRKCOCTEMYUEG-UHFFFAOYSA-N 5-aminoisoindole-1,3-dione Chemical compound NC1=CC=C2C(=O)NC(=O)C2=C1 PXRKCOCTEMYUEG-UHFFFAOYSA-N 0.000 description 1
- 241000219310 Beta vulgaris subsp. vulgaris Species 0.000 description 1
- 229920002799 BoPET Polymers 0.000 description 1
- 241000283690 Bos taurus Species 0.000 description 1
- 244000241235 Citrullus lanatus Species 0.000 description 1
- 235000012828 Citrullus lanatus var citroides Nutrition 0.000 description 1
- 244000241257 Cucumis melo Species 0.000 description 1
- 235000009847 Cucumis melo var cantalupensis Nutrition 0.000 description 1
- 235000015001 Cucumis melo var inodorus Nutrition 0.000 description 1
- 240000002495 Cucumis melo var. inodorus Species 0.000 description 1
- FEWJPZIEWOKRBE-JCYAYHJZSA-N Dextrotartaric acid Chemical compound OC(=O)[C@H](O)[C@@H](O)C(O)=O FEWJPZIEWOKRBE-JCYAYHJZSA-N 0.000 description 1
- 239000005715 Fructose Substances 0.000 description 1
- 229910000530 Gallium indium arsenide Inorganic materials 0.000 description 1
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 description 1
- 241001124569 Lycaenidae Species 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 239000005041 Mylar™ Substances 0.000 description 1
- 238000013494 PH determination Methods 0.000 description 1
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 1
- 229920002472 Starch Polymers 0.000 description 1
- 229910000831 Steel Inorganic materials 0.000 description 1
- CZMRCDWAGMRECN-UGDNZRGBSA-N Sucrose Chemical compound O[C@H]1[C@H](O)[C@@H](CO)O[C@@]1(CO)O[C@@H]1[C@H](O)[C@@H](O)[C@H](O)[C@@H](CO)O1 CZMRCDWAGMRECN-UGDNZRGBSA-N 0.000 description 1
- 229930006000 Sucrose Natural products 0.000 description 1
- 235000021536 Sugar beet Nutrition 0.000 description 1
- 229910052770 Uranium Inorganic materials 0.000 description 1
- 235000009754 Vitis X bourquina Nutrition 0.000 description 1
- 235000012333 Vitis X labruscana Nutrition 0.000 description 1
- 244000070471 Vitis rupestris Species 0.000 description 1
- 235000004284 Vitis rupestris Nutrition 0.000 description 1
- 240000006365 Vitis vinifera Species 0.000 description 1
- 235000014787 Vitis vinifera Nutrition 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 229910052782 aluminium Inorganic materials 0.000 description 1
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 1
- 229910021529 ammonia Inorganic materials 0.000 description 1
- WQZGKKKJIJFFOK-VFUOTHLCSA-N beta-D-glucose Chemical compound OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-VFUOTHLCSA-N 0.000 description 1
- 230000008033 biological extinction Effects 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000001739 density measurement Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000004836 empirical method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 235000015203 fruit juice Nutrition 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 239000008103 glucose Substances 0.000 description 1
- 239000003292 glue Substances 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 239000005337 ground glass Substances 0.000 description 1
- 230000036433 growing body Effects 0.000 description 1
- 238000010949 in-process test method Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 238000012994 industrial processing Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 235000011090 malic acid Nutrition 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 229910052751 metal Inorganic materials 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 239000011022 opal Substances 0.000 description 1
- 239000002420 orchard Substances 0.000 description 1
- 150000007524 organic acids Chemical class 0.000 description 1
- 235000005985 organic acids Nutrition 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 239000004033 plastic Substances 0.000 description 1
- 235000013606 potato chips Nutrition 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000005070 ripening Effects 0.000 description 1
- 238000009738 saturating Methods 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000012883 sequential measurement Methods 0.000 description 1
- 229910052710 silicon Inorganic materials 0.000 description 1
- 239000010703 silicon Substances 0.000 description 1
- 229920002379 silicone rubber Polymers 0.000 description 1
- 239000004945 silicone rubber Substances 0.000 description 1
- 239000002002 slurry Substances 0.000 description 1
- 238000012306 spectroscopic technique Methods 0.000 description 1
- 238000004611 spectroscopical analysis Methods 0.000 description 1
- 238000009987 spinning Methods 0.000 description 1
- 239000008107 starch Substances 0.000 description 1
- 235000019698 starch Nutrition 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 239000005720 sucrose Substances 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012956 testing procedure Methods 0.000 description 1
- 229920001169 thermoplastic Polymers 0.000 description 1
- 239000004416 thermosoftening plastic Substances 0.000 description 1
- 238000004448 titration Methods 0.000 description 1
- 238000004454 trace mineral analysis Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000002235 transmission spectroscopy Methods 0.000 description 1
- 238000002460 vibrational spectroscopy Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/02—Details
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/02—Details
- G01J3/0205—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows
- G01J3/0218—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows using optical fibers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/02—Details
- G01J3/0205—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows
- G01J3/0224—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows using polarising or depolarising elements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J3/30—Measuring the intensity of spectral lines directly on the spectrum itself
- G01J3/36—Investigating two or more bands of a spectrum by separate detectors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J3/42—Absorption spectrometry; Double beam spectrometry; Flicker spectrometry; Reflection spectrometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/46—Measurement of colour; Colour measuring devices, e.g. colorimeters
- G01J3/50—Measurement of colour; Colour measuring devices, e.g. colorimeters using electric radiation detectors
- G01J3/51—Measurement of colour; Colour measuring devices, e.g. colorimeters using electric radiation detectors using colour filters
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/46—Measurement of colour; Colour measuring devices, e.g. colorimeters
- G01J3/52—Measurement of colour; Colour measuring devices, e.g. colorimeters using colour charts
- G01J3/524—Calibration of colorimeters
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/02—Food
- G01N33/025—Fruits or vegetables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/46—Measurement of colour; Colour measuring devices, e.g. colorimeters
- G01J3/50—Measurement of colour; Colour measuring devices, e.g. colorimeters using electric radiation detectors
- G01J3/501—Colorimeters using spectrally-selective light sources, e.g. LEDs
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/46—Measurement of colour; Colour measuring devices, e.g. colorimeters
- G01J3/50—Measurement of colour; Colour measuring devices, e.g. colorimeters using electric radiation detectors
- G01J3/51—Measurement of colour; Colour measuring devices, e.g. colorimeters using electric radiation detectors using colour filters
- G01J3/513—Measurement of colour; Colour measuring devices, e.g. colorimeters using electric radiation detectors using colour filters having fixed filter-detector pairs
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
- G01N2021/3155—Measuring in two spectral ranges, e.g. UV and visible
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
Definitions
- the present disclosure relates generally to the use of the combined visible and near infra red spectrum in an apparatus and method for measuring physical parameters, e.g., firmness, density and internal and external disorders, and chemical parameters, e.g., molecules containing O-H, N-H and C-H chemical bonds, in fruit and correlating the resulting measurements with fruit quality and maturity characteristics, including Brix, acidity, density, pH, firmness, color and internal and external defects to forecast consumer preferences including taste preferences and appearance, as well as harvest, storage and shipping variables.
- physical parameters e.g., firmness, density and internal and external disorders
- chemical parameters e.g., molecules containing O-H, N-H and C-H chemical bonds
- NIR near-infrared
- NIR is a form of vibrational spectroscopy that is particularly sensitive to the presence of molecules containing C-H (carbon-hydrogen), O-H (oxygen-hydrogen), and N-H (nitrogen-hydrogen) groups. Therefore, constituents such as sugars and starch (C-H), moisture, alcohols and acids (O-H), and protein (N-H) can be quantified in liquids, solids and slurries. In addition, the analysis of gases (e.g., water vapor, ammonia) is possible. NIR is not a trace analysis technique and it is generally used for measuring components that are present at concentrations greater than 0.1%. Short-Wavelength NIR vs.
- NIR Long-Wavelength NIR
- SW-NIR short wavelength-NIR
- the SW-NIR region offers numerous advantages for on-line and in- situ bulk constituent analysis. This portion of the NIR is accessible to low-cost, high performance silicon detectors and fiber optics.
- high intensity laser diodes and low-cost light emitting diodes are becoming increasingly available at a variety of NIR wavelength outputs.
- the relatively low extinction (light absorption) coefficients in the SW-NIR region yields linear absorbance with analyte concentration and permits long, convenient pathlengths to be used.
- Diffuse Reflectance Sampling vs. Transmission Sampling: Traditional NIR analysis has used diffuse reflectance sampling. This mode of sampling is convenient for samples that are highly light scattering or samples for which there is no physical ability to employ transmission spectroscopy. Diffusely reflected light is light that has entered a sample, undergone multiple scattering events, and emerged from the surface in random directions. A portion of light that enters the sample is also absorbed.
- transmission sampling is typically used for the analysis of clear solutions, it also can be used for interrogating solid samples.
- a transmission measurement is usually performed with the detector directly opposite the light source (i.e., at 180 degrees) and with the sample in the center. Alternately the detector can be placed closer to the light source (at angles less than 180 degrees), which is often necessary to provide a more easily detected level of light.
- NIR Calibration begins by acquiring a spectrum of each of the samples.
- Constituent values for all of the analytes of interest are then obtained using the best reference method available with regards to accuracy and precision. It is important to note that a quantitative spectral method developed using statistical correlation techniques can perform no better than the reference method.
- computer models employing statistical calibration techniques are developed that relate the NIR spectra to the measured constituent values or properties. These calibration models can be expanded and must be periodically updated and verified using conventional testing procedures. Factors affecting calibration include fruit type and variety, seasonal and geographical differences, and whether the fruit is fresh or has been in cold or other storage.
- Calibration variables include the particular properties or analytes to be measured and the concentration or level of the properties.
- Intercorrelations should be minimized in calibration samples so as not to lead to false interpretation of a models predictive ability. Co-linearity occurs when the concentrations of two components are correlated, e.g., an inverse correlation exists when one component is high, the other is always low or vice versa.
- NTR NIR analysis of tree fruit. NTR has been used for the measurement of fruit juice, flesh, and whole fruit. In juice, the individual sugars (sucrose, fructose, glucose) and total acidity can be quantified with high correlation (>0.95) and acceptable error. Individual sugars can not be readily measured in whole fruit.
- Brix is the most successfully measured NIR parameter in whole fruit and can generally be achieved with an error of ⁇ 0.5-1.0 Brix. More tentative recent research results indicate firmness and acidity measurement in whole fruit also may be possible.
- Japan has the large-scale deployment of on-line NIR for fruit sorting occurred. These instruments require manual placement/orientation of the fruit prior to measurement and early versions were limited to a measurement rate of three samples per second.
- the Japanese NIR instruments are also limited to a single lane of fruit and appear to be difficult to adapt to multi-lane sorting equipment used in the United States of America. While earlier Japanese NIR instruments employed reflectance sampling, more recent instruments use transmission sampling. In Koashi et al, U.S. Pat. No.
- Iwamoto et al., U.S. Pat. No. 5,324,945 also use NIR radiation to predict sugar content of mandarin oranges. Iwamoto utilizes a transmission measurement arrangement whereby the light traverses through the entire sample of fruit and is detected at 180 degrees relative to the light input angle.
- Moderately thick-skinned fruit were used to demonstrate the method, which relies on a fruit diameter correction by normalizing (dividing) the spectra at 844 nm, where, according to the disclosed data, correlation with the sugar content is lowest. NTR wavelengths in the range of 914-919 nm were found to have the highest correlation with sugar content. Second, third and fourth wavelengths that were added to the multiple regression analysis equation used to correlate the NIR spectra with sugar content were 769-770 nm, 745 nm, and 785-786 nm. In U.S. Pat. No. 5,708,271, Ito et al.
- wavelengths of NIR radiation used are greater than 800 nm and 860 nm, respectively.
- the wavelengths of NIR radiation with the highest correlation to sugar content of mandarins were 914 nm or 919 nm, when the fruit were
- 5,324,945 lists 914 nm or 919 nm as the primary analytical wavelength correlated with whole fruit sugar content; multiple linear regression was used to add successive wavelengths to the model as follows: 769-770 nm (2nd wavelength added), 745 nm (3rd wavelength added), and 785-786 nm (4th wavelength added).
- addition of the fourth wavelength to the model only reduced the standard error of prediction (SEP) by 0.1-0.2 Brix, which is approaching or less than the error limits of the refractometer used to determine the reference ("true”) Brix values.
- SEP standard error of prediction
- the apparatus and process disclosed herein is of the nondestructive determination or prediction of O-H, N-H and C-H containing molecules that are indicators of sample qualities, including fruit such as apples, cherries, oranges, grapes, potatoes, cereals, and other such samples, using near-infrared spectroscopy.
- Prior art has
- ⁇ utilized spectrum from 745nm and above This disclosure is of 1) the utilization of the spectrum from 250 nm to 1150 nm for measurement or prediction of one or more parameters, e.g., Brix, firmness, acidity, density, pH, color and external and internal defects and disorders including, for example, surface and subsurface bruises, scarring, sun scald, punctures, watercore, internal browning, in samples including fruit; 2) an apparatus and method of illuminating the interior of a sample and detecting emitted light from samples exposed to the above spectrum in at least one spectrum range and, in the preferred embodiment, in at least two spectrum ranges of 250 to 499nm and 500nm to 1150nm; 3) the use of the chlorophyl absorption band, peaking at 680nm, in combination with the spectrum from 700nm and above to predict one or more of the above parameters; 4) the use of the visible pigment region, including xanthophyll, from approximately 250nm to 499nm and anthocyanin from approximately 500 to
- Prior art has only examined spectrum from fruit for the prediction of Brix.
- This disclosure is of the examination of a greater spectrum using the combined visible and near infrared wavelength regions for the prediction of the above stated characteristics.
- the apparatus and method disclosed eliminates the problem of saturation of light spectrum detectors within particular spectrum regions while gaining data within other regions in the examination, in particular, of fruit.
- spectrometers with CCD (charge coupled device) array or PDA (photodiode array) detectors will detect light within the 250 to 1150nm region, but when detecting spectrum out of fruit will saturate in regions, e.g., 700 to 925nm, or the signal to noise (S/N) ratio will be unsatisfactory and not useful for quantitation in other regions, e.g., 250 to 699nm and greater than 925nm, thus precluding the gaining of additional information regarding the parameters above stated.
- CCD charge coupled device
- PDA photodiode array
- an apparatus and method permitting 1) the automated measurement of multiple spectra with a single pass or single measurement activity by detecting more than one spectrum range during a single pass or single measurement activity, 2) combining the more than one spectrum range detected, 3) comparing the combined spectrum with a stored calibration algorithm to 4) predicting the parameters above stated.
- This approach provides dual or plural spectra with good signal to noise ratio for all wavelengths intensities using a single light source intensity and the same exposure time on all spectrometer detectors.
- This approach uses at least one filtered light detector using filtered input 82 to the spectrometer 170 rather than different exposure times.
- a filter can be any material that absorbs light with equal strength over the range of wavelengths used by the spectrometer including but not limited to neutral density filters, Spectralon, Teflon, opal coated glass, screen.
- the dual intensity approach using two different lamp voltages proves problematic because the high and low intensity spectra are not easily combined together due to slope differences in the spectra.
- the dual exposure approach yields excellent combined spectra, which are necessary for firmness and other characteristic prediction and also improves Brix prediction accuracy. Measurements are disclosed, with the apparatus and process of this disclosure, which are made simultaneously in multiple sample types, e.g., where samples are apples, measurement is independent of a particular apple cultivar, using a single calibration equation with errors of ⁇ 1-2 lb. and ⁇ 0.5-1.0 Brix.
- This disclosure pertains to laboratory, portable and on-line NIR analyzers for the simultaneous measurement of multiple quality parameters of samples including fruit.
- a variety of calibration models may be used, from universal to highly specific, e.g., the calibration can be specific to a variety, different geographical location, stored v.
- NIR technology will play as a tool for grading sample qualities including fruit quality.
- the unique ability of NIR statistical calibration techniques to extract non-chemical "properties” provides a technique for development of a general NIR "quality index” for tree fruit.
- This general "quality index” combines all of the information that could be extracted from the NIR spectra and includes information about Brix, acidity, firmness, density, pH, color and external and internal disorders and defects.
- the near-infrared wavelength region below 745 nm has not been explored by prior investigations. Generally, the prior art design and or apparatus utilized was such that longer wavelength regions provided adequate data.
- the prior art for measuring sugar content in liquids and whole fruits using near-infrared spectroscopy utilizes longer wavelengths of radiation.
- NIR density measurement can be used to remove poor quality fruit in a sorting/packing line or at the supermarket. Information about color pigments and chlorophyll, related to maturity and quality, are obtained from 250 to approximately 699 nm. From approximately 700-1150 nm, the short wavelength NIR
- C-H, N-H, O-H information is obtained.
- Combining the visible and NIR region gives more analytical power to predict chemical, physical and consumer properties, particularly for fruit. All of these parameters can be determined simultaneously from a combined visible/NIR spectrum. Multiple parameters can be combined to arrive at a "Quality Index" that is a better measure of maturity or quality than a single parameter.
- Absorption of light by whole fruit in the approximately 250-699 nm region is dominated by pigments, including chlorophyll (a green pigment) which absorbs in the approximately 600-699 nm region.
- Chlorophyll is composed of a number of chlorophyll- protein complexes.
- Chlorophyll and pigments are important for determining firmness.
- NTR wavelengths of 700-925 nm and longer have been readily accessible to common near-infrared spectrometers, shorter wavelengths have not typically been explored for the following reasons: 1) lead-salt and other detector types, e.g., InGaAs, were not sensitive to shorter wavelengths; 2) light diffraction gratings were blazed at longer wavelengths yielding poor efficiency at short wavelengths; 3) light sources did not have enough energy output at shorter wavelengths to overcome the strong light absorption and scattering of biological (plant and animal) material in the visible region (250-699 nm).
- lead-salt and other detector types e.g., InGaAs
- VIS/NIR visible/near-infrared
- sugar content also known as Brix or soluble solids, which is inversely related to dry matter content
- firmness acidity, density, pH, color and internal and external defects and disorders.
- the apparatus and method is successful in measuring one or more such characteristic in apples, grapes, oranges, potatoes and cherries.
- Demonstrated in this disclosure is the ability to combine chemical and physical property data permitting the prediction of consumer properties, such as taste, appearance and color; harvest variables, such as time for harvest; and storage variables such as prediction of firmness retention and time until spoilage.
- FIG. 1 is a top plan of an embodiment of an apparatus for measuring and correlating characteristics of fruit with combined visible and near infrared spectrum showing an embodiment of the disclosure illustrating a sample holder having a securing or spring biasing article urging a holding article, shown here essentially as hemispherical, in contact with a sample having a sample surface, and preventing the sample from movement, a sample shown as an apple, a light detector having a light detector securing or spring biasing article placing or holding the light detector in contact with the sample surface, and light sources proximal the sample surface with the light sources positioned between 0 and 90 degrees, e.g., typically 45 degrees, in relation to the light sensor.
- a sample holder having a securing or spring biasing article urging a holding article, shown here essentially as hemispherical, in contact with a sample having a sample surface, and preventing the sample from movement
- a sample shown as an apple a light detector having a light detector securing or spring biasing
- the light source and light detector are positioned generally orthogonal to the sample surface.
- the light sources may be, for example, tungsten/halogen lamps.
- An optional filter or filters functioning as heat block, bandpass and or cutoff filters may be positioned between the light source and the sample or between the sample and a spectrometer(s).
- the light sources may, for example but without limitation, be 5W lamp sources from a spectrometer or one or more external light sources controlled by the CPU with power up to 1000 Watts each, but more typically 50 Watt, 75 Watt or 150 Watt.
- the output from the light sensor, shown here as a fiber-optic sensor becomes the input to a light detector such as a CCD array within a spectrometer.
- Fig. 1 A is a side elevation section of Fig 1.
- Fig. IB is a side elevation section of Fig 1 with no sample additionally showing a light source securing article.
- Fig. 1C is a flow diagram demonstrating the method of this invention. The flow diagram is schematically representative of all embodiments of this disclosure.
- Fig. ID is a flow diagram demonstrating the method and apparatus illustrating the light source(s) which illuminate a sample, light collection channels l...n (light detector l...n) of the spectra from a sample delivered as input to a spectra measuring device, shown here as spectrometer l...n.
- Spectrometer l...n channels output l...n are converted from analog to digital and become, for each channel, input to a CPU.
- the CPU is computer program controlled with each step, following the CPU in this flow diagram representative of a computer program controlled activity.
- the CPU output is also for each channel l....n where the steps of 1) calculating of absorbance spectra occurs for each channel l...n, 2) combining absorbance spectra into a single spectrum encompassing the entire wavelength range detected from the sample by spectrometers 1...n, 3) mathematical preprocessing, e.g., smoothing or box car smooth or calculate derivatives, 4) comparing the preprocessed combined spectra with the stored calibration spectrum for each characteristic, l...x, for which the sample is examined, 5) sorting decisions are made based on the results of step 4) or with 6) further combinations and comparisons of the results of quantification of each characteristic, l...x, for which the sample is examined.
- Absorbance is calculated as follows: once the dark spectrum, reference spectrum and sample spectrum are collected, they are processed to compute the absorbance spectrum, which Beer's law indicates is proportional to concentration.
- the dark spectrum which may include background/ambient light, is subtracted from both the sample spectrum and the reference spectrum.
- the log base 10 of the reference spectrum divided by the sample spectrum is then calculated. This is the absorbance spectrum.
- dark and reference can be collected periodically, i.e., they do not necessarily need to be collected along with every sample spectrum.
- a stored dark and reference can be used if light source and detector are stable and don't drift.
- Pre-processing uses techniques known to those practiced in the art such as binning, smoothing, wavelength ratioing, taking derivatives, spectral normalizing, wavelength subtracting, etc.
- Fig. IE is a flow diagram demonstrating the method and apparatus illustrating the light source(s) as a broad band source, such as a tungsten halogen lamp, which illuminates a sample; at least one, but in the preferred embodiment a plurality, of discrete wavelength filtered (bandpass) photo detectors provide spectrum detection for light collection channels l...n (photo detector l...n) of the spectra from a sample. The management of the detected spectra is as described for Fig. ID. Fig.
- IF is a flow diagram demonstrating the method and apparatus illustrating the light source(s) provided by discrete wavelength light emitting diodes(LEDs) which may be sequentially fired or lighted to illuminate a sample; at least one broadband photo detector and, in an alternative embodiment a least one broadband photo detector for each LED, provide spectrum detection for light collection channels l...n (photo detector l...n) of the spectra from a sample.
- the management of the detected spectra is as described for Fig. ID.
- Alternative light sources for this embodiment include but are not limited to tunable diode lasers, laser diodes and the use of a filter wheel between the light source and the sample or between the sample and photodetector.
- FIG. 2 is a top plan depicting at least one light source, with a single light source shown in this illustration, with optional filter and with at least one light detector, with a plurality of light detectors illustrated, proximal to the sample surface.
- This depiction demonstrates an orientation of light detectors relative to the direction of light cast on the sample surface with one light detector oriented at approximately 45 degrees to the direction of the light cast by the light source and a second light detector oriented at approximately 180 degrees from the direction of the light cast by the light source.
- the light detectors are in the same plane as the light from the light source.
- the light detector outputs are illustrated as providing inputs to spectrometers.
- the outputs may be combined to provide a single input to a single spectrum measuring and detecting instrument or may separately form inputs to separate spectrometers.
- light shutters may be used and alternately activated to provide light input from each measuring location separately in series, thus producing two spectra from different depths or locations of a sample.
- Fig. 2 A is a section elevation view of Fig 2 with the sample removed.
- Fig. 2B is a top plan depicting a single light source, with optional f ⁇ lter(s) and with multiple light detectors proximal and directed to illuminate the sample surface demonstrating an orientation of light detectors with both light detectors oriented at approximately 45 degrees to the direction of the light cast by the light source.
- Fig. 2C is an elevation view of Fig 2B.
- Fig. 2D is a section from Fig. 2C depicting a shielding method or apparatus, e.g., in the form of a bellows or other shielding article shielding the light detector from ambient light and directing the light detector to detect light spectrum output from the sample.
- Fig. 2E is a detail of a shielding device between the light detector of Fig. 2 and a sample. Shown in this illustration is a shield in the form of a bellows. Other shielding apparatus and methods will provide like shielding structure.
- Fig. 1 is an elevation view of Fig 2B.
- Fig. 2D is a section from Fig. 2C depicting a shielding method or apparatus, e.g., in the form of a bellows or other shielding article shielding the light detector from ambient light and directing the light detector to detect light spectrum output from the sample.
- Fig. 2E is a detail of a shielding device between the light
- FIG. 3 is a top plan depicting an alternative embodiment of a light source and light detector configuration where the light source is communicated by fiber optics from an illumination source, e.g., a lamp such as the lamp at a spectrometer; light detection is provided by light sensors, e.g., fiber optics or other means of transmission, positioned in varying relationships to the light source.
- Fig. 3 A is a section from Fig. 3 showing an embodiment where light sources 120 or lamps 123 are transmitted from a light source 120 or lamp 123 by light source fibers which are concentric to at least one detection fiber or light detector 80.
- the light source and light detector may be as described for Fig. 1.
- Alternative light source may be provided by at least one light source, depicted here as a plurality of light sources, which may be sequentially fired light emitting diodes emitting discrete wavelengths; where LEDs are employed, the light sensor or light detector may be a broadband photodiode detector central to concentrically positioned LEDs. While Fig. 3A illustrates light sources or lamps (and alternatively LEDs) concentrically positioned around a broadband light detector (and alternatively a broadband photodiode detector 255, it will be recognized that such light sources of this embodiment, as well as the light sources 120/LEDs 257 of other embodiments, can be placed in other arrangements. These two and other configurations also apply in the use of filtered photodetectors 255 and broadband lamp 123 design. Fig.
- FIG. 3B is a section from Fig. 3 showing an embodiment where light detectors or light detection fibers surround a least one light source or light source fibers.
- the light source and light detector may be as described for Fig. 1.
- Alternative light source and light detection may be provided.
- the centrally positioned light source may be a lamp or light transmitted from a spectrometer; the light detection may be by fiber optics transmission with discrete bandwidth filters between the fiber optics fiber and the sample limiting the transmission by any single or group of fibers.
- light source delivery and detection may be by a bifurcated reflectance probe; a reflectance probe may provide one or more light delivery sources and one or more light detectors providing inputs to one or more spectrometer.
- FIG. 4 is a top plan depicting an alternative embodiment of a light source and light detector configuration where at least one, and as depicted in this illustration two, light sources are communicated by fiber optics from an illumination source, e.g., a lamp such as the lamp at a spectrometer or an external lamp under computer control; light detection is provided by light sensors, e.g., fiber optics or other means of transmission, positioned in varying relationships to the light source detecting the output from the sample and providing an input to a spectrometer.
- Fig. 5 is a top plan depicting an alternative embodiment of the disclosure in a hand held case showing a light source and light detector configured in a sampling head.
- At the sampling head at least one light source, which may be a tungsten halogen lamp, is positioned in relation to discrete-wavelength filtered photodetectors.
- a method or article is required to shield the photodetectors from the light source and from ambient light which is illustrated as an ambient shield provided, for example, by pliable or compressible foam, bellows and by other such materials or structures.
- the sampling head is arranged so that the photodetectors are concentrically arrayed in relation to the light source.
- the light source may be communicated by fiber optics from an illumination source, e.g., a lamp within the case or by placement of a lamp within the sampling head, e.g., the broadband output lamp, e.g., tungsten halogen, is physically located centrally to concentrically arrayed photodetectors.
- the light source may be present to be in contact with the sample surface or proximal to the sample surface. Electrical communication is effected between the light source and photodetectors and a computer processor.
- the photodetectors fulfilling a spectrometer or spectral measurement function, provide the input which will be processed with microprocessor stored calibration algorithms to produce an output representing one or more parameters of the sample. The operation of this embodiment is seen in Fig.
- FIG. 5 A is a side elevation of Fig 5 depicting a sample positioned on the sampling head.
- Fig. 5B is an illustration of the embodiment of Fig. 5 where the sampling head 260 is in the form of a clamp 263 having at least two clamp jaws 266 which receive and secure within at least one jaw 266 structure at least one lamp 123 and in at least one clamp jaw 266 structure at least one light detector 80 such that the jaws 266, when the clamp 263 is closed, receive a sample 30 positioned to have the at least one lamp 123 and the at least one light detector 80 proximal the sample surface 35.
- the light detector 80 is depicted as a fiber optic fiber transmitting spectrum from the sample to an array of filtered 130 photodetectors 255 or a spectrometer 170.
- the output 82 will be managed as shown in Fig. ID or IE.
- Fig. 5C is a section from Fig. 5B of the array of filtered 130 photodetectors 255.
- the spectrum from the sample detected by fiber optic fiber 80 which is contained and positioned to transmit the detected spectrum from the sample so that the fiber is central to a concentrically arrayed filtered 130 photodetectors 255.
- a positioning structure 79 secures and positions the light detector 80 relative to the filtered 130 photodetectors 255.
- Fig. 5D is an illustration of the embodiment of Fig.
- the sampling head 260 is in the form of a clamp 263 having at least two clamp jaws 266 which receive and secure within at least one jaw 266 structure at least one lamp 123 and in at least one clamp jaw 266 structure at least one arc photodetector array 90 such that the jaws 266, when the clamp 263 is closed, receive a sample 30 positioned to have the at least one lamp 123 and the at least one arc photodetector array 90 proximal the sample surface 35.
- the arc photodetector array 90 is depicted as an array of filtered 130 photodetectors 255 which will preferably be equidistant from the lamp 123 when a sample 30 is received.
- the output 82 will be managed as shown in Fig. ID or IE. Fig.
- FIG. 5E is a section of the photodetector 255 array of Fig. 5D.
- Fig. 6 is a top plan depicting an additional embodiment of the disclosure in a hand held case showing a light source and light detector configuration in the form of a sampling head.
- at the sampling head at least one light source is positioned in relation at least one photodetector.
- a method or article is required to shield the light source and light detector or photodetectors from ambient light is illustrated as an ambient shield provided, for example, by pliable or compressible foam, bellows , as indicated by the structure of Fig. 2D and 2E and by other articles equally recognized as providing such shielding structure.
- the sampling head is arranged so that the at least one light detector or photodetector is central to concentrically arrayed discrete wavelength light emitting diodes.
- the light emitting diodes fulfill the function of light source and are sequentially fired or lighted with the spectrum output detected by the at least one light detector or photodetector.
- Fig. IF wherein all components are encased within the case 250.
- Fig. 6 A is a section elevation of Fig 6 depicting the sampling head showing the ambient shield, light emitting diodes and photodetector or light detector fixed by affixing articles within the sampling head. The output from the light detector is depicted as well as is the case.
- FIG. 6B is an elevation representative of an additional embodiment of the disclosure of this invention and of the embodiment of Fig. 6 where a sampling head is affixed in a case, light detectors are affixed by affixing articles within the sampling head.
- the sampling head receives a sample which is positioned to be illuminated by a light source lamp.
- This embodiment depicts the case as having a cover which serves as an ambient shield.
- the structure of the sampling head may be of a compressible or pliable foam or a bellows which may provide the structure allowing an ambient shield.
- a light source input is depicted for example from a spectrometer.
- Outputs from the photodetectors are depicted which may be inputs to a spectrum measuring instrument such as a spectrometer with a detector.
- Fig. 6C is a plan view of the embodiment of Fig. 6B illustrating a plurality of light detectors, illustrated here as fiber optic light detectors. Shown in this illustration are two light detectors with one proximal the light source and another distal from the light source with the purpose being to provide two different pathlengths, shallow and deep, by taking the difference between the far or deep spectrum and the near or shallow spectrum data of greater accuracy can be obtained. This difference method provides a pathlength correction to improve concentration or property or sample characteristic predictions.
- Fig. 6D is a section detail view from Fig. 6B illustrating the light source, lamp, light source securing article, case, sampling head, light detectors positioned proximal and distal from the light source, light source input and light detector outputs.
- Fig. 6C is a plan view of the embodiment of Fig. 6B illustrating a plurality of light detectors, illustrated here as fiber optic light detectors. Shown in this illustration are two light detectors with one proximal the light source and another distal
- FIG. 6E is an elevation view of an embodiment of the disclosure of Fig. 6 wherein the sampling head structure provided the ambient shield structure.
- Fig. 6F is a section detail from Fig. 6E showing light detectors affixed within the sampling head ambient shield positioned proximal and distal from the light source, a lamp with lamp input, light detector outputs and a case.
- Fig. 7 is a side elevation showing another embodiment in a packing/sorting line form of the disclosure illustrating a light source and light detector affixed and positioned by bracket articles, light detector fixture and light source securing articles which will be recognized as structure from which at least one light source and at least one light detector will be suspended, rigidly secured and otherwise positioned including the use of such as rods, bars and other such bracket fixture articles.
- the at least one light source is positioned to illuminate a sample, depicted in this drawing as an apple.
- the at least one light detector is positioned by bracket articles and light detector fixture to detect the light spectrum output from the sample. Samples, in this illustration are conveyed by a sample conveyor. Total exposure to the at least one light source and at least one light detector will be limited by the nature of the sample being interrogated and of the embodiment, i.e., sampling time may be limited in a packing/sorting line application for apples, to 5ms or less. However, it will be recognized that other sampling times and strategies will be within the realm of use for the invention disclosed herein.
- the at least one light detector monitoring the sample depicted is directed to detect light at approximately 30 degrees relative to the direction of the light cast from the at least one light source, although various other placements of light detector(s) relative to light source(s) can also be utilized.
- the light source and light detector are positioned proximal the sample.
- the light source lamp may be powered from a spectrometer or externally controlled by the CPU.
- the light detector may be a single fiber optic fiber with the light spectrum detected forming the input to a spectrum detection instrument such as a spectrometer. The processing of the light spectrum detected is as described and set out in Fig. 1C and ID Fig.
- FIG. 7A is a section elevation of Fig 7 depicting the light source, and sample conveyance system, bracket fixture, light source securing article, lamp input and spectrometer as a sample moves into illumination from the light source and toward the light detector.
- Fig. 7B is a section elevation of Fig 7 depicting the light detector, and sample conveyance system, bracket fixture, light detector fixture, light detector output, spectrometer, and detector as a sample moves toward and under the light detector.
- Fig. 7C is an elevation depicting at least one light detector 80 and as shown a plurality of light detectors 80 representative of measurements of a plurality of spectrum regions.
- a filtered 130 light detector 80 is representative of the detection of spectrum of 700 to 925nm
- another light detector 80 is representative of detection of red pigments and chlorophyll in the 500 to 699nm range and the 926 to 1150 nm range
- another light detector 80 is representative of detection of the yellow pigment region in the range of 250 to 499 nm.
- Two additional light detectors 80 are shown positioned opposite a light source 120 lamp 123 such that the sample will pass between the lamp 123 and light detector 80 and is representative of an input to reference spectrometers 170 separately operating in the 250- 499 nm range and 500-1150 nm range. Where the sample is an apple it will be expected that the reference channels additionally will not detect spectrum out of the sample and will indicate the presence or absence of a sample.
- Shielding may be utilized between the light source and the light detectors and or sample, e.g., options include but are not limited to 1) a light shield as a curtain may extend from a bracket fixture between the light source and light detectors reducing the direct exposure of the light detectors to the light source, 2) the light shield may extend between the light source and light detectors and sample wherein an aperture will be formed in the light shield between the light source and sample limiting surface reflection from the sample to the light detectors and 3) the light shield may provide filter function, e.g., heat blocking, cutoff and bandpass, between the light source and sample limiting the possibility of heat or burn damage to the sample.
- filter function e.g., heat blocking, cutoff and bandpass
- FIG. 7D is a section from Fig. 7C showing the lamp 123 oriented to illuminate the sample from the side. As illustrated, the sample as an apple is illuminated from the stem side.
- Fig. 7E is a section from Fig. 7C showing one of the light detectors 80.
- Fig. 8 is a side elevation showing an additional embodiment of the apparatus disclosed in Fig. 7 wherein at least one light shield is positioned by a bracket fixture article to separate the at least one light source from the at least one light detector as a sample is conveyed by a sample conveyor under and past a light source toward and under a light detector.
- the light shield may be a curtain and is depicted in Fig. 8 as a curtain composed of two portions, each suspended from a bracket fixture.
- Fig. 8 A is a section elevation of Fig 8 depicting the light shield and at least one curtain, light source, and sample conveyance system as a sample moves into contact with and under the light shield.
- Fig. 8B is a section elevation of Fig 8 depicting the light shield, at least one curtain, light detector and sample conveyance system as a sample moves into contact with and under the light shield.
- Fig. 1C, ID, IE and IF are flow diagrams demonstrating the method of this invention.
- the flow diagram Fig. 1C is representative of all embodiments of this disclosure.
- the flow diagram Fig. 1C is representative of all embodiments of this disclosure.
- the flow diagram Fig. 1C is representative of all embodiments of this disclosure.
- Fig. ID illustrates one or more light sources 120 and multiple channels from light detector 50 through final prediction of sample characteristic.
- Fig. ID demonstrates the method and apparatus of this disclosure illustrating the light source(s) 120, which may be lamps 123 or other light sources, which illuminate a sample 30 interior 36, light collection channels l...n, composed for example of fiber optic fibers 80 or photodetectors 255, e.g., light detector l...n, of the spectra from a sample 30 delivered as input 82 to a spectra measuring device, shown here as spectrometer(s) l...n. 170.
- a light source 120 with lamp 123 is external to the spectrometer and is controlled by a CPU 172 which triggers power 125 to the light source 120 lamp 123.
- Spectrometer l...n 170 channels output l...n are converted from analog to digital by A/D converters l...n 171 and become, for each channel, input to a CPU 172.
- the CPU 172 is computer program controlled with each step, following the CPU 172 in this flow diagram is representative of a computer program controlled activity.
- a CPU 172 output is provided for each channel l....n where the steps of 1) calculation of absorbance spectra 173 occurs for each channel l...n, 2) combine absorbance spectra 174 into a single spectrum encompassing the entire wavelength range detected from the sample by spectrometers l...n 170, 3) mathematical preprocessing or preprocess 175, e.g., smoothing or box car smooth or calculate derivatives, precedes 4) the prediction or predict 176, for each channel, comparing the preprocessed combined spectra 175 with the stored calibration spectrum or calibration algorithm(s) 177 for each characteristic l...x 178, e.g., Brix, firmness, acidity, density, pH, color and external and internal defects and disorders, for which the sample is examined, followed by 5) decisions or further combinations and comparisons of the results of quantification of each characteristic, 1...X, e.g., determination of internal and or external defects of disorders 179, 180; determination of color 181; determination of indexes such as eating quality index 182, appearance quality index
- Sorting or other decisions 184 may for example be input process controllers to control packing/sorting lines or may determine the time to harvest, time to remove from cold storage, and time to ship.
- the apparatuses depicted in Fig. 1 through 8 do not all illustrate the entire flow diagram sequence from illumination of sample 30 through determination of the predicted result as is depicted in Fig. IC, ID, IE and IF.
- Fig. IC, ID, IE and IF For signal processing illustrations, reference is made to the indicated drawings. Fig.
- IE is a flow diagram demonstrating the method and apparatus illustrating the light source(s) 120 as a broad band source, such as a tungsten halogen lamp, which illuminates a sample 30; at least one, but in an embodiment a plurality, of discrete wavelength filtered (bandpass) photodetectors 255 having filters 130 provide spectrum detection for light collection channels 1...n (photodetector 1...n) of the spectra from a sample 30.
- a light source 120 with lamp 123 is controlled by a CPU 172 which triggers power 125 to the light source 120 lamp 123.
- the spectrum detected from the sample surface 35 may be communicated by fiber optic fibers as light detectors 80 to the photodetectors 255.
- An alternative to this embodiment may use an AOTF, (acousto-optic tunable filter) to replace the at least one or a plurality of photodetectors 255 as the spectrum detection device.
- AOTF acousto-optic tunable filter
- IF is a flow diagram demonstrating the method and apparatus illustrating the light source(s) provided by at least one, but in an embodiment a plurality of discrete wavelength light emitting diodes 257, which may be sequentially fired or lighted by a CPU trigger for power 125 to illuminate a sample 30; at least one broadband photodetector 255 and, in an alternative embodiment a least one broadband photodetector 255 for each LED 257, provide spectrum detection for light collection channels 1...n (photodetector 1...n) of the spectra from a sample. The management of the detected spectra is as described for Fig. ID.
- Fig. 1, 1 A and IB depict an embodiment of a Nondestructive Fruit Maturity and Quality Tester 1 for measuring and correlating characteristics of fruit with combined Visible and Near Infra-Red Spectrum showing an embodiment of the disclosure illustrating a sample holder 5 having a securing or spring biasing article 9 urging a holding article 12 against and in contact with a sample 30.
- the holding article depicted in Fig. 1 is illustrated as essentially a hemisphere sized to receive a sample 30.
- the sample has a sample surface 35.
- At least one light source 120 will be employed proximal the sample surface 35.
- the light source 120 is comprised of at least one lamp 123, optional filters 130.
- two light sources 120 each directed essentially orthogonally to the sample surface 35 and illuminating the sample 30 approximately 60 TO 90 degrees relative to each other.
- a light detector 80 is depicted as directed to detect light from the sample surface 35 at approximately 30 TO 45 degrees relative to the direction of the light cast from either light source 120.
- the light detector 80 is illustrated as positioned by a light detector fixture 50 having a light detector securing or spring biasing article 60 placing, holding and or urging a light detector 80 into contact with the sample surface 35.
- Monitoring of the light source 120 is depicted by light detectors 80 depicted as directed toward the lamp 123 output; the output 82 of these reference light detectors 80 is detected by a reference spectrometer 170; an alternative to the use of two spectrometers 170 will be the sequential measurement of reference light detectors 80 and the light detector 80 directed to the sample surface 35.
- All light detector 80 are fixed by light detector fixtures 50 by light detector securing or spring biasing articles 60 to a plate 7 or other containing device such as a case.
- the securing article 9 urging the holding article 12 against the sample 30 also urges the sample against the light detector 80.
- the securing article 9 and holding article 12 in combination with the light detector 80 and light detector securing article 60 secure and prevent the sample 30 from movement.
- the sample 30 is shown, in Fig. 1, as an apple.
- the light sources 120 may be, for example, tungsten/halogen lamps.
- An optional filter 130 or filters 130 functioning as heat block, bandpass and or cutoff filters, separately or in combination, may be positioned between the lamp 123 and the sample 30 or between the sample 30 and the light detector 80.
- the light sources 120 may be lamps 123, provided for example by external 50Watt, 75 Watt, or 150 Watt lamp sources controlled by a CPU 172.
- Power 125 can be provided by power supply from a spectrometer 170 or from an alternate power supply.
- Both the light source(s ) and the spectrometer(s) are controlled by a CPU 172 and their operation can be precisely controlled and optimally synchronized using digital input/output (I/O) trigger.
- the light detector 80 shown here as a fiber-optic sensor, provides a light detector output 82 which becomes the input to a spectrometer 170, or other spectrum measuring or processing instrument, which is detected by a detector 200, e.g., at least one light detection device or article, such as a CCD array which may be a CCD array within a spectrometer 170.
- sample holder 5, light detector fixture 50 and light detector securing article 60 and light sources 120 with light source securing article 122 are affixed to a plate 7, for experimental purposes but will be otherwise enclosed and or affixed in a container, case, cabinet or other or other fixture for commercial purposes, e.g., applications include and are not limited to sample measurements on high speed sorting and packing lines, harvesters, trucks, conveyor-belts and experimental and laboratory.
- Fig. 2, 2 A, 2B, 2C, 2D and 2E depicts an alternative embodiment of the Nondestructive Fruit Maturity and Quality Tester 1 depicting a single light source 120, with lamp 123 and optional filter 130 and with multiple light detectors 80 in contact with the sample surface 35.
- This depiction of the relative positioning of the light detectors 80 with the sample 30 or sample surface 35 is directed to the shielding of the light detector 80 from ambient light and is intended to demonstrate either direct contact between the light detector 80 and the sample surface 35 or shielded a shield 84 composed, for example, by bellows, a foam structure or other pliable or compressible article or apparatus providing a sealing structure or shield method of insuring that the light detector 80 is shielded from ambient light and light from the light source 120 and receives light spectrum input solely from the sample 30.
- the positioning of the light source 120 relative to the light detectors 80 illustrate a positioning of one light detector 80 at angle theta of approximately 45 degrees to the direction of the light as directed by the light source 120 to illuminate the sample 30.
- the second light detector 80 is at angle gamma of approximately 180 degrees to the direction of the light as directed by the light source 120.
- the positioning of the light detector 80 at approximately 180 degrees to the direction of the light as directed by the light source 120 may be a position utilized for the detection of internal disorders within the sample, e.g., internal disorders within Georgia Jonagold apples, such as water core, core rot, internal browning/breakdown, carbon dioxide damage, and, in some cases, insect damage/infestation.
- the light detectors 80 in this illustration are suggestive of the many light detector 80 positions possible with the positioning dependent on the sample and the characteristic or characteristics to be measured or predicted. In this illustration the light detectors 80 are positioned to detect within the same plane as the light directed from the light source 120.
- the orientation of 180 degrees between light source 120 and light detector 80 will be preferred for smaller samples. Larger samples 30 will attenuate light transmission thus requiring the location of the light detector 80 proximal the light source 120 to insure exposure to light spectrum output 82 characteristic of the sample 30.
- the orientation of the light source 120 and light detectors 80 is sensitive to fruit size, fruit skin and fruit pulp or flesh properties. The orientation where the sample 30 is an apple will likely preclude a 180 degree orientation because of limitations in proximity and intensity of the light source 120 as being likely to damage or burn the apple skin. However, orange skins are less sensitive and may withstand, without commercial degradation, a light source 120 of high intensity and closely positioned to the orange surface.
- the signal output or light detector output 82 is dependent on the orientation of the light source 120 relative to the sample 30 and sample surface 35 and the light detector 80.
- Fig. 2B and 2C depict an alternative orientation of light detectors 80 where the light detectors 80 are oriented at angle theta of approximately 45 degrees to the direction of the light as directed by the light source 120. This illustration demonstrates two light detectors 80 positioned approximately 90 degrees apart and positioned to detect light from approximately the same plane.
- Fig. 1 depicts the orientation of the light source 120 relative to the sample 30 and sample surface 35 and the light detector 80.
- Fig. 2B and 2C depict an alternative orientation of light detectors 80 where the light detectors 80 are oriented at angle theta of approximately 45 degrees to the direction of the light as directed by the light source 120.
- This illustration demonstrates two light detectors 80 positioned approximately 90 degrees apart and positioned to detect light from approximately the same plane.
- the positioning of the light source or light sources and light detector or detectors will depend on the measurement intended.
- FIG. 2D and 2E depict a shielding method or apparatus, e.g., in the form of a bellows or other shield 84 article shielding the light detector from ambient light and enabling the light detector to solely detect light spectrum output from the sample.
- the shield 84 structure may be formed of a flexible or pliant rubber, foam or plastic which will conform to the surface irregularities of the sample and will provide a sealing function between the shielding material and sample surface which will eliminate introduction of ambient light into contact with the light detector.
- the shield 84 is depicted in the form of a bellows in Fig. 2D and 2E.
- Fig. 1, 2 - 4, 6, 7 and 8 depict light sources which may be provided by spectrometers 170 (as in the case of Fig.
- tungsten halogen lamps or the equivalent are used which generally produce a spectrum within the range of 250- 1150 nm when the filament temperature is operated at 2500 to 3500 degrees kelvin.
- the light source for the invention disclosed herein may be a broadband lamp, which for example, but without limitation, may be a tungsten halogen lamp or the equivalent, which may produce a spectrum within the range of 250-1150 nm; other broadband spectrum lamps may be employed depending upon the sample 30, characteristics to be predicted, and embodiment utilized
- the light detector 80 output 82 in these embodiments will generally be received by a spectrometer 170 having a detector 200 such as a CCD array.
- Fig. 3, 3 A and 3B depict an alternative embodiment of a Nondestructive Fruit Maturity and Quality Tester-Combined Unit 15 of a combined unit 126 having a combined source/detector 135.
- the source of light and method of light detection in this embodiment may be a light source 120, lamp 123 and light detector 80 configuration where the light source 123 lamp 123 is communicated by fiber optics from an illumination source, e.g., a lamp such as the lamp at a spectrometer 170; light detection is provided by light detectors 80, e.g., fiber optics or other manner of light transmission, positioned in varying relationships to the lamp 123 as shown in Fig. 3A and 3B.
- Fig. 3A is a section from Fig.
- a combined source/detector 135 has an alternative source of light and light detection;
- the source of light depicted as a plurality of sources, may be sequentially fired light emitting diodes 257 emitting discrete wavelengths;
- the light detection may be a broadband photodiode detector 255 central to concentrically positioned LEDs.
- the combined unit 126 and sample holder 5 are mounted to a plate 7 or other mounting or containing fixture, case, cabinet or other device suitable for commercial or experimental purposes, for example with a bracket or other mounting article, so as to be fixed or as to have a spring or other biasing function to urge the combined unit 126 and sample holder 5 against the sample.
- a light shield 84 as depicted in Fig.
- Fig. 3B is a section from Fig. 3 showing an additional embodiment of a combined unit 126 where a centrally positioned light source 120 lamp 123, for example light via fiber optics from a tungsten halogen lamp, is concentric to at least one and, as depicted here a plurality, of discrete wavelength photodetectors.
- the output of the at least one detection fibers or light detectors 80 is the input to a spectrometer 170 or other spectral measuring instrument such as a photodetector 255. Depicted is a spectrometer 170 having a detector 200.
- light source delivery and detection for the embodiment of Fig.
- a reflectance probe may provide one or more light delivery sources and one or more light detectors providing inputs to one or more spectrometer.
- Fig. 3A illustrates LEDs 257 concentrically positioned around a broadband photodiode detector 255
- the LEDs of this embodiment, as well as the light sources 120 of other embodiments can be placed in other arrangements, e.g., the photodiode detector 255, as well as the detectors 80 of other embodiments, can be 180 degrees opposite a circle of LEDs 257 and the sample 30 placed between the LEDs 257 and the photodiode detector 255, e.g., for cherries or grapes; alternatively, the LEDs 257 can be placed on an arc, equidistant and 180 degrees opposite from the photodetector 255 in relationship to the sample 30.
- Nondestructive Fruit Maturity and Quality Tester 1 showing at least one light source 120 and lamp 123 and light detector 50 configuration where at least one, and as depicted in this illustration two, light source 120 and lamps 123 are communicated by fiber optics to or proximal the sample surface 35, from an illumination source, e.g., a lamp 123 or other external light source.
- Light detection is provided by light detectors 80, e.g., fiber optics or other method of light transmission. In this embodiment the light sources 120 and light detector 80 are in contact with the sample surface 35.
- the light detector 80 detects the light spectrum output from the sample 30 and providing light detector input 82 to a spectrum measuring or processing instrument or method including, for example, a spectrometer 170 having a detector 200.
- a spectrum measuring or processing instrument or method including, for example, a spectrometer 170 having a detector 200.
- the light detector 80 will be inserted into the sample 30 thus effecting a shielding of the light detector 80 from ambient light, e.g., on harvester-mounted applications or in a processing plant where the product will be processed such as sugar beets or grapes.
- the light shield 84 depicted in Fig. 2D and 2E is applicable to the interrelationship of the sample 30 and sample surface 35 with the light detector 80 and light source 120 and lamp 123. Illustrated in Fig.
- Fig. 5 is a top plan depicting an alternative embodiment of the Nondestructive Fruit Maturity and Quality Tester 1 in a hand held case 250 showing a light source 120 and at least one light detector 80, shown here as six light detectors 80, configuration in the form of a sampling head 260.
- At the sampling head 260 at least one light source 120 lamp 123 is positioned in relation to light detectors 80 provided by at least one discrete-wavelength photodetector 255.
- Shown in Fig. 5 are a plurality of discrete- wavelength photodetectors 255, filling the combined function of light detector 80, and spectrum detecting instrument such as a CCD array detector 200.
- the operation of this embodiment is seen in Fig. IE wherein all components are encased within the case 250.
- Electronic and computer communication between the sampling head 260 and the computer control circuitry is via electronic signal cabling 265 or wireless including infrared or other such transmission method or apparatus.
- the sampling head 260 ambient shield 262 will provide a shielding method or apparatus, e.g., fulfilling the same or similar structural function as the shield 84 in Fig. 2D and 2E, in shielding the at least one photodetector 255 and lamp 123 from ambient light.
- the sampling head 260 and ambient shield 262, depicted in Fig. 5 and 5 A may be formed from a pliable polyfoam within which the at least one lamp 123 and at least one photodetector 255 may be secured by a fixture article.
- the material or structure forming the sampling head 260 and ambient shield 262 may be flexible or pliable foam, in the form of a bellows or other shielding article similar to that depicted in Fig. 2D and 2E.
- a pliable polyfoam to form the ambient shield 262 will serve to seal out or preclude exposure, by a sealing action between a sample surface 35 and the ambient shield 262, of the at least one photodetector 255 and lamp 123 from ambient light.
- Other shielding apparatus and methods will provide adequate shielding structure including bellows, a case or box enclosing the sampling head 260 and sample 30 or other such article providing shielding structure between ambient light and the interface between the sampling head 260, the at least one photodetector 255 and lamp 123 and the sample 30 and sample surface 35.
- the operation of this embodiment is seen in Fig. IE wherein all components are encased within the case 250.
- FIG. 5 and 5 A illustrate the sampling head 260 arranged so that at least one, and as illustrated in Fig. 5, a plurality of discrete-wavelength filtered 130 photodetectors 255 are concentrically arrayed in relation to the centrally positioned at least one light source 120.
- the light source 120 lamp 123 which may be communicated by fiber optics from an illumination source, e.g., a lamp within the case 250 or may, for particular samples 30, e.g., oranges, be present to be in contact with or closely proximal the sample surface 35. Electrical communication and light communication is effected between the light source 120 and photodetectors 255 and a spectrometer 170 by fiber optics and or wiring, printed circuit paths, cables.
- the photodetectors 255 fulfill a spectrometer or spectral measurement function, provides the input 82 which will be processed with microprocessor stored calibration algorithm to produce an output representing one or more parameters of the sample.
- Fig. 5 A is a side elevation of Fig 5 depicting a sample positioned on the sampling head.
- 5B, 5C, 5D and 5E illustrate embodiment of the invention directed particularly to small samples 30, e.g., grapes and cherries
- the sampling head 260 is in the form of a clamp 263 having at least two clamp jaws 266 which receive and secure within at least one jaw 266 structure at least one lamp 123 having a light source input 125 and in at least one clamp jaw 266 structure at least one light detector 80 such that the jaws 266, when the clamp 263 is closed, receive a sample 30 positioned to have the at least one lamp 123 and the at least one light detector 80 proximal the sample surface 35.
- the light detector 80 is depicted as a fiber optic fiber transmitting spectrum from the sample to an array of filtered 130 photodetectors 255 or a spectrometer 170.
- Fig. 5B depicts a light detector 80 as a fiber transmitting spectrum from a sample 30 to be displayed on a filtered 130 photodetector array 255 where the fiber 80 is contained and positioned to transmit the detected spectrum from the sample 30 so that the fiber 80 is central to a concentrically arrayed filtered 130 photodetectors 255.
- a positioning structure 79 which may be tubes interconnected to position the fiber light detector 80 central to the photodetector array 255, secures and positions the light detector 80 relative to the filtered 130 photodetectors 255.
- a collimating lens 78 will be positioned between the light detector 80 fiber and the array 255 to insure that light from the light detector 80 is normal to the filtered 130 photodetector array 255.
- Fig. 5F depicts an arc photodetector array 90 received and secured within at least one jaw 266 structure where the photodetectors 255 within the photodetector array 90 are preferably equidistant from the light source 120 or lamp 123.
- Fig. 6 through 6F illustrate an additional embodiment of the Nondestructive Fruit Maturity and Quality Tester 1. Fig.
- FIG. 6 is a top plan depicting an additional embodiment of the disclosure in a hand held case 250 form showing a light source 120 in the form of LEDs 257 and light detector 80, in the form of a photodetector 255, configuration in the form of a sampling head 260.
- the photodetector 255 is used without filters, i.e., wavelength bandpass filters, and is sensitive from -250-1150 nm.
- Alternative devices or methods for providing light source and light detection includes, but is not limited to diodelasers and other light sources producing a discrete wavelength spectrum.
- at the sampling head 260 at least one LED 257, and as illustrated in Fig.
- a plurality of LEDs 257 is positioned in relation at least one photodetector 255.
- a method or article is required to shield the LEDs 257 and photodetector/photodiode detector 255 from ambient light which is illustrated as an ambient shield 262 including structures of compressible and pliable foam, bellows as indicated by the shield 84 structure of Fig. 2D and 2E and other such materials, structures or articles.
- the sampling head 260 is arranged so that the at least one photodetector/photodiode detector 255 is central to concentrically arrayed discrete wavelength LEDs 257.
- the light emitting diodes 257 fulfill the function of light source and are sequentially fired or lighted with the spectrum output detected by the at least one photodetector/photodiode detector 255.
- the photodetector 255 output 82 is processed as demonstrated in Fig. IF.
- the photodetector 255 is responsive to a broad range of wavelengths, both visible and near-infrared (i.e., -250-1150 nm). When each LED 257 is fired, the light enters the sample 30, interacts with the sample 30, and re-emerges to be detected by the photodetector 255.
- the photodetector 255 produces a current proportional to the intensity of light detected.
- the current is converted to a voltage, which is then digitized using an analog-to-digital converter.
- the digital signal is then stored by an embedded microcontroller/microprocessor.
- the microcontroller/microprocessor used in the preferred embodiment is an Intel 8051. However, other microprocessors and other devices and circuits will perform the needed tasks.
- the signal detected by the photodetector 255 as each LED 257 is fired is digitized, A/D converted and stored. After each LED 257 has been fired and the converted signal stored, the microprocessor stored readings are combined to create a spectrum consisting of as many data points as there are LEDs 257. This spectrum is then used by the embedded microprocessor in combination with a previously stored calibration algorithm to predict the sample properties of interest. Signal processing then proceeds as shown in Fig.
- Fig. 6A is a section elevation of Fig 6 depicting the sampling head 260 showing the ambient shield 262, composed for example of compressible foam or bellows or other such structure, e.g., a rubber plunger, originally designed for a vacuum pick-up tool which looks much like a toilet plunger, but has a more gentle curve and is available in a variety of sizes including 1mm diameter and larger; in certain of these embodiments a 20 mm rubber plunger was used with a pickup fiber optic operating as the "handle" that couples to the plunger. The sample then makes a seal with the plunger prior to measurement.
- Other devices or methods will also provide the requisite sealing structure, as described in this specification.
- Fig. 6B, 6C and 6D are representative of an additional embodiment of the disclosure of this invention where a sampling head 260 is affixed in a case 250, light detectors 80 are affixed by affixing articles within the sampling head 260.
- the sampling head 260 receives a sample 30 which is positioned to be illuminated by a light source 120 lamp 123.
- This embodiment depicts the case 250 as having a cover which serves as an ambient shield 262.
- the structure of the sampling head 260 may be of a compressible or pliable foam or a bellows which may provide the structure allowing an ambient shield 262.
- Ambient light can also be measured after the sample 30 is in place, but before the light source 120 lamp 123 is turned on. This ambient light signal is then stored and subtracted accordingly for subsequent measurements.
- a light source input power 125 is depicted for example from a spectrometer 170 or may be from a CPU 172 trigger or other external lamp source and/or power supply.
- Outputs 82 from the light detector/photodiode detectors 80 are depicted and processed as shown in Fig. IF. Fig.
- FIG. 7, 7 A and 7B are representative of an embodiment of the disclosure wherein the lamp 123 is positioned within the sampling head 260. Alternatively, the lamp 123 may be positioned by an affixing article within the ambient shield 262.
- FIG. 7, 7 A and 7B Another embodiment in a packing/sorting line form of the disclosure is depicted in Fig. 7, 7 A and 7B illustrating a light source 120 and light detector 80 affixed and positioned by bracket articles 275, light detector fixture 50 and light source securing articles 122 which will be recognized as mounting structure from which at least one light source 120 and at least one light detector 80 will be suspended, rigidly secured and otherwise positioned including the use of such as rods, bars and other such bracket article 275 fixtures .
- the at least one light source 120 is positioned to illuminate a sample 30, depicted in this drawing as an apple.
- the at least one light detector 80 is positioned by bracket articles 275 and light detector fixture 50 to detect the light spectrum output from the illuminated sample 30. Samples 30, in this illustration are conveyed by a sample conveyor 295. Total exposure to the at least one light source 120 and at least one light detector 80 will be determined by the intensity of the light source used and the nature of the sample being interrogated. For apples, exposure times of 5-10 msec or less are commonly used to provide multiple measurements per apple at line speeds up to 20 fruit/second.
- the positioning of the light detector(s) 80 and of the light sources(es) 120 relative to each other and relative to the sample is dependent on the characteristics of the sample and of the qualities sought to be measured.
- the light source 120 may be positioned to be directed essentially orthogonal to the sample surface 30 in a plane oriented 90 degrees from the plane to which the light detector 80 is directed.
- the light source 120 and light detector 80 are positioned proximal the sample 30.
- the light source 120 lamp 123 may be powered from a spectrometer 170 or other external source, as noted in the discussion of Fig. 1.
- the light detector 80 may be a single fiber optic fiber with the light spectrum detected forming the output 82 to a spectrum detection instrument such as a spectrometer 170 and detector 200.
- the processing of the light spectrum detected is as described and set out in Fig. IC.
- Another embodiment directed to sorting/packing lines is seen in Fig. 7C, 7D and 7E depicting at least one light detector 80 and as shown a plurality of light detectors 80 representative of measurements of a plurality of spectrum regions.
- a filtered 130 light detector 80 is representative of the detection of spectrum of 700 to 925nm
- another light detector 80 is representative of detection of red pigments and chlorophyl in the 500 to 699 nm range and water, alcohols and physical quality (e.g., firmness, density) information available in the 926 to 1150 nm range
- another light detector 80 is representative of detection of the yellow pigment region in the range of 250 to 499 nm.
- Two additional light detectors 80 are shown positioned opposite a light source 120 lamp 123 such that the sample will pass between the lamp 123 and light detector 80 and is representative of an input to two reference spectrometers 170, one monitoring the 250-499 nm wavelength region and the other monitoring the 500-1150 nm region..
- the reference channel additionally will not detect spectrum out of the sample and will indicated the presence or absence of a sample.
- the output of the reference channel(s) can be used as an object locator to determine which spectra from the sample light detector(s) to retain for use in prediction.
- Shielding may be utilized between the light source 120 lamp 123 and the light detectors 80 and or sample 30, e.g., options include but are not limited to 1) a light shield 284 as a curtain 285 may extend from a bracket fixture 275 between the light source 120 lamp 123 and light detectors 80 reducing the direct exposure of the light detectors 80 to the light source 120 lamp 123, 2) the light shield 285 may extend between the light source 120 lamp 123 and light detectors 80 and sample 30 wherein an aperture will be formed in the light shield 284 between the light source 120 lamp 123 and sample 30 limiting surface reflection from the sample surface 35 to the light detectors 80 and 3) the light shield 284 may provide filter 130 function, e.g., heat blocking, cutoff and bandpass, between the light source 120 lamp 123 and sample surface 35 limiting the possibility of heat or burn damage to the sample 30.
- filters 130 function e.g., heat blocking, cutoff and bandpass
- FIG. 8, 8A and 8B An additional embodiment is seen in Fig. 8, 8A and 8B wherein at least one light shield 284 is positioned by a bracket article 275 to separate the at least one light source 120 and lamp 123 from the at least one light detector 80 as a sample 30 is conveyed by a sample conveyor 295 under and past a light source 120 and lamp 123 toward and under a light detector 80.
- the light shield 284 may be a curtain 285 and is depicted in Fig. 8 as a curtain 285 composed of at least one portions and as shown in Fig. 8A of two portions or a plurality of portions, each suspended from a bracket article 275. Where there are a plurality of curtain 285 portions, the respective curtain 285 portions will overlap and separate as the sample 30 passes.
- the sample 30, for example an apple is conveyed by a packing/sorting conveyance system 295.
- a cycle will be repeated as each sample 30 moves toward, into contact with, under and past the light shield 284.
- the packing/sorting conveyance system 295 will have samples 30 sequentially positioned on the conveyance system 295 such that the space between sample 30 is minimal generally in relation to the size of the sample 30.
- the light shield 284 the sample 30 will be illuminated by the light source 120 while the light detector 80 will detect only ambient light and will be shielded from the light source 120.
- the light source 120 may, for example, be a tungsten/halogen lamp or light transmitted by optics to illuminate the sample 30.
- the light detector 80 for example a optic fiber detector, is positioned such that the sample surface 35 will be proximal to the light detector 80 as the sample 30 contacts and passes under the light shield 284.
- the light shield 284 may be composed of a flexible or pliable sheet opaque to the spectra to which the light detector 80 is sensitive and may be comprised, for example, of silicone rubber, Mylar, thermoplastics and other materials.
- the light detector 80, light shield 284 and light source 120 will be mechanically affixed by bracket articles 275 or other mounting apparatus or methods readily recognized by those of ordinary skill in the art or measurement at packing/sorting systems.
- An alternative configuration of the embodiments of Fig. 7 and 8 will employ a .
- plurality of light sources 120 including, for example a light source 120 illuminating the sample 30 from the top with a second light source 120 illuminating the sample 30 from the side or two light sources 120 illuminating the sample 30 from opposite sides illustrating the multiple positions which may be employed for light sources 120.
- a plurality of light detectors 80 will view the same or different sample surface 35 locations with each light detector 80 output 82 either sensed by a separate spectrometer or combined to form a single output 82. Where a plurality of outputs 82 are received by a plurality of spectrometers 170 at least one spectrometer 170 will have a neutral density filter installed to block some percentage, e.g.
- the blocking of light to one spectrometer 170 effects the same result as using a shorter exposure time.
- the dual intensity approach proves problematic because the high and low intensity spectra are not easily pasted or combined together due to slope differences in the spectra, however the dual intensity approach may be preferred for predicting certain parameters (e.g., firmness, density ) with certain sample types (e.g. stored fruit or oranges). While the dual exposure approach yields excellent combined spectra, both approaches provide useable combined spectra, which are necessary for firmness and other parameter prediction and also improved Brix accuracy.
- Partial Least Squares (PLS) regression analysis is used during calibration to generate a regression vector that relates the VIS and NIR spectra to brix, firmness, acidity, density, pH, color and external and internal defects and disorders.
- This stored regression vector is referred to as a prediction or calibration algorithm.
- Spectral pre- processing routines are performed on the data prior to regression analysis to improve signal-to-noise (S/N), remove spectral effects that are unrelated to the parameter of interest, e.g., baseline offsets and slope changes, and "normalize" the data by attempting to mathematically correct for pathlength and scattering errors.
- a pre-processing routine typically includes "binning”, e.g., averaging 5-10 detector channels to improve S/N, boxcar or gaussian smoothing (to improve S/N) and computation of a derivative.
- the 2nd derivative is most often used, however, the 1st derivative can also be used and the use of the 4th derivative is also a possibility.
- firmness prediction data is often used after binning, smoothing and a baseline correction or normalization; where no derivative is used.
- a 2nd-derivative transformation often is best.
- PC A Principal Components Analysis
- under-ripe and ripe fruit can be separated and spoiled, e.g., higher pH, or rotten fruit can be identified for segregation.
- the NIR spectra of whole apples, and other fruit, in the approximately 250-1150 nm region also show correlation with pH and total acidity.
- the 250-699 nm wavelength region contains color information, e.g., xanthophylls, yellow pigments, absorb in the 250-499 nm region; anthocyanin, which is a red pigment, has an absorption band spanning the 500-550 nm region, improves classification or predictive performance, particularly for firmness.
- An example is the prediction of how red a cherry is by measuring and applying or comparing the anthocyanin absorption at or near 520 nm to the pertinent predictive or classification algorithm.
- Under-ripe oranges, having a green color can be predicted by measurement of sample spectrum output 82 in the chlorophyll absorption region (green pigments) at or near 680 nm and applying the measured output 82 spectrum to the pertinent predictive algorithm.
- the spectrum output from the sample, in the 950- 1150 nm region has additional information about water, alcohols and acids, and protein content. For example, sample water content relates to firmness in most fruit with water loss occurring during storage. High pH fruit, often indicative of spoilage, can also be uniquely identified in the presence of other apples using a classification algorithm.
- the present disclosure is a non-destructive method and apparatus for measuring the spectrum of scattered and absorbed light, particularly within the NTR range of 250-1150 nm, for the purpose of predicting, by use of the applicable predictive algorithm, particular fruit characteristics including sugar content, firmness, density, pH, total acidity, color and internal and external defects. These fruit characteristics are key parameters for determining maturity, e.g., when to pick, when to ship, when and how to store, and quality, e.g., sweetness/sourness ratio and firmness or crispness for many fruits and vegetables. These characteristics are also indicators of consumer taste preferences, expected shelf life, economic value and other characteristics.
- Internal disorders can also be detected, e.g., for Georgia Jonagold apples, including disorders such as water core, core rot, internal browning/breakdown, carbon dioxide damage, and, in some cases, insect damage/infestation.
- the disclosure simultaneously utilizes 1): the visible absorption region (about 250-699 nm) that contains information about pigments and chlorophyll, 2) the wavelength portion of the short-wavelength NIR that has the greatest penetration depth in biological tissue, especially the tissue of fruits and vegetables (700-925 nm), and 3) the region from 926-1150 nm, which contains information about moisture content and other O- H components such as alcohols and organic acids such as malic, citric, and tartaric acid.
- Benchtop, handheld, portable and automated packing/sorting embodiments are disclosed.
- the benchtop embodiment will generally be distinguished from the high speed packing/sorting embodiment through the greater ease of examining the sample 30 with more than one intensity light source 120 , i.e., lamps 123 or light sources 120 controlled with more than one voltage or power level or more than one exposure time.
- a benchtop embodiment discussed herein utilizes a dual intensity light source 120, e.g., by utilizing dual voltages or dual exposure times or other methods of varying the intensity of the light source 120 used to illuminate the sample 30.
- the light detector 80 may be operated to provide at least one exposure at one lamp 123 intensity and, for example, the light detector 80 may provide dual or a plurality of exposures at 1 lamp intensity.
- the method of providing dual or a plurality of exposures at one lamp intensity is accomplished as follows: the light detector 80 exposure time is adjustable through basic computer software control. In the computer program, two spectrum of different exposure times are collected for each sample 30.
- the benchtop method may, as preferred by the operator, involve direct physical contact between the sample surface 35 and the apparatus delivering the light source 120, e.g., at least one light detector 80 may penetrate the sample surface 35 into the sample interior.
- a high speed packing/sorting embodiment generally will be limited in the delivery or the exposure of the light source 120, relative to or at the sample surface 35, resulting from the limited time, usually a few milliseconds, the sample 30 will be in range of the light source 120.
- Multiple passes or arrangements of multiple light sources 120 and multiple light detectors 80, including photodetectors 255 and other light detection devices, will permit, in the highspeed packing/sorting embodiment, the exposure of the sample to multiple light source 120 intensities.
- the handheld embodiment generally will allow sampling of a limited number of items by orchard operators, i.e., in inspection of fruit samples on the plant or tree, and from produce delivered for packing/sorting, to centralized grocery distribution centers or individual grocery stores. Obtaining data over the wavelength region of 250- 1150 nm is only possible using a multi intensity or multi exposure measurement, i.e., dual intensity or dual exposure as in the preferred embodiment.
- the number of different light source intensity or exposures required is dependent on the characteristics of the sample and of the detector 200.
- the spectrum acquired at longer detector 200 exposure times or higher light source intensity saturates the detector pixels, for some detectors, e.g., Sony ILX 511, or Toshiba 1201, from -700-925 nm, yet yields excellent S/N data from -500-699 nm and from -926-1150 nm.
- the low intensity or shorter exposure time spectrum is optimized to provide good S/N data from 700-925 nm.
- Accurate firmness predictions of fresh and stored fruit requires the 700-925 nm region and the 500-699 nm, e.g., pigment and chlorophyll, plus the 926-1150 nm region. Addition of the 250-499 nm region, e.g., yellow pigments known as xanthophylls which absorb light, will improve prediction of firmness and other parameters such as Brix, acidity, pH, color and internal and external defects. There is high correlation between the spectrum output from the sample 30 in the 926-1150 nm region with water content. Stored fruit appears to have higher relative water content than fresh fruit and less light scattering.
- the chlorophyll and pigment of a sample 30 is predicted by correlation with the sample spectrum output 82 in the 250-699 nm region, with this correlation likely being the most important for prediction of firmness of fresh fruit, while the longer wavelength water region may be more important for accurate firmness measurement of stored fruit.
- the 700-925 nm region also contains absorption bands from carbon-hydrogen, oxygen-hydrogen, and nitrogen-hydrogen bonds, e.g., (CH, OH, NH).
- the 926-1150 nm region is of greatest interest.
- pre-sprout condition in grain for example, can be predicted by examination of the sample output spectrum in the 500-699 nm region.
- the preferred embodiment of the apparatus is composed of at least one light source 120, a sample holder 5 including, for example a sorting/packing sample conveyor 295 and other devices and methods of positioning a sample 30, with at least one light detector 80, i.e. optical fiber light sensors in the preferred embodiment, detecting the sample spectrum output 82 to be received by a spectrum measuring instrument such as a spectrometer 170 with a detector 200, e.g., a CCD array, with the signal thus detected to be computer processed, by a CPU 172 having memory, and compared with a stored calibration algorithm, i.e., stored in CPU 172 memory, producing a prediction of one or more characteristics of the sample.
- a spectrum measuring instrument such as a spectrometer 170 with a detector 200, e.g., a CCD array
- the at least one light source 120 and at least one light detector 80 are positioned relative to the sample surface 35 to permit detection of scattered and absorbed spectrum issuing from the sample. Bracket fixtures 275, brackets and other recognized positioning and affixing devices and methods will be employed to position light sources 120, light detectors 80 and sample holders 5. In the preferred embodiment the positioning of the light source 120 and light sensor or light detector 80 will be such as to shield 84 the light detector 80 from direct exposure to the light source 120 and will limit the light detector 80 to detection or exposure of light transmitted from the light source 120 through the sample 30.
- the light source 120 may be fixed in a conical or other cup or shielding container which will allow direct exposure of the light source 120 to the sample surface while shielding the light source 120 from the light detector 80.
- the light detector 80 may be fixed in a shielding container, e.g., a shield 84 or ambient shield 262, thus shielding the light detector 80 from the light source 80 and exposing the light detector 80 solely to the light spectrum transmitted through the sample 30 from the light source 80 to the light detector 80.
- the spectrum detected by the light detectors 80 i.e., the signal output 82, is directed, as input, to at least one spectrometer 170 or other device sensitive to and having the capability of receiving and measuring light spectrum. In the preferred embodiment two or more spectrometers 170 are employed.
- One spectrometer 170 monitors the sample channel, i.e., the light detector 80 output 82, and another spectrometer 170 monitors the reference, i.e., light source 120 channel. If the lamp 123 is turned on and off between measurements, ambient light correction can be done for both light detector 80 and light source 120 channel, e.g., spectrum collected with no light is subtracted from spectrum collected when lights are on and stabilized. Alternatively, the light source 120 can be left on and ambient light can be physically eliminated using a shield 84 or ambient shield 262, such as a lid or cover or appropriate light-tight box.
- a shield 84 or ambient shield 262 such as a lid or cover or appropriate light-tight box.
- shielding of the light detector 80 composed of fiber optic fibers applies as well to photodetectors 255 and the utilization of light sources other than tungsten halogen lamps including for example light emitting diodes 257.
- Another alternative with multiple sampling points and thus multiple light detectors 80, as with fiber-optic sensors, is to converge all or some sampling points, as depicted in Fig. 4, back to a single sample or light detector 80 channel spectrometer 170, e.g., using a bifurcated, trifurcated or other multiple fiber-optic spectrometer 170 input.
- sample points i.e., light detectors 80
- multiple points e.g., on a conveyor belt full of product
- a single spectrometer 170 thus providing an "average" spectrum that is used to predict an average property such as Brix for all sample 30 or light detector 80 channels.
- two or more spectrometers 170, or at least two spectrometers 170 are used for reference and or measurement.
- a spectrometer 170 used in gathering data for this invention utilized gratings blazed at 750 nm to provide coverage from 500-1150 nm.
- spectrometers 170 operating in the 250-499 nm wavelength region can be included to provide expanded coverage of the visible region where xanthophylls, e.g., yellow pigments, absorb light.
- Information in the output 82 spectrum detected from 1000-1100 nm also contains repeated information, if a cutoff or long-pass filter is not used, from 500-550 nm, e.g., regarding Anthocyanin, which is a red pigment, has an absorption band spanning the 500-550 nm region, which improves classification or predictive performance, particularly for firmness.
- the spectrometers 170 used in the preferred embodiment have charge-coupled device (CCD) array detectors 200 with 2048 pixels or channels, but other array detectors 200, other light detectors 80, including other detector 200 sizes vis-a-vis array size or other method of detector size characterization, may be used as would be recognized by one of ordinary skill in the art.
- CCD charge-coupled device
- One of the two spectrometers 170 monitors the light source 120 intensity and wavelength output directly, providing a light source reference signal 81 that corrects for ambient light and lamp, detector, and electronics drift which are largely caused by temperature changes and lamp aging.
- the other spectrometer(s) 170 receives the light detector 80 signal output 82 from one or more light detectors 80 which are sensing light output from one or more samples 30 and/or one or more locations on a sample 30, e.g., at multiple points over a single sample 30, such as an apple, or at multiple points over a sample conveyor 295 belt of apples, grapes or cherries, or a different sample 30, e.g., a different lane on a packing/sorting line, can be measured with each additional spectrometer 170.
- Each light sensor e.g., light detector 80(photodetector 255 or other light sensing apparatus or method), in the preferred embodiment represents a separate sample 30 or different location on the same sample 30 or group of samples 30.
- Spectra from all spectrometers 170 are acquired, in the preferred embodiment, simultaneously.
- AID conversion can occur in parallel or series for each spectrometer (parallel preferred).
- the computer then processes the spectra and produces an output.
- Current single CPU computers process spectra in series.
- a dual CPU computer, two computers, or digital signal processing (DSP) hardware can perform spectral processing and provide output in parallel.
- DSP digital signal processing
- spectra from the wavelength region from about 250- 1150 nm is examined from samples 30, e.g., fruit including apples.
- a reflectance fiber-optic probe was used as the light detector 80.
- the spectrophotometer 170 used to collect the data i.e., sense the spectrum output 82 from the light detector 80, was a DSquared Development, LaGrande, Ore., Model DPA 20, one of ordinary skill in the art will recognize that other spectrometers and spectrophotometers 170 may be used.
- the spectrophotometer 170 referenced employed a five watt tungsten halogen light source 120, a fiber-optics light sensor to detect the spectrum or output 82 from the sample 30 and provide the light sensor signal input 82 to the spectrometer 170.
- Other lamps 123 or light sources 120 may be substituted as well as other light sensors or light detectors 80.
- the light detector signal input 82 to the spectrometer 170 is detected by a charge coupled device array detector 200.
- the output from the charge coupled device array detector is processed as described above. Firmness and Brix were measured using the standard destructive procedures of Magness-Taylor firmness ("punch test”) and refractometry, respectively.
- the NIR spectra is detected by an array detector 200 which permits recording or detection of 1024 data points.
- the 1024 data points are smoothed using a nine-point gaussian smooth, followed by a 2nd-derivative transformation using a "gap" size of nine points.
- Partial least squares (PLS) regression was used to relate the 2nd- derivative NIR spectra to Brix and firmness.
- PLS Partial least squares
- the validated model can then be used to nondestructively predict Brix and firmness in unknown whole fruit samples.
- This information guides harvest decisions indicating time to harvest, which fruit is suitable for cold storage, where the fruit is classified from acceptable to unacceptable characteristics of quality or consumer taste, which fruit to be removed from the sorting/packing operation as not meeting required characteristics, e.g., firmness, Brix, color and other characteristics.
- This disclosure of embodiments of an apparatus and method is directed to the simultaneous measurement and use of more than one spectral region from a sample.
- the use of the chlorophyll absorption region and the NIR region, including the highly absorbing 950-1150 O-H region is accomplished by exposing the sample, e.g.
- Fig. 1 illustrates filtered light sources 120 allowing exposure of the sample 30 to different light intensities.
- Fig. 2 illustrated the use of more than one light detector 80 where filtering between the sample 30 and light detector 80 allows detection of different spectral regions. Shown in Fig.
- the light source is a plurality of discrete wavelength LEDs 257
- the intensity of the light source 120 will be selected to provide light output to the light detector 80 which will give optimal S/N data in the desired spectral region.
- a light source e.g., a lower intensity light source
- the sample e.g. apple
- an acceptable S/N ratio in the 700-925nm region.
- the spectrum is dominated by noise due to the low light levels and is not useful.
- a higher intensity light source is selected to illuminate the sample, saturating the detector array at the 700-925nm regions while obtaining data with an acceptable S/N ratio, in the red pigment region of 500-600 nm, the chlorophyll region of 600-699nm and in the O-H region of 926-lOOOnm.
- the data from each of the two passes comprises separate data inputs delivered to an analog to digital converter for computer processing. Same spectrometer and A/D for benchtop unit, where the two spectra are acquired sequentially. For on-line, two spectrometers are used, each with its own A D. In one embodiment A D cards external to the computer are utilized which are serial and are provided by Ocean Optics.
- This process is provides for multiple channels into a data analyzer for analysis by software.
- Ocean Optics drivers hereafter referred to as drivers, accept MS “C” or Visual Basic to 1) determine the spectrum detected from the sample or 2) subject the data to the predictive algorithm and produce the output.
- Display control computer programs or software periodically requests drivers to deliver the spectrums to be combined.
- the digital combination then produces, with standard display software, the output display representing the entire spectrum ranges detected from the each sample.
- the spectrum sampling protocol may seek 50 spectrum samples during each of the multiple passes, e.g., 50 spectrum samples during the pass subjecting the fruit sample to the lower intensity light source and separately 50 spectrum samples during the pass subjecting the fruit sample to the higher intensity light source.
- the total duration of each pass will be determined by the speed of the sorting/packing line and may be limited to approximately 5ms per sample. However, it will be recognized, for all embodiments and sample types, that other sampling times and strategies will be within the realm of use for the invention disclosed herein as different samples and different embodiments are employed. Where the samples being processed, on a sorting/packing line, are apples, there is expected to be little space between each successive apple. Spectrum obtained from the space between apples and at the leading and trailing sides of the sample or apple will be discarded.
- the spectrum data detected will be that exiting the sample 30 representative of the portion of the sample 30 constituting the path between the point of exposure of the sample 30 with the light source 120 and the point of spectrum exit for detection by the light detector 80.
- this method can determine whether light detected by the light detector 80 is from an apple or the empty space between apples in a sorting/packing line sample conveyor 295. This method can also detect the leading and trailing edges of an apple as it passes by the light detector 80 having an output 82 to a spectrometer 170.
- discrimination can occur to select specific spectra samples which, for example, are expected to be from the midsection of the sample or apple.
- the cycle detected by the light detector 80 thus, for each sample 30 in the on the sample conveyor 295 of a sorting/packing line, is composed of an initial segment where the light detector 80 or pickup fiber is exposed to only ambient light with a light shield 284 between the light detector 80 and the light source 120.
- the leading edge or side of the apple will commence to be revealed permitting the light detector 80 to detect spectrum output 82 from the apple.
- the sample 30 under the light shield 284 exposes the light detector 80 to spectrum output 82 from the sample 30 until the sample 30 moves to the point where the trailing edge or side of the sample 30 is remaining exposed to the light source 120.
- the sample 30 then moves past the light shield 284 and all light from the light source 120 is blocked between the light detector 80 and the light source 120.
- the initial spectra detected by the light detector 80 will be at the leading edge or side of the sample 30 as it approaches the curtain 285.
- the intermediate spectrum measurements between the initial time at which the leading edge of the sample 30 is exposed to the light source 120 and the time when the trailing edge or side of the sample 30 is exposed to the light source 120, will include those where the light detector 80 or light pickup is optimally positioned to detect spectra most representative of the characteristics of the light spectra output 82 from the sample 30 as the light source 120 illuminates the sample 30, e.g., apple, other fruit or other O-H, C-H or N- H materials.
- the light detector 80 analog output 82 is converted to digital data by an A/D card. Computer program or software tests the data for acceptance or discarding.
- the criteria for acceptance of each spectrum sample 30 is a predetermined spectral feature determined by the expected spectral output 82 of the sample 30, e.g., where the sample 30 is an apple, i.e., the criteria will be to detect a spectrum from 250 to 1150nm falling within the spectra expected for an apple. The detection of the space between apples, in the sorting/packing line, will be recognized as not apples.
- This spectrum acquired for each sample 30 is the input to the predictive algorithms as indicated by the flow diagram of Fig. IC. Multiple spectrum, for example fifty spectrum, are detected by the light detector 80 for each sample.
- the computer program compares each detected discrete spectrum with an expected spectrum from the particular sample, the spectrum not meeting the criteria are discarded, the retained spectrum, e.g., 40 - 50 samples, are combined to provide the spectrum which becomes the input for the predictive algorithm.
- Multiple spectra from the same apple are averaged to provide a single average spectrum representing multiple points on the apple, the apple may be spinning as it travels by the sensor, e.g., clockwise or counter clockwise in relation to the direction of sorting line travel with better measurement indicated with counterclockwise motion of the sample, thus giving even greater coverage of its surface.
- the average absorbance spectrum for a sample is calculated, the spectrum is multiplied by the regression vector (via a vector multiplication dot product). The regression vector is obtained from previous calibration efforts and is stored on the computer.
- the results of the processing the spectrum output 82 by the predictive algorithms will determine the predicted characteristics of the sample 30.
- the characteristics determined for each discrete sample 30, e.g., apple or other fruit, will be used for decision making in handling or disposition of the sample 30 including, for example, 1) in the packing/sorting line different characteristics will be used for sorting and packing decisions, e.g., by color, size, firmness, taste as predicted by acidity and Brix and 2) characteristics indicating spoilage may trigger methods of elimination of the particular sample 30 from the packing/sorting line.
- Packing and sorting of apples will likely involve multiple packing/sorting illumination or light source 120 and light detector 80s for each line.
- the sample 30 is comprised of smaller fruit, e.g., cherries or grapes
- data is acquired, tested to determine if the data corresponds to preset criteria with data selected which meets preset criteria and discarded if it fails to meet preset criteria.
- Data received by light sensors is then combined to compose the total spectrum sampled.
- the total spectrum is then compared with the predictive algorithm and decisions are made regarding the sample 30 including, for example, sorting/packing decisions.
- the results of the comparison of the total spectrum with the predictive algorithm provides a number or other output for end use including information for computer directed sorting equipment.
- the lamp 123 in the preferred embodiment is a 12- Volt, 75-Watt tungsten halogen lamp.
- other light sources include but are not limited to light emitting diode, laser diode, tunable diode laser, flash lamp and other such sources which will provide equivalent light source and will be familiar to those practiced in the art.
- the lamp is held at a resting voltage of 2-Volts.
- the lamp When a measurement is taken, the lamp is ramped up to the desired voltage, a brief delay allows the lamp output 82 to stabilize, then spectra are acquired. After data acquisition, the lamp is ramped down to the resting voltage. This procedure extends lamp life and prevents burning the sample.
- the lamp In high speed operations the lamp may always be lighted, e.g., on a high-speed packing/sorting line or used on harvest equipment, and a light "chopper" or shutter or other equivalent article or method could be utilized to deliver light to the passing sample for a determined period of time.
- the operation of the light source is important in extending lamp life, reducing operating expense and reducing disruption of operations.
- the lamp 123 voltage is ramped up and down to preserve lamp 123 life and to lessen the likelihood of burning fruit.
- An ambient/room light background measurement is made to correct for the dark spectrum, which may include ambient light. It is stored and subtracted from the sample and reference (if applicable) so that there is no contribution of ambient light to the sample spectrum, which would affect accuracy.
- Dual intensity illumination is employed to: 1) improve data accuracy above 925 nm and below 700 nm and 2) to normalize path length changes due to scattering. Dual exposure time increases the likelihood of increased data quality with large and small fruit. Utilization of more than one light detector 80, with each positioned at different distances from the sample, will likewise increase the ability to obtain increased data quality throughout each portion of the spectrum from approximately 250nm to 1150 nm.
- PCA Principal Components Analysis
- FIG. 4 illustrates an alternative embodiment of the disclosure and includes at least one light source 120 transmitted by a transmitting article, for example a fiber optic fiber or other equivalent article for transmitting light; a sample 30 having an sample surface 35; input mechanism of positioning light from the at least one light source 120 proximal the sample surface; at least one illumination detector; output mechanism of positioning the at least one illumination detector proximal the sample surface; the at least one light source 120 and the at least one illumination detector may be positioned in relation to the surface or against the surface by a positioning article provided, for example, by a positioning article spring biased against the surface of the sample; the pressure against a sample surface, by an at least one light source 120 or an at least one illumination detector, will be limited by surface characteristics of the sample and/or the character of the measurement process, i.e., pressure may be reduced where a sample is subject to surface damage or where the measurement process is in at high speed limiting the time permitted for each separate sample contact.
- a transmitting article for example a fiber optic fiber or other equivalent article for transmitting light
- the illumination is transmitted to the surface, for example by fiber optics or other equivalent manner; and at least one device or method of measuring the illumination detected from the sample.
- the light source for the disclosure herein may be a broadband lamp, which for example, but without limitation, may be a tungsten halogen lamp or the equivalent, which may produce a spectrum within the range 250-1150 nm and have a filament temperature of of 2500 to 3500 degrees kelvin; other broadband spectrum lamps may be employed depending upon the sample 30, characteristics to be predicted, and embodiment utilized;
- the at least one device or method of measuring the illumination may be a spectrometer having at least one input; the at least one spectrometer may include, for example, a 1024 linear array detector with those of ordinary skill in the art recognizing that other such detectors will provide equivalent detection;
- the at least one illumination detector may be a light pickup fiber or other equivalent detector including for example a fiber optics light pickup; the at least one illumination detector collects a spectrum which is received by the at least one spectrometer input; the sample
- the light source 120 may comprises a plurality of illumination fibers.
- a plurality of illumination fibers may be arrayed such that each of the plurality of illumination fibers is equidistant from adjacent illumination fibers; the at least one illumination detector may, in this embodiment, be positioned centrally in the array of illumination fibers.
- the plurality of illumination fibers may, for example, be comprised of 32 illumination fibers and the light source 120 may be provided, for example, by a 5w tungsten halogen lamp or other equivalent light source or by a plurality of illumination sources provided for example by at least two light sources such as, for example, at least two 50 Watt light sources.
- Illumination sources may be composed, for example, of sources having a focusing ellipsoidal reflector with cooling fan.
- the at least one illumination detector may comprise a plurality of light detectors 80, which may for example, be arrayed such that each illumination detector is equidistant from adjoining light detectors 80; where at least two light sources are positioned are employed, they may for example be positioned 45 degrees relative to the illumination detectors, in the array of illumination fibers.
- a plurality of light detectors 80 may be comprised of twenty-two illumination detectors.
- An embodiment of the disclosure may be comprised of at least one light source 120 composed of a 5 w tungsten halogen lamp; the at least one illumination detector is a single detection fiber; the light source 120 is positioned against the sample 30 degrees distal to the detection fiber.
- an alternative embodiment may include a polarization filter between the light source 120 and the sample, provided, for example by a linear polarization filter or an equivalent as understood by one of ordinary skill in the art; a matching polarization filter is positioned between the at least one illumination detector and the sample, which may be provided, for example by a linear polarization filter rotated 90 degrees in relation to the polarization filter between the light source 120 and the sample.
- FIG. 9 is an elevation depicting an additional embodiment of the invention demonstrating at least one light detector 80 having an output 82 to a spectrometer 170 having a detector 200
- a colluminatmg lens 78 is intermediate the at least one detector 80 and a sample 30.
- the detector 80 positioned to detect light from the sample 30.
- An aperture 310 allows illumination of the sample 30 by the at light source 120 lamp 123.
- a least light shutter 300 intermediate the light source 120 lamp 123 and aperture 310. The light shutter 300 operable by shutter operating means.
- the shutter control means 305 receiving control signals from a CPU 172 having shutter operating control output 307
- a reference light shutter 301 intermediate the light source 120 lamp 123 and the reference light transmitting means 81.
- the reference light shutter 301 operable by shutter control means 305
- the reference light shutter 301 shutter control means 305 receiving control signals from a CPU 172 having a shutter operating control output 307.
- the reference light transmitting means 81 providing an input to the spectrometer 170.
- the CPU 172 providing lamp power output 125 to the light source 120 lamp 123.
- the spectrometer 170 receiving input from reference light transmitting means 81 having output 82 received as in input to the CPU 172.
- the spectrometer output 82 capable of A/D conversion to form input to the CPU 172.
- the spectrometer 170 receiving input from detector output 82 received as in input to the CPU 172.
- Encoder/pulse generator 330 input to CPU 172 providing sample conveyor 295 movement data.
- Fig. 10 illustrates using spectroscopic sensors for measuring fruits and vegetables while in motion on a sample conveyor 295. Shown is a sample 30 with proximity sensing means 340.
- Fig. 10A is a section from Fig. 10 illustrating the proximity sensing means 340 in the form of reflectance means.
- Fig. 11 illustrates the manner of taking a reference measurement of the light source 120 lamp(s) 123 where intensity vs. wavelength output can also be obtained using reflecting means 360.
- Reflecting means 360 may be inserted via an aperture 310, for example in a case 250, when a reference measurement is to be made as dictated by reflecting control means 308 as an output from a CPU 172.
- the CPU 172 via means, will detect the presence or absence of a sample 30 and, when a sample 30 is absent for "n" time increments or sample conveyor 295 movements will provide a reflecting control means 308 control signal to reflecting position means 306, e g., linear actuator or rotary solenoid operated by means, e g., mechanical driven by electrical, pneumatic, hydraulic or other power means.
- Fig. 12 and 13 illustrate the mechanical insertion of reference means 430 in or near the location where actual sample 30 is normally measured. Insertion is by insertion means including but not limited to an actuator system 400.
- FIG. 14 and 14A illustrate a means of reducing the width of apparatus structure by mounting light source 120 lamps 123 distal from a sample 30 with spectrum from the sample 30 directed by reflecting means 360 and lens 78 or reference light transmission means 320 with spectra received via apertures 310.
- Fig. 15 and 15A illustrates spectra detection from sample 30 other than discrete increments, such as apples, including, for example potato chips, where light source 120 lamps 123 illuminate the sample(s) 30 with detectors 80 receiving input with light detector output 82 conveyed as input to spectrometers 170 detectors 200.
- a lens 130 is depicted between the sample 30 and the detector 80.
- Illustrations 15 and 15A depict in detail, with filter 130 and mounting means, a single detector 80.
- a CPU 172 controlled by computer program, is not depicted m Fig. 10, 10A, 11, 12, 13, 14, 14A, 15 or 15A as a person of ordinary skill will appreciate such structure from viewing other drawings presented herein.
- Calibration of spectroscopic maturity and quality sensors involves building algorithms that relate the visible and near infrared spectrum of an individual fruit or vegetable to one or more of the following: Brix (including, but not limited to sugar content, or sweetness, or soluble solids content); acidity (including but not limited to total acidity, or sourness, or malic acid content or citric acid content or tartaric acid content); pH; firmness (including but not limited to crispness or hardness); internal disorders or defects including but not limited to watercore, browning, core rot, insect infestation.
- Brix including, but not limited to sugar content, or sweetness, or soluble solids content
- acidity including but not limited to total acidity, or sourness, or malic acid content or citric acid content or tartaric acid content
- pH firmness (including but not limited to crispness or hardness); internal disorders or defects including but not limited to watercore, browning, core rot, insect infestation.
- the individual property data collected above can be combined as follows: using the ratio of the sugar content to acid content to better predict eating quality, taste, sweet/sour ratio; using the combined data from two or more of the following: sugar content, acid content, pH, firmness, color, external and internal disorders to better predict eating quality.
- spectroscopic sensors for measuring fruits and vegetables while in motion on a sample conveyor 295 system in sorting and packing warehouses is illustrated in Fig. 10 and Fig. 10A and is done as follows:
- the presence or absence of a sample 30 and the position/location of the sample 30 relative to the point of spectrum measurement is determined using one or more of the following means: 1) sample 30 position determination means and or sample conveyor 295 position determination means, provided for example by an encoder or pulse generator 330, as seen in Fig.
- a proximity sensing means 340 including proximity sensors of, but not limited to, magnetic, inductance, optical, mechanical sensors; and also known as object presence sensors, such as thru-beam or reflectance sensors 341, is used to provide information about the position, i.e., orientation or location of the product on the packing or sorting line relative to the NTR sensor, e.g., light detector 80, and/or size of the sample 30, such proximity sensing means 340 and their use being of common knowledge to those practiced in the art of industrial processing object presence sensing.
- the proximity sensing means 340 can be placed 1, 2, 3 or ...n units of length, e.g., cups or pockets or conveyor belt length, before the NIR sensor, e.g., detector 80, to indicate if 1, 2, or 3 or...n more empty spaces, e.g., cups or pockets or a defined and known length of conveyor belt, are present in sequence, thus allowing a greater amount of time for performing dark spectra and/or reference spectra and/or standard/calibration samples.
- the presence or absence of sample(s) 30 is determined over a defined length of the particular sample conveyor 295 system.
- sample(s) 30 If sample(s) 30 is present, multiple visible and near-infrared spectra are acquired as the sample 30 passes by the light source 120 lamp(s) 123 providing light detector output 82 and spectrometer(s) 170 detector 200 input; such light collection may be achieved using a collimating lens 78 and or other light transmission means including for example fiber- optics to transfer the light that has interacted with the sample 30 to the spectrometer(s) 170 detectors 200. If no sample 30 is present, other reference measurements are made to improve stability and accuracy such as previously mentioned dark spectra, reference spectra (lamp intensity and color output), and standard/calibration samples, which may be optical filters or polymers or organic material with known and repeatable spectral characteristics.
- Measurements that are made when no sample is present include, but are not limited to 1) measuring a reference spectrum (intensity vs. wavelength) of the light source(s), 2) measuring the dark current (no light conditions) of one or more spectrometer(s) 170 detector(s) 200, including but not limited to the sample spectrometer(s) 170 and the reference spectrometer(s) 170, and 3) standard or calibration samples or filters 130 or material. Obtaining a spectrum of the lamp(s) for determining reference light output and obtaining baseline dark current spectra from detector(s). Both reference and dark spectra are used with sample spectrum to calculate the product's absorbance spectrum.
- reference light transmission means 320 e.g., a fiber-optic bundle which may be furcated, a light pipe or other means of transmitting light, with a common end 322 providing input to a reference spectrometer 170, and, where furcated, one or more branched ends 81 , each of which is mounted by means to allow only light from the light source 120 lamp(s) 123 to enter the reference light transmission means 320.
- a light shutter 300 is placed between each light source 120 lamp 123 and each reference light transmission means 320.
- the at least one light shutter 300 can be opened and closed separately by shutter control means 305 including, for example, driven by a linear actuator or rotary solenoid or other mechanical or pneumatic device, or all at once.
- shutter control means 305 including, for example, driven by a linear actuator or rotary solenoid or other mechanical or pneumatic device, or all at once.
- Each light source 120 lamp 123 in the system can be measured separately to determine if it is faulty or if it will soon need replacement based on a stored intensity vs. wavelength spectrum profile.
- the combined intensities from the reference light transmission means 320 is used as the reference spectrum for pu ⁇ oses of calculating an absorbance (or log 1/R) spectrum, which is linear with concentration (e.g., percent Brix or acidity or pounds of firmness, etc.).
- the dark current is largely affected by temperature and must be periodically measured and its intensity value at each wavelength (or detector) pixel subtracted from the reference spectrum obtained with the shutters 330 open.
- the sample spectrometer's 170 detector 200 dark cunent must also be periodically measured by closing light shutters 330 that are placed between the light source and the sample 30, or between the sample 30 and the sample spectrometer light collection fiber, seen here as detector 80 and detector output 82, or between the light collection fiber and the spectrometer 170.
- Fig. 9 is an elevation depicting an additional embodiment of the invention demonstrating at least one light detector 80 having at least one output 82 to at least one spectrometer 170 having at least one detector 200.
- At least one colluminatmg lens 78 intermediate the at least one light detector 80 and a sample 30.
- the at least one light detector 80 positioned to detect light from the sample 30.
- At least one aperture 310 in the shielding means to allow illumination of the sample 30 by the at least one light source 120 lamp 123.
- At least one light interruption means intermediate the at least one light source 120 lamp 123 and the at least one aperture 310.
- Light intenuption means provided, for example, by light shutter 300 means.
- the at least one light shutter 300 operable by at least one shutter control means 305, e.g., linear actuator or rotary solenoid operated by means, e.g., mechanical driven by electrical, pneumatic, hydraulic or other power means or other shutter means including for example liquid crystal screen operated by means.
- the at least one shutter control means 305 receiving control signals from at least one CPU 172 having at least one shutter operating control output 307.
- At least one reference light transmitting means 81 including, for example, fiber-optics including bifurcated fiber-optics, receiving reference light output from the at least one light source 120 lamp 123.
- At least one reference light interruption means comprised for example of shutter 301, intermediate the at least one light source 120 lamp 123 and the at least one reference light transmitting means 81.
- the at least one reference light shutter 301 operable by at least one shutter control means 305, e.g., linear actuator or rotary solenoid operated by means, e.g., mechanical driven by electrical, pneumatic, hydraulic or other power means or other shutter means including for example liquid crystal screen operated by means.
- the at least one reference light shutter 301 shutter control means 305 receiving control signals from at least one CPU 172 having at least one shutter operating control output 307.
- the at least one reference light transmitting means 81 providing an input to the at least one spectrometer 170 detector 200.
- the at least one CPU 172 providing at least one lamp power output 125 to the at least one light source 120 lamp 123.
- the at least one spectrometer 170 receiving input from at least one reference light transmitting means 81 having at least one output 82 received as in input to the at least one CPU 172.
- the spectrometer output 82 capable of A/D conversion to form input to the at least one CPU 172.
- the spectrometer output 82 capable of A D conversion to form input to the at least one CPU 172.
- a reference measurement of the light source 120 lamp(s) 123 intensity vs. wavelength output can also be obtained using reflecting means 360, as seen in Fig. 11, including but not limited to, for example, minors or other reflecting or diffusing material, including roughened aluminum, gold, Spectralon®, Teflon, ground glass, steel. Reflecting means 360 will be positioned to reflect light source 120 lamp 123 light to a detector 80 having an output 82 received by a spectrometer 170 detector 200.
- a colluminating lens 78 may be positioned intermediate the detector 80 and the light reflected by the reflecting means 360.
- Reflecting means 360 may be positioned, e.g., inserted via an aperture 310, for example where a case 250 is utilized, when a reference measurement is to be made as dictated by reflecting control means 308 as an output from a CPU 172.
- the CPU 172 via means, will detect the presence or absence of a sample 30 and, when a sample 30 is absent for "n" time increments or sample conveyor 295 movements will provide a reflecting control means 308 control signal to reflecting position means 306, e.g., linear actuator or rotary solenoid operated by means, e.g., mechanical driven by electrical, pneumatic, hydraulic or other power means.
- the reflecting means 360 capable of being withdrawn as dictated by reflecting control means 308 as an output from the CPU 172 when reference measurement is to be ceased and spectra measurement of a sample 30 resumed.
- a light reflecting or diffusing body for obtaining the reference spectrum may also be obtained by mechanical insertion of reference means 430, as seen in Fig. 12 and Fig. 13, in or near the location where actual sample 30 is normally measured, which is between the light source 120 lamp(s) 123 and reference light transmission means 320 leading to the sample spectrometer 170 detector 200(s).
- Insertion is by insertion means including but not limited to an actuator system 400 capable, upon receiving control signals or means as recognized by those of ordinary skill including control signals or means provided from a CPU 172, of operation of an actuator 410 causing a piston 420 to extend 421 and retract 422 as seen in Fig. 12 and 13.
- Power including for example electrical, pneumatic, hydraulic and other means, is provided to operate the actuator by power transmission means 440 as will be appreciated by those of ordinary skill.
- a CPU 172, controlled by computer program is not depicted in Fig. 10, 10A, 11, 12 or 13 as a person of ordinary skill will appreciate such structure from viewing other drawings presented herein. Achieving whole product measurement (minimizing errors due to localized measurement).
- two or more light sources 120 lamps 123 and/or detection 80 points are used.
- the product can be measured rolling or not rolling with a rolling measurement generally improving whole product measurement, while a non-rolling measurement provides better accuracy and introduces less spectral noise due to movement.
- a single fruit or vegetable sample 30 passes by the point of spectrum acquisition, multiple spectra are acquired, each spectrum representing a different measurement location or area on the product.
- One or more means may be used to determine the size or weight of the individual fruit or vegetable sample 30.
- Means for determining product size includes, but is not limited to 1) a separately determined weight or mass using sensors common to the industry, 2) utilizing the color sorter or defect sorter data (e.g., from camera or CCD images), 3) utilizing other size sensors based on magnetic, inductive, light reflectance or multiple light beam curtains, common to other industries.
- the relative size of the sample 30 can then be used to adjust the hardware spectrum acquisition parameters or the amount of light (by varying the aperture 310 size) to provide an improved signal-to-noise ratio spectrum for large samples 30 and/or to prevent detector 80 saturation by light for small product sample 30, e.g., detector 80 exposure or integration time can be set for longer time periods for large product samples 30 and for shorter time periods for small product.
- the absorbance spectrum is equal to the negative logarithm (base 10) of the ratio of the dark cunent conected sample spectrum to the dark cunent conected reference spectrum. All of the absorbance spectra for each product sample 30 can then be combined to produce a mean or average absorbance spectrum of the product sample. This average absorbance spectra can then be used to compute the component or property of interest based on a previously stored calibration algorithm. Alternatively, each absorbance spectrum can be used individually with a previously stored calibration algorithm to compute multiple results of the component or property of interest for an individual product, followed by determination of the average or mean component or property value computed by summing all of the values and dividing the resultant sum by the number of absorbance spectra used.
- the product can be transported past the NIR measurement location rolling or not rolling. If absorbance spectra are collected from the product as it is rolling, the exact location of any one measurement (one spectrum) is not usually known, and therefore the entire product (as opposed to one localized spot) must be analyzed for the component or property of interest. If calibration algorithms are constructed in this way (using measurements of rolling product), all of the retained spectra for that individual product are averaged to produce an average absorbance spectrum and the total product component or property is assigned to this one absorbance spectrum.
- each sub-portion of the product sample 30 will have one or more spectra associated with that particular location.
- the laboratory determined component or property is then assigned to each spectrum or spectra from that particular location.
- Mathematical processing is performed on absorbance spectra prior to conducting statistical correlation analysis and calibration model building. Absorbance spectra are pre-processed using a bin and smooth function. Partial least squares analysis (or variants thereof such as piecewise direct standardization) are then used to relate the processed absorbance spectrum to the assigned component and property values such as Brix, acidity, pH, firmness, color, internal or external disorder severity and type, and eating quality. Method to minimize the number of samples needed to develop a calibration model.
- spectra are collected on all test samples 30, 2) prior to destructive laboratory measurements, principal components analysis (PCA) is performed on the absorbance spectra, 3) Resultant Score plots from PCA (e.g., Score 1 vs. Score 2, Score 3 vs. Score 4, etc.) are then generated, 4) A subset of the original samples (e.g., 40% of the original number of samples) are selected from the Score plots in either a random fashion or by selecting samples that, as a group, yield a similar range, mean and standard deviation of score values compared to the entire group of original samples 30.
- PCA principal components analysis
- Calibration updates are periodically required to maintain measurement accuracy, particularly with agricultural product samples 30 that can vary in composition with growing conditions and variety.
- Several methods can be used to minimize the efforts of calibration updates. As fruit or vegetable samples 30 are analyzed in a packing and sorting warehouse, their visible/near infrared spectra can be examined by software to determine if the sample qualifies as a potential calibration update sample 30. Good calibration update samples 30 will cover low to high component values and will have Score values that cover the same range as the original sample's 30 Score values.
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
Applications Claiming Priority (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US524329 | 2000-03-13 | ||
US09/524,329 US6512577B1 (en) | 2000-03-13 | 2000-03-13 | Apparatus and method for measuring and correlating characteristics of fruit with visible/near infra-red spectrum |
US09/804,613 US6847447B2 (en) | 2000-03-13 | 2001-03-12 | Apparatus and method and techniques for measuring and correlating characteristics of fruit with visible/near infra-red spectrum |
US804613 | 2001-03-12 | ||
PCT/US2001/008146 WO2001069191A1 (fr) | 2000-03-13 | 2001-03-12 | Appareil et procede de mesure et de correlation de caracteristiques de fruits avec un spectre visible/infrarouge proche |
Publications (2)
Publication Number | Publication Date |
---|---|
EP1285244A1 EP1285244A1 (fr) | 2003-02-26 |
EP1285244A4 true EP1285244A4 (fr) | 2008-04-16 |
Family
ID=27061466
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP01918659A Withdrawn EP1285244A4 (fr) | 2000-03-13 | 2001-03-12 | Appareil et procede de mesure et de correlation de caracteristiques de fruits avec un spectre visible/infrarouge proche |
Country Status (10)
Country | Link |
---|---|
EP (1) | EP1285244A4 (fr) |
JP (1) | JP2003527594A (fr) |
CN (1) | CN1430723A (fr) |
AU (2) | AU4571001A (fr) |
BR (1) | BR0109219A (fr) |
CA (1) | CA2402669C (fr) |
IL (1) | IL151751A0 (fr) |
MX (1) | MXPA02009027A (fr) |
NZ (1) | NZ521919A (fr) |
WO (1) | WO2001069191A1 (fr) |
Families Citing this family (75)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB8719091D0 (en) * | 1987-08-12 | 1987-09-16 | Unilever Plc | Skin treatment composition |
JP2005249507A (ja) * | 2004-03-03 | 2005-09-15 | National Food Research Institute | 分光装置応答特性の平準化法 |
ITVI20050098A1 (it) | 2005-04-06 | 2006-10-07 | Caeleno Srl | Procedimento di valutazione del grado di maturazione fenolica di un frutto e relativo dispositivo |
CN100462712C (zh) * | 2005-08-03 | 2009-02-18 | 北京农业信息技术研究中心 | 便携式植物氮素和水分含量的无损检测方法及测量仪 |
CN100335886C (zh) * | 2006-05-09 | 2007-09-05 | 江西农业大学 | 反射式水果糖酸度检测仪 |
CN100335887C (zh) * | 2006-05-09 | 2007-09-05 | 江西农业大学 | 透射式水果营养成分测定装置 |
WO2009038206A1 (fr) * | 2007-09-21 | 2009-03-26 | Suntory Holdings Limited | Procédé d'analyse du spectre visible/infrarouge proche et fermentation de raisin |
JP5170379B2 (ja) * | 2007-10-17 | 2013-03-27 | 株式会社宝計機製作所 | 果菜類の糖度測定装置及び糖度測定方法 |
JP2010210355A (ja) * | 2009-03-09 | 2010-09-24 | Kobe Univ | 近赤外線分光法を用いた野菜等の成分の非破壊計測法および非破壊計測装置 |
PT104566B (pt) * | 2009-05-12 | 2013-09-20 | Univ Do Minho | Método e dispositivo de monitorização da produção de uva com espectroscopia uv-vis-swnir |
SE535853C2 (sv) * | 2010-07-08 | 2013-01-15 | Itab Scanflow Ab | Kassadisk |
WO2012005350A1 (fr) * | 2010-07-09 | 2012-01-12 | 千代田電子工業株式会社 | Dispositif de mesure non destructive pour fruits et légumes |
ES2388513B1 (es) * | 2011-02-22 | 2013-07-01 | Urtasun Tecnología Alimentaria S.L. | Sistema para análisis de productos vegetales durante el procesado de los mismos. |
ES2401624B2 (es) * | 2011-10-03 | 2014-01-15 | Universidad De Huelva | Dispositivo portatil para el reconocimiento de la madurez de frutos |
CN102590131A (zh) * | 2012-01-18 | 2012-07-18 | 中国农业大学 | 生鲜肉深层水分无损伤在线检测装置及方法 |
CN102645416A (zh) * | 2012-03-27 | 2012-08-22 | 北京林业大学 | 一种快速测定蓝莓中花青素含量的方法 |
WO2013157946A1 (fr) | 2012-04-20 | 2013-10-24 | Moba Group B.V. | Procédé de détection de défauts de produits alimentaires |
US9413988B2 (en) * | 2012-07-24 | 2016-08-09 | Fluke Corporation | Thermal imaging camera with graphical temperature plot |
GB2507828B (en) * | 2013-02-04 | 2015-05-13 | Messier Dowty Ltd | Deformation Detection Tool & Method for Detecting Deformation |
CN103281459A (zh) * | 2013-06-06 | 2013-09-04 | 仝晓萌 | 一种可测水果甜度和ph值的手机 |
CN103487397B (zh) * | 2013-09-23 | 2015-10-28 | 浙江农林大学 | 一种雷竹笋硬度快速检测方法及装置 |
CN104574341B (zh) * | 2013-10-11 | 2017-09-05 | 中国林业科学研究院资源信息研究所 | 一种水果糖度的确定方法和装置 |
CN104089881A (zh) * | 2013-12-17 | 2014-10-08 | 浙江工商大学 | 大黄鱼存储时间检测方法 |
CN103792235A (zh) * | 2014-01-10 | 2014-05-14 | 内蒙古农业大学 | 漫透射光谱与图像信息融合的蜜瓜内部品质在线检测方法与装置 |
JP2015148453A (ja) * | 2014-02-05 | 2015-08-20 | 国立大学法人神戸大学 | 青果物の品質測定装置、及び青果物の品質測定方法 |
CN103808689A (zh) * | 2014-02-21 | 2014-05-21 | 山东省农业科学院农业质量标准与检测技术研究所 | 五点式近红外水果成熟度及品质检测仪 |
ES2554396B1 (es) * | 2014-04-30 | 2016-10-07 | Universidad De Sevilla | Dispositivo de medición discreta por reflectancia de NIR multibanda del índice glucoacídico en uva para vinificación |
KR102238946B1 (ko) | 2014-06-27 | 2021-04-12 | 삼성전자주식회사 | 가스 센서, 이를 포함하는 냉장고 및 그 제어 방법 |
JP2016045091A (ja) * | 2014-08-22 | 2016-04-04 | 三井金属計測機工株式会社 | 青果類のアントシアニン含量の非破壊計測装置及び非破壊計測方法 |
CN104251837B (zh) * | 2014-10-17 | 2016-08-31 | 北京农业智能装备技术研究中心 | 水果内部品质近红外透射光谱在线检测系统及方法 |
CN104483287A (zh) * | 2014-12-05 | 2015-04-01 | 南京工业大学 | 基于近红外光谱的在线发酵过程生物学参数的检测装置及方法 |
WO2016142864A1 (fr) | 2015-03-09 | 2016-09-15 | Alliance For Sustainable Energy, Llc | Procédés discontinus ou continus pour évaluer les propriétés physiques et thermiques de films |
CN105241555B (zh) * | 2015-09-09 | 2018-07-31 | 浙江大学 | 基于水果表面不同组织热特性的水果损伤检测方法及装置 |
CN106680236A (zh) * | 2015-11-06 | 2017-05-17 | 深圳市芭田生态工程股份有限公司 | 一种将光谱数据和化学检测数据进行映射的方法 |
CN106680219A (zh) * | 2015-11-06 | 2017-05-17 | 深圳市芭田生态工程股份有限公司 | 一种利用光谱数据和化学检测数据建立数据模型的方法 |
TWI703313B (zh) * | 2015-12-09 | 2020-09-01 | 台灣超微光學股份有限公司 | 光譜儀的量測方法、光譜儀及其電子電路 |
CN105807014A (zh) * | 2016-01-13 | 2016-07-27 | 青岛万福质量检测有限公司 | 一种蔬果能量检测方法 |
CN107621460A (zh) * | 2016-07-15 | 2018-01-23 | 华东交通大学 | 一种近红外光谱漫透射技术黄桃隐性损伤与糖度同时在线检测装置与方法 |
CN109640674A (zh) * | 2016-07-22 | 2019-04-16 | 开利公司 | 冷链变质识别和管理系统 |
CN106323880A (zh) * | 2016-07-29 | 2017-01-11 | 河南科技大学 | 基于soc高光谱指数的植物叶片花青素含量估算方法及装置 |
CN106311627B (zh) * | 2016-08-05 | 2018-07-20 | 中山市恒辉自动化科技有限公司 | 一种食品自动检测装置 |
CN106525720B (zh) * | 2016-11-17 | 2019-03-29 | 常熟理工学院 | 采用双波长拟合相邻单波长实现食品安全快速检测的方法 |
CN107340244B (zh) * | 2017-02-06 | 2019-08-30 | 重庆文理学院 | 一种大棚芹菜的季节性最优光谱检测方法 |
CN106950183A (zh) * | 2017-02-28 | 2017-07-14 | 中国科学院合肥物质科学研究院 | 一种基于光谱技术的便携式土壤养分检测装置 |
CN107202761A (zh) * | 2017-06-09 | 2017-09-26 | 甘肃萃英大农科技有限公司 | 一种快速检测水果内部品质的便携式检测设备及检测方法 |
EP3546922B1 (fr) * | 2018-03-29 | 2021-06-30 | The Automation Partnership (Cambridge) Limited | Procédé et système informatisés pour l'analyse spectroscopique d'un matériau biologique |
US11270245B2 (en) | 2018-08-07 | 2022-03-08 | Walmart Apollo, Llc | System and method for forecasting deliveries via blockchain smart contracts |
CN109655414B (zh) * | 2018-11-27 | 2021-11-02 | Oppo广东移动通信有限公司 | 电子设备、信息推送方法及相关产品 |
DE102018220601A1 (de) * | 2018-11-29 | 2020-06-04 | Robert Bosch Gmbh | Spektrometervorrichtung und ein entsprechendes Verfahren zum Betreiben einer Spektrometervorrichtung |
JP7280038B2 (ja) * | 2018-12-20 | 2023-05-23 | 株式会社クボタ | 携帯型測定装置 |
US11035788B2 (en) | 2019-03-15 | 2021-06-15 | Tropicana Products, Inc. | Technologies for the selection and processing of plants |
JP6763995B2 (ja) * | 2019-04-18 | 2020-09-30 | 浜松ホトニクス株式会社 | 分光測定装置および分光測定方法 |
CN110263969B (zh) * | 2019-05-07 | 2023-06-02 | 西北农林科技大学 | 一种货架期苹果品质动态预测系统及预测方法 |
CN110208194A (zh) * | 2019-06-11 | 2019-09-06 | 华东交通大学 | 水果成熟度检测装置及成熟度评价方法 |
CN110687134B (zh) * | 2019-09-29 | 2021-03-16 | 武汉大学 | 带状fpc生产中的在线检测装置及方法 |
CN110927073A (zh) * | 2019-11-06 | 2020-03-27 | 广东弓叶科技有限公司 | 多光谱成像方法、电子装置及存储介质 |
SG10201911636PA (en) * | 2019-12-04 | 2020-03-30 | Teapasar Pte Ltd | System and method for non-destructive rapid food profiling using artificial intelligence |
CN111060473B (zh) * | 2020-01-15 | 2021-06-25 | 王丽娟 | 一种食品质量分析检测装置 |
JP7376380B2 (ja) * | 2020-02-13 | 2023-11-08 | 大王製紙株式会社 | 廃プラスチックの選別装置 |
CN111443089A (zh) * | 2020-04-27 | 2020-07-24 | 深圳市道创智能创新科技有限公司 | 分选检测设备 |
CN112986174A (zh) * | 2021-02-03 | 2021-06-18 | 佛山一本农业科技有限公司 | 一种基于近红外光谱的果蔬优选分拣方法、系统及可读存储介质 |
CN113390802B (zh) * | 2021-04-28 | 2023-03-14 | 中国农业科学院农产品加工研究所 | 一种用于肉品品质在线检测的距离调整方法及系统 |
CN113177925B (zh) * | 2021-05-11 | 2022-11-11 | 昆明理工大学 | 一种无损检测水果表面缺陷的方法 |
CN113418878A (zh) * | 2021-06-15 | 2021-09-21 | 桂林电子科技大学 | 基于微型光谱传感器的水果成熟度检测系统及方法 |
US12033329B2 (en) | 2021-07-22 | 2024-07-09 | X Development Llc | Sample segmentation |
US11995842B2 (en) | 2021-07-22 | 2024-05-28 | X Development Llc | Segmentation to improve chemical analysis |
CN113640244B (zh) * | 2021-07-28 | 2022-09-23 | 湖南师范大学 | 一种基于可见近红外光谱的果树品种鉴别方法 |
CN114235723B (zh) * | 2021-11-04 | 2024-04-02 | 福建师范大学 | 一种水果内在品质的无损测量方法及终端 |
CN114295883B (zh) * | 2022-01-06 | 2023-08-22 | 南京大学 | 一种提高光纤电流传感器测量精度的多维度标定方法 |
CN115561180A (zh) * | 2022-01-14 | 2023-01-03 | 深圳进化动力数码科技有限公司 | 基于红外多光谱和可见光的生鲜商品新鲜度检测设备 |
CN115046961A (zh) * | 2022-06-15 | 2022-09-13 | 浙江大学 | 适用于检测农产品中番茄红素含量的光谱检测方法 |
CN115406854B (zh) * | 2022-08-23 | 2024-08-16 | 绿萌科技股份有限公司 | 一种面向定标模型评价的短波红外检测水果腐烂的方法 |
CN115758779A (zh) * | 2022-11-29 | 2023-03-07 | 江苏大学 | 一种云服务的光电检测模型传递共享方法和物联网监测评价系统 |
CN116297297B (zh) * | 2023-05-22 | 2023-08-15 | 成都博瑞科传科技有限公司 | 基于阵列光谱和离子选择法的水中总氮检测方法及传感器 |
CN117093841B (zh) * | 2023-10-18 | 2024-02-09 | 中国科学院合肥物质科学研究院 | 小麦透射光谱的异常光谱筛选模型确定方法、装置及介质 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH04104041A (ja) * | 1990-08-23 | 1992-04-06 | Mitsui Mining & Smelting Co Ltd | 柑橘果実の糖度測定方法およびその装置 |
WO1998052037A1 (fr) * | 1997-05-15 | 1998-11-19 | Sinclair International Limited | Systeme pour evaluer l'etat d'un fruit ou d'un legume |
WO1999040419A1 (fr) * | 1998-02-06 | 1999-08-12 | Dsquared Development, Inc. | Dispositif d'analyse qualitative de grains |
WO2000002036A1 (fr) * | 1998-07-03 | 2000-01-13 | Centrum Voor Plantenveredelings- En Reproduktieonderzoek (Cpro-Dlo) | Procedes pour determiner la qualite de fruits et de baies et appareil pour trier fruits et baies |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5089701A (en) | 1990-08-06 | 1992-02-18 | The United States Of America As Represented By The Secretary Of Agriculture | Nondestructive measurement of soluble solids in fruits having a rind or skin |
US5303026A (en) * | 1991-02-26 | 1994-04-12 | The Regents Of The University Of California Los Alamos National Laboratory | Apparatus and method for spectroscopic analysis of scattering media |
JP2517858B2 (ja) | 1991-10-04 | 1996-07-24 | 農林水産省食品総合研究所長 | 近赤外透過スペクトルによる果実糖度の非破壊測定法 |
US5926262A (en) * | 1997-07-01 | 1999-07-20 | Lj Laboratories, L.L.C. | Apparatus and method for measuring optical characteristics of an object |
-
2001
- 2001-03-12 NZ NZ521919A patent/NZ521919A/en unknown
- 2001-03-12 CN CN01809360A patent/CN1430723A/zh active Pending
- 2001-03-12 JP JP2001568026A patent/JP2003527594A/ja not_active Withdrawn
- 2001-03-12 BR BR0109219-7A patent/BR0109219A/pt not_active IP Right Cessation
- 2001-03-12 EP EP01918659A patent/EP1285244A4/fr not_active Withdrawn
- 2001-03-12 CA CA002402669A patent/CA2402669C/fr not_active Expired - Fee Related
- 2001-03-12 AU AU4571001A patent/AU4571001A/xx active Pending
- 2001-03-12 MX MXPA02009027A patent/MXPA02009027A/es active IP Right Grant
- 2001-03-12 WO PCT/US2001/008146 patent/WO2001069191A1/fr active IP Right Grant
- 2001-03-12 AU AU2001245710A patent/AU2001245710B2/en not_active Ceased
- 2001-03-12 IL IL15175101A patent/IL151751A0/xx active IP Right Grant
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH04104041A (ja) * | 1990-08-23 | 1992-04-06 | Mitsui Mining & Smelting Co Ltd | 柑橘果実の糖度測定方法およびその装置 |
WO1998052037A1 (fr) * | 1997-05-15 | 1998-11-19 | Sinclair International Limited | Systeme pour evaluer l'etat d'un fruit ou d'un legume |
WO1999040419A1 (fr) * | 1998-02-06 | 1999-08-12 | Dsquared Development, Inc. | Dispositif d'analyse qualitative de grains |
WO2000002036A1 (fr) * | 1998-07-03 | 2000-01-13 | Centrum Voor Plantenveredelings- En Reproduktieonderzoek (Cpro-Dlo) | Procedes pour determiner la qualite de fruits et de baies et appareil pour trier fruits et baies |
Non-Patent Citations (1)
Title |
---|
See also references of WO0169191A1 * |
Also Published As
Publication number | Publication date |
---|---|
IL151751A0 (en) | 2003-04-10 |
AU4571001A (en) | 2001-09-24 |
NZ521919A (en) | 2004-03-26 |
BR0109219A (pt) | 2004-06-22 |
AU2001245710B2 (en) | 2005-02-17 |
CA2402669C (fr) | 2006-07-18 |
CA2402669A1 (fr) | 2001-09-20 |
MXPA02009027A (es) | 2004-08-19 |
JP2003527594A (ja) | 2003-09-16 |
CN1430723A (zh) | 2003-07-16 |
EP1285244A1 (fr) | 2003-02-26 |
WO2001069191A1 (fr) | 2001-09-20 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US6847447B2 (en) | Apparatus and method and techniques for measuring and correlating characteristics of fruit with visible/near infra-red spectrum | |
WO2001069191A1 (fr) | Appareil et procede de mesure et de correlation de caracteristiques de fruits avec un spectre visible/infrarouge proche | |
AU2001245710A1 (en) | Apparatus and method for measuring and correlating characteristics of fruit with visible/near infra-red spectrum | |
Walsh et al. | Visible-NIR ‘point’spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use | |
Lu et al. | A near–infrared sensing technique for measuring internal quality of apple fruit | |
US8072605B2 (en) | Method and apparatus for determining quality of fruit and vegetable products | |
Giovenzana et al. | Testing of a simplified LED based vis/NIR system for rapid ripeness evaluation of white grape (Vitis vinifera L.) for Franciacorta wine | |
McGlone et al. | Vis/NIR estimation at harvest of pre-and post-storage quality indices for ‘Royal Gala’apple | |
Ventura et al. | Non-destructive determination of soluble solids in apple fruit by near infrared spectroscopy (NIRS) | |
US5324945A (en) | Method of nondestructively measuring sugar content of fruit by using near infrared transmittance spectrum | |
Ignat et al. | Forecast of apple internal quality indices at harvest and during storage by VIS-NIR spectroscopy | |
Khodabakhshian et al. | Determining quality and maturity of pomegranates using multispectral imaging | |
JP2006170669A (ja) | 青果物の品質検査装置 | |
Bodria et al. | Optical techniques to estimate the ripeness of red-pigmented fruits | |
Kim et al. | Defect and ripeness inspection of citrus using NIR transmission spectrum | |
Chalucova et al. | Determination of green pea maturity by measurement of whole pea transmittance in the NIR region | |
Singh et al. | Optical sensors and online spectroscopy for automated quality and safety inspection of food products | |
Lee et al. | Measurement of sugar contents in citrus using near infrared transmittance | |
Omar et al. | Specialized optical fiber sensor for nondestructive intrinsic quality measurement of Averrhoa Carambola | |
Sharma et al. | Application of a Vis-NIR spectroscopic technique to measure the total soluble solids content of intact mangoes in motion on a belt conveyor | |
Pan et al. | Nondestructive measuring soluble solid contents and weight of intact pears based on on-line near-infrared spectroscopy | |
Hong et al. | A Comparison between the Post-and Pre-dispersive Near Infrared Spectroscopy in Non-Destructive Brix Prediction Using Artificial Neural Network | |
Polshin et al. | Vibrational spectroscopy techniques in the quality assessment of fruits and vegetables. | |
Ying et al. | Non-destructive measurement of sugar content in Fuji apple with bifurcated fiber optic sensor | |
Ying et al. | Performance of FT-NIR instrument for Brix value measurement of intact pear fruit |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PUAI | Public reference made under article 153(3) epc to a published international application that has entered the european phase |
Free format text: ORIGINAL CODE: 0009012 |
|
17P | Request for examination filed |
Effective date: 20021011 |
|
AK | Designated contracting states |
Kind code of ref document: A1 Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LI LU MC NL PT SE TR Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LI LU MC NL PT SE TR |
|
AX | Request for extension of the european patent |
Extension state: AL LT LV MK RO SI |
|
RAP1 | Party data changed (applicant data changed or rights of an application transferred) |
Owner name: FPS FOOD PROCESSING SYSTEMS B.V. |
|
A4 | Supplementary search report drawn up and despatched |
Effective date: 20080317 |
|
17Q | First examination report despatched |
Effective date: 20080701 |
|
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN |
|
18D | Application deemed to be withdrawn |
Effective date: 20101001 |