CN105606561A - Method for spectrometric determination of essential nutrients of pitaya - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 38
- 240000008086 Echinocereus enneacanthus Species 0.000 title abstract 5
- 235000010837 Echinocereus enneacanthus subsp brevispinus Nutrition 0.000 title abstract 5
- 235000006850 Echinocereus enneacanthus var dubius Nutrition 0.000 title abstract 5
- 235000020774 essential nutrients Nutrition 0.000 title abstract 4
- 238000004611 spectroscopical analysis Methods 0.000 claims abstract description 51
- 239000000126 substance Substances 0.000 claims abstract description 37
- 235000015097 nutrients Nutrition 0.000 claims abstract description 24
- 238000001228 spectrum Methods 0.000 claims abstract description 18
- 238000013499 data model Methods 0.000 claims abstract description 16
- 230000003595 spectral effect Effects 0.000 claims abstract description 16
- CIWBSHSKHKDKBQ-JLAZNSOCSA-N Ascorbic acid Chemical compound OC[C@H](O)[C@H]1OC(=O)C(O)=C1O CIWBSHSKHKDKBQ-JLAZNSOCSA-N 0.000 claims abstract description 14
- 238000004364 calculation method Methods 0.000 claims abstract description 14
- FPIPGXGPPPQFEQ-UHFFFAOYSA-N 13-cis retinol Natural products OCC=C(C)C=CC=C(C)C=CC1=C(C)CCCC1(C)C FPIPGXGPPPQFEQ-UHFFFAOYSA-N 0.000 claims abstract description 7
- ZZZCUOFIHGPKAK-UHFFFAOYSA-N D-erythro-ascorbic acid Natural products OCC1OC(=O)C(O)=C1O ZZZCUOFIHGPKAK-UHFFFAOYSA-N 0.000 claims abstract description 7
- FPIPGXGPPPQFEQ-BOOMUCAASA-N Vitamin A Natural products OC/C=C(/C)\C=C\C=C(\C)/C=C/C1=C(C)CCCC1(C)C FPIPGXGPPPQFEQ-BOOMUCAASA-N 0.000 claims abstract description 7
- 229930003268 Vitamin C Natural products 0.000 claims abstract description 7
- FPIPGXGPPPQFEQ-OVSJKPMPSA-N all-trans-retinol Chemical compound OC\C=C(/C)\C=C\C=C(/C)\C=C\C1=C(C)CCCC1(C)C FPIPGXGPPPQFEQ-OVSJKPMPSA-N 0.000 claims abstract description 7
- 235000000346 sugar Nutrition 0.000 claims abstract description 7
- 235000019155 vitamin A Nutrition 0.000 claims abstract description 7
- 239000011719 vitamin A Substances 0.000 claims abstract description 7
- 235000019154 vitamin C Nutrition 0.000 claims abstract description 7
- 239000011718 vitamin C Substances 0.000 claims abstract description 7
- 229940045997 vitamin a Drugs 0.000 claims abstract description 7
- PVNIIMVLHYAWGP-UHFFFAOYSA-N Niacin Chemical compound OC(=O)C1=CC=CN=C1 PVNIIMVLHYAWGP-UHFFFAOYSA-N 0.000 claims abstract description 6
- 229930003537 Vitamin B3 Natural products 0.000 claims abstract description 6
- 235000010980 cellulose Nutrition 0.000 claims abstract description 6
- 239000001913 cellulose Substances 0.000 claims abstract description 6
- 229920002678 cellulose Polymers 0.000 claims abstract description 6
- 239000003925 fat Substances 0.000 claims abstract description 6
- 235000019197 fats Nutrition 0.000 claims abstract description 6
- 229960003512 nicotinic acid Drugs 0.000 claims abstract description 6
- DFPAKSUCGFBDDF-UHFFFAOYSA-N nicotinic acid amide Natural products NC(=O)C1=CC=CN=C1 DFPAKSUCGFBDDF-UHFFFAOYSA-N 0.000 claims abstract description 6
- 235000018102 proteins Nutrition 0.000 claims abstract description 6
- 102000004169 proteins and genes Human genes 0.000 claims abstract description 6
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 6
- 235000019160 vitamin B3 Nutrition 0.000 claims abstract description 6
- 239000011708 vitamin B3 Substances 0.000 claims abstract description 6
- 235000018481 Hylocereus undatus Nutrition 0.000 claims description 75
- 244000157072 Hylocereus undatus Species 0.000 claims description 75
- 239000000203 mixture Substances 0.000 claims description 42
- 238000001514 detection method Methods 0.000 claims description 41
- 235000016709 nutrition Nutrition 0.000 claims description 27
- 238000002372 labelling Methods 0.000 claims description 23
- 238000013507 mapping Methods 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 13
- 238000013506 data mapping Methods 0.000 claims description 7
- 238000005259 measurement Methods 0.000 claims description 6
- 230000035764 nutrition Effects 0.000 claims description 5
- 238000013480 data collection Methods 0.000 claims description 4
- 238000002835 absorbance Methods 0.000 claims description 3
- 238000010183 spectrum analysis Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 2
- 239000000470 constituent Substances 0.000 claims 1
- 238000004451 qualitative analysis Methods 0.000 claims 1
- 238000004445 quantitative analysis Methods 0.000 claims 1
- 235000013399 edible fruits Nutrition 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 206010009944 Colon cancer Diseases 0.000 description 1
- 208000001333 Colorectal Neoplasms Diseases 0.000 description 1
- 235000017286 Melicoccus bijugatus Nutrition 0.000 description 1
- 235000018675 Solanum caripense Nutrition 0.000 description 1
- 240000002017 Solanum caripense Species 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 239000012491 analyte Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 235000005911 diet Nutrition 0.000 description 1
- 230000037213 diet Effects 0.000 description 1
- 235000013325 dietary fiber Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000001095 inductively coupled plasma mass spectrometry Methods 0.000 description 1
- 238000002329 infrared spectrum Methods 0.000 description 1
- 210000000936 intestine Anatomy 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001228 trophic effect Effects 0.000 description 1
- 230000002087 whitening effect Effects 0.000 description 1
Classifications
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- 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
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- Spectroscopy & Molecular Physics (AREA)
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- Biochemistry (AREA)
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- General Physics & Mathematics (AREA)
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- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a method for spectrometric determination of essential nutrients of pitaya. By means of the spectrometric method, it is determined that essential nutrients of pitaya include protein, sugar, cellulose, fat, vitamin A, vitamin B3 and vitamin C. The pitaya essential nutrient determination spectral range is 800-2500 nm. A data model between spectrum and chemical components is established. Calculation is conducted through a calculation server according to the name of a product to be tested and at least 127 formulas in a nutrient matching data model, so that the content of a certain nutrient in pitaya to be tested is obtained.
Description
Technical field
The invention belongs to material detection field, particularly relate to the method for utilizing spectral detection chemical composition, concreteRelate to a kind of method of utilizing spectrographic determination dragon fruit main nutrient composition.
Background technology
Dragon fruit, calls red dragon fruit, Green Dragon fruit, celestial mamoncillo, Yu Longguo. In dragon fruit fruit, contain abundantNutrient, dragon fruit is rich in the vitamin C of skin whitening and abundant has fat-reducing, reduces blood sugar, profitThe water-soluble dietary fiber of intestines, prevention colorectal cancer. Along with people are more and more higher to the attention rate of health diet, disappearThe person of expense is also more and more higher to the requirement of its Internal quality index in the time selecting fruit, and dragon fruit is as the most normal on marketThe fruit of seeing, the height of its nutritional labeling quality is usually used as selecting the main reference index of dragon fruit, and at presentThe mensuration of dragon fruit nutritional labeling is often used to expensive experiment instrument with destructive chemical analysis or in laboratoryDevice analysis is main, adopts the methods such as ICP-MS, AAS, chemical analysis and gravimetric method to dragon fruitNutritional labeling analyze, these methods all need dragon fruit to damage, and can not carry out on-the-spot test and divideAnalyse.
Utilize spectral technique analysis to become the developing direction of crops composition detection, modern near-infrared spectrum analysis skillArt analysis speed is fast, is because spectral measurement speed is very fast, also very fast reason of calculation by computer speed.Near-infrared spectral analysis technology be by the hydric group in analyte as OH, CH, NH, SH,PH etc. show characteristic absorption near infrared region, utilize spectral technique analysis to become crops composition detectionDeveloping direction, NIR spectra analysis analysis speed is fast, be because spectral measurement speed very fast,Also very fast reason of calculation by computer speed.
In current method, because the foundation of model carries out according to the pattern of collection of illustrative plates, or according to local numberAccording to what carry out, or on stoichiometric basis, carrying out match spectrum data, all there is modeling in these methodsRear adjustment difficulty is large, and basic data is incomplete, causes the correction of data model and the renewal of formula and changes difficultyGreatly, how setting up spectroscopic data and dragon fruit chemical composition data model becomes research emphasis and the difficult point in this field.
Summary of the invention
In order to overcome the existing various defects of said determination method, the invention provides one and utilize spectroscopic methodology to surveyThe method of determining dragon fruit main nutrient composition, the method comprises the steps:
A. determine that spectrographic determination dragon fruit main nutrient composition quantity is T, 1≤T≤7, nutritional labeling is dividedBe not: protein, sugar, cellulose, fat, vitamin A, vitamin B3, vitamin C;
B. determine that the spectral region that dragon fruit main nutrient composition is measured is: 800-2500nm;
C. the spectroscopic data that is more than or equal to more than 50 dragon fruit sample is collected and chemical detection data receiptsCollection;
D. utilize spectroscopic data and chemical detection data to set up data model, data model embedding data computing clothesBusiness device;
E. carry out spectroscopic data collection for dragon fruit to be checked, spectroscopic data inputted to data operation server,Dragon fruit and the nutritional labeling of selecting to need detection, described nutritional labeling is nutrition definite in steps A simultaneouslyOne or more in composition;
F. calculation server is according to the K in the name of product of required detection and nutritional labeling matched data modelIndividual formula, carries out computing, obtains the selected nutrient composition content of the dragon fruit that detects, K >=T.
Described step D utilizes spectroscopic data and chemical detection data to set up data model, comprises objectSpectroscopic data input spectrum database, by the input chemline of the chemical detection data of same object, thenChemical detection data in spectroscopic data in spectra database and chemline are shone upon, form this thingThe mapping database of body, comprises the steps:
Step I: the device launch spot of launching the spectrum collection of holding concurrently with light source irradiates dragon fruit sample A to be detected1，And collect dragon fruit sample A1The spectrum reflecting, adopts spectral analysis apparatus to determine the ripple of collected spectrumLength and absorbance, form dragon fruit sample A1Spectroscopic data;
Step II: to dragon fruit sample A1Carry out chemical analysis, analyze its T kind composition and content, form fireThe chemical detection data of dragon fruit sample;
Step II I: by dragon fruit A1Spectroscopic data and the same database of chemical detection data typing, form numberAccording to mapping X1;
Step IV: repeat above-mentioned steps I, Step II and Step II I, to dragon fruit sample A2To An+1Carry outRepeat for n time, form n group spectroscopic data and corresponding n group chemical detection data, by spectroscopic data and chemistry inspectionSurvey the same database of data typing, form the data-mapping set of n group data-mapping;
Step V: the spectroscopic data in data-mapping set in above-mentioned database is chosen to the suction of 2-100 wavelengthIt is corresponding that luminosity numerical value and chemical detection data are carried out, and determines that 2-100 wavelength absorbance changes and chemical detection numberThere is K formula of quantitative and qualitative analysis relation according to variation.
Step VI: the K of above-mentioned steps formula embedded to calculation server, gather dragon fruit fresh sample AXSpectroscopic data, by its input database time, 2-100 the wavelength typing computing that selecting step V is definiteServer, calculates the dragon fruit fresh sample chemical data that does not carry out actual detection, simultaneously by defeated this chemical dataGo out to display end and database, and in database with dragon fruit fresh sample AXSpectroscopic data form measure numberAccording to mapping, this measurement data mapping forms with existing data-mapping the data-mapping set of upgrading.
Step VII: K on database and the calculation server forming to step VI according to step I is publicFormula, is connected database with calculation server, arrange simultaneously the data input pin of database and data output end,Data input pin and the data output end of calculation server are set, form the spectroscopic data model of dragon fruit.
In above-mentioned steps IV, set up the method for data-mapping set specifically:
1) in spectroscopic data input spectrum database, set up data strip according to nanoscale, each nanoscale wavelengthBe defined as a data strip, by each nanoscale wavelength data and Wavelength strength data input database, formSpectroscopic data bar in spectra database, the corresponding formation of the nano wave length quantity k respective numbers in spectral regionSpectroscopic data bar k; For example wave-length coverage is 1000-1500 nanometer, has 501 spectroscopic data bars, and k is501, each spectroscopic data bar comprises wavelength and intensity;
2) in chemical detection data input chemline, the quantity of chemical detection data being pressed to the composition that detectsSet up data strip, set up data strip according to composition, each composition is defined as a data strip, by each composition titleAnd in component content input database, form the compositional data bar in chemline, the corresponding shape of quantity of compositionBecome the compositional data bar of respective numbers; For example, in the chemical detection data of object, there is composition in 5, have 5Bar data strip, is respectively Y1, Y2 ... Y5, each data strip comprises composition title and component content; OrChemical detection data are carried out to permutation and combination, then using all permutation and combination as data strip data database, rowRow combination;
3) by all the components data strip in corresponding a spectroscopic data bar in spectral catalogue chemical data table, shapeBecome mapping (enum) data group, the principle of correspondence is a spectroscopic data bar corresponding each compositional data bar respectively, forms single spectrumThe mapping (enum) data group corresponding with multicomponent; For example spectroscopic data bar is X1000, compositional data bar be Y1, Y2,Y3, Y4, Y5, for single spectrum and the mapping (enum) data group corresponding to multicomponent of 1000 nanometers be X1000Y1,X1000Y2,X1000Y3,X1000Y4,X1000Y5}；
According to the above-mentioned method of setting up mapping (enum) data group, by all spectroscopic data bars and chemical data in spectral catalogueIn table, all the components data strip is distinguished correspondence, forms the set of all mapping (enum) data groups, is mapping (enum) dataSet; For example spectroscopic data bar is 501, and compositional data bar is 5, the spectrum that one-time detection formsIn the mapping (enum) data set of data and chemical detection data, comprise 501 × 5=2505 bar data, this 2505 numberAccording to the mapping (enum) data set that is this detection of object.
Detect if the different samples of this object are carried out to n time, form n mapping (enum) data set, by nIn an independent database of the unified input of individual mapping (enum) data set, form this object Mapping database.
Said n is more than or equal to 30, n and is more than or equal to 50, is more preferably, and n is more than or equal to 100.
Preferably, the wave-length coverage of described spectrum is 800-1800nm, or the wave-length coverage of spectrum is1500-2500。
Preferably, described dragon fruit is that the basic identical but component content difference value of trophic component composition is in 20%Similar dragon fruit, dragon fruit sample is without pretreatment or cut and collect section processing.
Preferably, described spectroscopic data is that wavelength is wavelength and the intensity of 1001 wavelength of 800-1800nmData acquisition system, or spectroscopic data is that wavelength is the wavelength of 1001 wavelength of 1500-2500 and intensityData acquisition system.
Preferably, the number of described formula is K, and K value meets following relational expression:
T represents the nutritional labeling number of dragon fruit, and wherein C represents knockdown implication
Preferably, the T in described formula is 7, and the number of described formula is 127.
In method of the present invention, chemical measurement data, also referred to as stoichiometry data, refer to and pass through CucumberNational standard measure obtain chemical data. The for example vitamin A content in dragon fruit, need to pressMeasure according to national standard or professional standard, also can adopt the instrument that meets GB certainty of measurement to carry outMeasure.
In method of the present invention, spectroscopic data is the light energy of the different wave length collected by spectrum gathering-device,Be converted into spectroscopic data by light inverted signal device, spectroscopic data General Requirements has spectral intensity, even certainWavelength light intensity of wave is zero, also needs to record at spectroscopic data.
The beneficial effect of the inventive method is embodied in following three aspects:
1,, when the present invention detects, dragon fruit sample, without pretreatment, reaches the effect of nondestructive measurement;
2, adopt spectral technique analysis to measure, analysis speed is fast, can detect at any time, convenient and swift;
3, the mapping method of spectroscopic data of the present invention and chemical detection data takes into full account the single thing that detects dataThe characteristic of matter characteristic and combination material can be measured the single nutritional labeling of dragon fruit or multiple as required simultaneouslyComposition;
4, the modeling method between spectroscopic data provided by the invention and chemical composition data can conveniently be upgraded basisDatabase, provides data greatly and reliably, improves and detects accuracy, reduces human error.
Detailed description of the invention
Embodiment 1
A method of utilizing spectrographic determination dragon fruit main nutrient composition, the method comprises the steps:
A. determine that spectrographic determination dragon fruit main nutrient composition is: protein, sugar, cellulose, fat,Vitamin A, vitamin B3, vitamin C;
B. determine that the spectral region that dragon fruit main nutrient composition is measured is: 800-2500nm;
C. carrying out the spectroscopic data of 100 dragon fruit samples collects and chemical detection Data Collection;
D. utilize spectroscopic data and chemical detection data to set up data model, data model embedding data computing clothesBusiness device;
E. carry out spectroscopic data collection for dragon fruit to be checked, spectroscopic data inputted to data operation server,Dragon fruit and the nutritional labeling of selecting to need detection, described nutritional labeling is nutrition definite in steps A simultaneouslyOne or more in composition;
F. calculation server is according in the name of product of required detection and nutritional labeling matched data model127 formula, carry out computing, obtain the selected nutrient composition content of the dragon fruit that detects, K >=T.
Embodiment 2
A method of utilizing spectrographic determination dragon fruit main nutrient composition, the method comprises the steps:
A. determine that spectrographic determination dragon fruit main nutrient composition is: protein, sugar, cellulose, fat,Vitamin A, vitamin B3, vitamin C;
B. determine that the spectral region that dragon fruit main nutrient composition is measured is: 800-1800nm;
C. carrying out the spectroscopic data of 500 dragon fruit samples collects and chemical detection Data Collection;
D. utilize spectroscopic data and chemical detection data to set up data model, data model embedding data computing clothesBusiness device;
E. carry out spectroscopic data collection for dragon fruit to be checked, spectroscopic data inputted to data operation server,Dragon fruit and the nutritional labeling of selecting to need detection, described nutritional labeling is nutrition definite in steps A simultaneouslyOne or more in composition;
F. calculation server is according in the name of product of required detection and nutritional labeling matched data model127 formula, carry out computing, obtain the selected nutrient composition content of the dragon fruit that detects, K >=T.
Embodiment 3
A method of utilizing spectrographic determination dragon fruit main nutrient composition, the method comprises the steps:
A. determine that spectrographic determination dragon fruit main nutrient composition is: protein, sugar, cellulose, fat,Vitamin A, vitamin B3, vitamin C;
B. determine that the spectral region that dragon fruit main nutrient composition is measured is: 1000-2500nm;
C. carrying out the spectroscopic data of 250 dragon fruit samples collects and chemical detection Data Collection;
D. utilize spectroscopic data and chemical detection data to set up data model, data model embedding data computing clothesBusiness device;
E. carry out spectroscopic data collection for dragon fruit to be checked, spectroscopic data inputted to data operation server,Dragon fruit and the nutritional labeling of selecting to need detection, described nutritional labeling is nutrition definite in steps A simultaneouslyOne or more in composition;
F. calculation server is according in the name of product of required detection and nutritional labeling matched data model127 formula, carry out computing, obtain the selected nutrient composition content of the dragon fruit that detects, K >=T.
Claims (8)
1. a method of utilizing spectrographic determination dragon fruit main nutrient composition, the method comprises the steps:
A. determine that spectrographic determination dragon fruit main nutrient composition quantity is T, 1≤T≤7, nutritional labeling is dividedBe not: protein, sugar, cellulose, fat, vitamin A, vitamin B3, vitamin C;
B. determine that the spectral region that dragon fruit main nutrient composition is measured is: 800-2500nm;
C. the spectroscopic data that is more than or equal to n above dragon fruit sample is collected and chemical detection Data Collection,Wherein n is more than or equal to 50;
D. utilize spectroscopic data and chemical detection data to set up the data model that K formula composition dragon fruit detects,Data model embedding data calculation server;
E. carry out spectroscopic data collection for dragon fruit to be checked, spectroscopic data inputted to data operation server,Dragon fruit and the nutritional labeling of selecting to need detection, described nutritional labeling is nutrition definite in steps A simultaneouslyOne or more in composition;
F. calculation server is according to the K in the name of product of required detection and nutritional labeling matched data modelIndividual formula, carries out computing, obtains the selected nutrient composition content of the dragon fruit that detects, K >=T.
2. method according to claim 1, is characterized in that K value meets following relational expression:
T represents the nutritional labeling number of dragon fruit, and wherein C represents knockdown implication.
3. method according to claim 2, is characterized in that, T=7, and the number of described formula is 127Individual.
4. method according to claim 1, is characterized in that, the wave-length coverage of described spectrum is800-1800nm, or the wave-length coverage of spectrum is 1500-2500nm.
5. according to the method described in claim 1-4 any one, it is characterized in that, described dragon fruit is nutritionBasic identical but the similar dragon fruit of component content difference value in 20% of constituent, dragon fruit sample withoutPretreatment or cut and collect section processing.
6. method according to claim 1, is characterized in that n is more than or equal to 100, and preferred n is largeIn equaling 200.
7. method according to claim 1, is characterized in that, described step D data model is set upMethod comprises the steps:
Step I: the device launch spot of launching the spectrum collection of holding concurrently with light source irradiates dragon fruit sample A to be detected1，Then collect dragon fruit sample A1The spectrum that reflects, adopts spectral analysis apparatus to determine collected spectrumWavelength and absorbance, form dragon fruit sample A1Spectroscopic data;
Step II: to dragon fruit sample A1Carry out chemical analysis, analyze its T kind composition and content, form fireThe chemical detection data of dragon fruit sample;
Step II I: by dragon fruit A1Spectroscopic data and the same database of chemical detection data typing, form numberAccording to mapping X1;
Step IV: repeat above-mentioned steps I, Step II and Step II I, to dragon fruit sample A2To An+1Carry outRepeat for n time, form n group spectroscopic data and corresponding n group chemical detection data, by spectroscopic data and chemistry inspectionSurvey the same database of data typing, form the data-mapping set of n group data-mapping;
Step V: the spectroscopic data in data-mapping set in above-mentioned database is chosen to the suction of 2-100 wavelengthIt is corresponding that luminosity numerical value and chemical detection data are carried out, and determines that 2-100 wavelength absorbance changes and chemical detection numberThere is K formula of quantitative and qualitative analysis relation according to variation.
Step VI: the K of above-mentioned steps formula embedded to calculation server, gather dragon fruit fresh sample AXSpectroscopic data, by its input database time, 2-100 the wavelength typing computing that selecting step V is definiteServer, calculates the dragon fruit fresh sample chemical data that does not carry out actual detection, simultaneously by defeated this chemical dataGo out to display end and database, and in database with dragon fruit fresh sample AXSpectroscopic data form measure numberAccording to mapping, this measurement data mapping forms with existing data-mapping the data-mapping set of upgrading.
Step VII: K on database and the calculation server forming to step VI according to step I is publicFormula, is connected database with calculation server, arrange simultaneously the data input pin of database and data output end,Data input pin and the data output end of calculation server are set, form the spectroscopic data model of dragon fruit.
8. method according to claim 7, is characterized in that, described spectroscopic data is that wavelength isThe wavelength of 1001 wavelength of 800-1800nm and the data acquisition system of intensity, or spectroscopic data is that wavelength isThe wavelength of 1001 wavelength of 1500-2500 and the data acquisition system of intensity.
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