CN107064053A - Method based on polyphenol content in near-infrared hyperspectral technique Non-Destructive Testing red bayberry - Google Patents
Method based on polyphenol content in near-infrared hyperspectral technique Non-Destructive Testing red bayberry Download PDFInfo
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- 150000008442 polyphenolic compounds Chemical class 0.000 title claims abstract description 95
- 235000013824 polyphenols Nutrition 0.000 title claims abstract description 95
- 244000132436 Myrica rubra Species 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000009659 non-destructive testing Methods 0.000 title description 6
- 238000001228 spectrum Methods 0.000 claims abstract description 76
- 230000003595 spectral effect Effects 0.000 claims abstract description 26
- 230000001066 destructive effect Effects 0.000 claims abstract description 12
- 238000001514 detection method Methods 0.000 claims abstract description 12
- 229940126670 AB-836 Drugs 0.000 claims abstract description 7
- 235000007652 Arbutus Nutrition 0.000 claims abstract description 7
- 240000008327 Arbutus unedo Species 0.000 claims abstract description 7
- ISWSIDIOOBJBQZ-UHFFFAOYSA-N Phenol Chemical compound OC1=CC=CC=C1 ISWSIDIOOBJBQZ-UHFFFAOYSA-N 0.000 claims abstract description 7
- 238000004611 spectroscopical analysis Methods 0.000 claims description 25
- 238000012937 correction Methods 0.000 claims description 21
- 238000012360 testing method Methods 0.000 claims description 21
- LNTHITQWFMADLM-UHFFFAOYSA-N gallic acid Chemical compound OC(=O)C1=CC(O)=C(O)C(O)=C1 LNTHITQWFMADLM-UHFFFAOYSA-N 0.000 claims description 14
- 238000009499 grossing Methods 0.000 claims description 14
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 12
- 235000009134 Myrica cerifera Nutrition 0.000 claims description 12
- 244000061457 Solanum nigrum Species 0.000 claims description 12
- 239000000284 extract Substances 0.000 claims description 12
- 230000008859 change Effects 0.000 claims description 7
- 229940074391 gallic acid Drugs 0.000 claims description 7
- 235000004515 gallic acid Nutrition 0.000 claims description 7
- 230000003287 optical effect Effects 0.000 claims description 6
- 239000002245 particle Substances 0.000 claims description 6
- 230000035945 sensitivity Effects 0.000 claims description 6
- 239000007787 solid Substances 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000005096 rolling process Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 claims description 2
- 238000005457 optimization Methods 0.000 claims description 2
- 238000001914 filtration Methods 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 5
- 238000012545 processing Methods 0.000 description 7
- 238000010238 partial least squares regression Methods 0.000 description 5
- 230000000050 nutritive effect Effects 0.000 description 4
- 239000000126 substance Substances 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 230000000844 anti-bacterial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 206010010774 Constipation Diseases 0.000 description 1
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 208000001431 Psychomotor Agitation Diseases 0.000 description 1
- 206010038743 Restlessness Diseases 0.000 description 1
- 239000002253 acid Substances 0.000 description 1
- 239000013543 active substance Substances 0.000 description 1
- 150000001413 amino acids Chemical class 0.000 description 1
- 235000010208 anthocyanin Nutrition 0.000 description 1
- 229930002877 anthocyanin Natural products 0.000 description 1
- 239000004410 anthocyanin Substances 0.000 description 1
- 150000004636 anthocyanins Chemical class 0.000 description 1
- 230000000702 anti-platelet effect Effects 0.000 description 1
- 230000000259 anti-tumor effect Effects 0.000 description 1
- 239000003146 anticoagulant agent Substances 0.000 description 1
- 230000003078 antioxidant effect Effects 0.000 description 1
- 235000019789 appetite Nutrition 0.000 description 1
- 230000036528 appetite Effects 0.000 description 1
- 235000021028 berry Nutrition 0.000 description 1
- 230000031018 biological processes and functions Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 150000001720 carbohydrates Chemical class 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 229930003944 flavone Natural products 0.000 description 1
- 150000002213 flavones Chemical class 0.000 description 1
- 235000011949 flavones Nutrition 0.000 description 1
- HVQAJTFOCKOKIN-UHFFFAOYSA-N flavonol Natural products O1C2=CC=CC=C2C(=O)C(O)=C1C1=CC=CC=C1 HVQAJTFOCKOKIN-UHFFFAOYSA-N 0.000 description 1
- 150000002216 flavonol derivatives Chemical class 0.000 description 1
- 235000011957 flavonols Nutrition 0.000 description 1
- 239000000796 flavoring agent Substances 0.000 description 1
- 235000019634 flavors Nutrition 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 239000002398 materia medica Substances 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 150000007524 organic acids Chemical class 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000010791 quenching Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000002000 scavenging effect Effects 0.000 description 1
- 208000010110 spontaneous platelet aggregation Diseases 0.000 description 1
- 210000002784 stomach Anatomy 0.000 description 1
- 230000035922 thirst Effects 0.000 description 1
- 229940088594 vitamin Drugs 0.000 description 1
- 229930003231 vitamin Natural products 0.000 description 1
- 235000013343 vitamin Nutrition 0.000 description 1
- 239000011782 vitamin Substances 0.000 description 1
- 150000003722 vitamin derivatives Chemical class 0.000 description 1
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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- 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
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- 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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- 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
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- 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
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Abstract
The invention discloses a kind of method of polyphenol content non-destructive determination in red bayberry based on near-infrared EO-1 hyperion, this method comprises the following steps：The new arbutus sample of different cultivars is gathered, spectral scan is carried out using near-infrared Hyperspectral imager, 900~1700nm near infrared band high spectrum images is collected, obtains sample set spectrum；Polyphenol content in red bayberry sample is determined using Forint phenol method；Calibration model is set up using the PLS Return Law (PLSR), the optimum prediction model of polyphenol content in red bayberry is obtained.The content of polyphenol in lossless, efficient, quick detection red bayberry can be achieved by near-infrared high light spectrum image-forming technology in the present invention.
Description
Technical field
It is based on the present invention relates to a kind of method based on near-infrared EO-1 hyperion non-destructive determination polyphenol content, more particularly to one kind
The method of polyphenol content in near-infrared hyperspectral technique Non-Destructive Testing red bayberry.
Background technology
Red bayberry is the characteristic fruit of China, has the plantation of larger area in provinces such as Zhejiang, Fujian and Guangdong.Red bayberry has
Contain abundant carbohydrate, protein, amino acid, organic acid, mineral matter, vitamin in higher nutritive value, pulp
And polyphenols.《Compendium of Materia Medica》Record red bayberry " quench the thirst, and five stolen goods, can wash stomach, relieving restlessness burst bad odor ".Neontology and
Medical science, which further proves that red bayberry has, prevents constipation, antibacterial, the effects such as alleviating poor appetite.
Bayberry polyphenol is the general name of contained polyphenols in red bayberry, including flavones, flavonols, anthocyanin, former youngster
Boheic acid, gallic acid etc..Bayberry polyphenol has higher biological action, energy scavenging capacity oxygen radical, with anti-oxidant
Property, antitumor, antibacterial, the effect such as anti-platelet aggregation (nutritive value and its processing progress of Xia Qile, Cheng Shao south red bayberries
[J] Chinese foods and nutrition, 2005 (6):21-22.).
Bayberry polyphenol is the main active substances in red bayberry, and its local flavor, mouthfeel and nutritive value etc. had a major impact,
It is to determine one of key factor of red bayberry aesthetic quality.Red bayberry is distinct Chinese characteristics berry resource, and contains abundant polyphenol.Cause
This, the polyphenol content in research red bayberry helps to evaluate the nutritive value of red bayberry, has very to further exploitation red bayberry resource
Important meaning.
The content of polyphenol needs first to carry out sample extraction in chemical method detection red bayberry, this step meeting destructive test sample,
It is difficult to the Fast nondestructive evaluation of large sample amount.In recent years, near-infrared high light spectrum image-forming technology is used as a kind of Non-Destructive Testing side
Method causes extensive concern.Its maximum feature is the advantage for combining spectral technique and both computer image technologies, can be obtained
A large amount of image blocks for include continuous wavelength spectral information, because it has, detection speed is fast, efficiency high, low cost and other advantages, gets over
To be applied to quality of agricultural product and the Non-Destructive Testing of safety more.Therefore can be using near-infrared high light spectrum image-forming technology come fast
Polyphenol content in fast Non-Destructive Testing red bayberry.
The content of the invention
It is an object of the invention to provide polyphenol content method for measuring, purport in a kind of red bayberry based on near-infrared EO-1 hyperion
Realizing the detection of lossless, quick large sample amount.
, should the red bayberry based on near-infrared EO-1 hyperion the invention provides a kind of non-destructive determination method of polyphenol content in red bayberry
Middle polyphenol content method for measuring comprises the following steps：
1) foundation of sample spectrum：
Collect the new arbutus sample of different cultivars to be randomly assigned, set up calibration samples collection and test samples collection；To correction and
The sample that test samples are concentrated carries out spectral scan with Hyperspectral imager, wherein camera lens and sample distance for 10~
40cm, the time for exposure is 0.5~4s, and sample translational speed is 5~15mm/s, gathers 900~1700nm near infrared band blooms
Spectrogram picture, obtains correction and test samples collection spectrum；
2) measure of sample polyphenol content：
Polyphenol content in red bayberry sample is determined using forint- phenol law, sample is extracted using 30~90% ethanol, takes and carry in right amount
Take liquid to be reacted with forint phenol, detect, quantified by standard items of gallic acid under 760nm wavelength；
3) pretreatment of sample spectrum：
Using exponential smoothing (rolling average exponential smoothing (Moving Average), convolution exponential smoothing (Savitzky-Golay),
Gaussian smoothing filter (Gaussian filter) and median filter smoothness of image (Median filter smoothing) etc.) to sample
Original spectrum is handled, and eliminates spectral noise, improves resolution ratio and sensitivity；Combined standard normal variate becomes scaling method
(Standard normal variate transformation, SNV) or multiplicative scatter correction algorithm (Multiplicative
Scatter correction, MSC) processing, eliminate the shadow of solid particle size, surface scattering and change in optical path length to spectrum
Ring.Need to reject the larger individual data of otherness in preprocessing process.
4) calibration model is set up using multivariate regression algorithm：
900~1700nm of pretreatment post-equalization sample set spectroscopic data and polyphenol content is combined first, using partially minimum
Two multiply the Return Law (partial least squares regression, PLSR) modeling, pass through X- load (X-loading
Weight) figure, extracts spectrum characteristic parameter, chooses crest and trough section spectrum, characteristic spectrum wave-length coverage is 920~930,960
~980,1030~1075,1140~1160,1240~1290nm, to model obtained part or all of correction feature for the first time
Spectral wavelength data and polyphenol content reuse PLSR modelings, and detection sample set spectroscopic data is substituted into after modeling, polyphenol is calculated
Actual value, the relative coefficient (R with predicted value2), optimization features described above spectral region to R2More than 0.9, R is chosen2During maximum
Corresponding characteristic spectrum wave-length coverage, obtains best modeled characteristic spectrum, sets up the optimum prediction model of polyphenol content in red bayberry.
Above-mentioned optimum modeling characteristic spectrum wave-length coverage is 924~928,965~972,1052,1149~1153 and
The R of 1264nm, model predication value and actual value2For 0.9214, root-mean-square error (Root-mean-square error, RMSE)
It is worth for 0.0841.
5) forecast sample polyphenol content is determined：
Sample characteristic spectral wavelength is scanned, near-infrared high-spectral data is gathered, spectroscopic data is inputted into bayberry polyphenol content
Forecast model, calculating obtains polyphenol content in testing sample.
Spectroscopic data pretreatment, modeling and prediction are operated on The Unscrambler X softwares.
Polyphenol content method for measuring in the red bayberry based on near-infrared EO-1 hyperion that the present invention is provided, by using near-infrared
High spectrum image extracts red bayberry spectroscopic data, the polyphenol content in red bayberry is determined by forint- phenol law, with reference to Pretreated spectra side
Method, extracts characteristic spectrum, is modeled using least square regression (PLSR), obtains the forecast model of polyphenol content in red bayberry.
By selected characteristic spectral wavelength data modeling in present invention modeling, detection sample polyphenol content is, it is only necessary to scan
Characteristic spectrum wavelength data, can shorten sweep time, improve detection rates.Modeling uses 900~1700nm for the first time
All data of wavelength, for the first time modeling can draw the relation between wavelength and content, can be modeled, obtained by first time
Characteristic spectrum wavelength period, models the characteristic wavelength section obtained using modeling for the first time and models again, select specific wavelength for the second time
The data of section, improve modeling accuracy, reduce data amount of calculation.
The present invention can avoid existing chemical measure meeting destructive test object, can be achieved lossless, quick and substantial amounts of
Detect the content of polyphenol in red bayberry.
Brief description of the drawings
Fig. 1 is polyphenol content method for measuring stream in the red bayberry based on near-infrared EO-1 hyperion that the embodiment of the present invention one is provided
Cheng Tu；
Fig. 2 is the averaged spectrum curve map for the near-infrared high spectrum image that the embodiment of the present invention two is provided；
Fig. 3 be the embodiment of the present invention three provide smoothed processing and multiplicative scatter correction algorithm (MSC) processing after it is near
Infrared high spectrum curve map；
Fig. 4 is X- load (X-loading weight) figure for the bayberry polyphenol that the embodiment of the present invention three is provided；
Fig. 5 is the comparison of polyphenol predicted value and actual value in red bayberry under the PLSR modeling methods that the embodiment of the present invention four is provided
Schematic diagram；
Embodiment
With reference to specific embodiment, the invention will be further described, and what is be exemplified below is only the specific implementation of the present invention
Example, but protection scope of the present invention is not limited to that：
Embodiment one
1) foundation of sample spectrum：
Collect the new arbutus sample of different cultivars (400) to be randomly assigned, set up calibration samples collection (200) and examine sample
This collection (200)；The sample concentrated to correction and test samples carries out spectral scan, wherein camera lens with Hyperspectral imager
It is 10cm with sample distance, the time for exposure is 0.5s, and sample translational speed is 5mm/s, gathers 1000~1600nm near-infrared ripples
Section high spectrum image, obtains correction and test samples collection spectrum；
2) measure of sample polyphenol content：
Polyphenol content in red bayberry sample is determined using forint- phenol law, sample is extracted using 90% ethanol, takes appropriate extract solution
Reacted with forint phenol, detect, quantified by standard items of gallic acid under 760nm wavelength；
3) pretreatment of sample spectrum：
Sample original spectrum is handled using rolling average exponential smoothing (Moving Average), spectrum is eliminated and makes an uproar
Sound, improves resolution ratio and sensitivity；Combined standard normal variate becomes scaling method (Standard normal variate
Transformation, SNV), eliminate solid particle size, the influence of surface scattering and change in optical path length to spectrum.Pretreatment
During need to reject the larger individual data of otherness.
4) calibration model is set up using multivariate regression algorithm：
900~1700nm of pretreatment post-equalization sample set spectroscopic data and polyphenol content is combined first, using partially minimum
Two multiply the Return Law (partial least squares regression, PLSR) modeling, pass through X- load (X-loading
Weight) figure, extracts spectrum characteristic parameter, chooses crest and trough section spectrum, selection characteristic spectrum wavelength is 920~930,965
~972,1052nm wave band, with reusing PLSR modelings with polyphenol content, substitutes into detection sample set spectroscopic data after modeling,
Calculate the relative coefficient (R of polyphenol actual value and predicted value2), set up the optimum prediction model of polyphenol content in red bayberry.Model
The R of predicted value and actual value2For 0.9021, root-mean-square error (Root-mean-square error, RMSE) value is 0.1263.
5) forecast sample polyphenol content is determined：
Sample characteristic spectral wavelength is scanned, near-infrared high-spectral data is gathered, spectroscopic data is inputted into bayberry polyphenol content
Forecast model, calculating obtains polyphenol content in testing sample.
Spectroscopic data pretreatment, modeling and prediction are operated on The Unscrambler X softwares.
Fig. 1 is polyphenol content method for measuring flow chart in the red bayberry based on near-infrared EO-1 hyperion of the invention, main step
Rapid to include the collection of sample, the scanning of near-infrared EO-1 hyperion is read in spectroscopic data, spectroscopic data pretreatment, chemical detection red bayberry
Polyphenol content, forecast model is set up with reference to spectroscopic data and polyphenol content；
Embodiment two
The method of polyphenol content non-destructive determination in red bayberry based on near-infrared EO-1 hyperion, it is characterised in that the step of this method
It is as follows：
1) foundation of sample spectrum：
Collect the new arbutus sample of different cultivars (600) to be randomly assigned, set up calibration samples collection (400) and examine sample
This collection (200)；；The sample concentrated to correction and test samples carries out spectral scan, wherein mirror with Hyperspectral imager
Head is 40cm with sample distance, and the time for exposure is 3s, and sample translational speed is 15mm/s, gathers 900~1700nm near-infrared ripples
Section high spectrum image, obtains correction and test samples collection spectrum；
2) measure of sample polyphenol content：
Polyphenol content in red bayberry sample is determined using forint- phenol law, sample is extracted using 30~90% ethanol, takes and carry in right amount
Take liquid to be reacted with forint phenol, detect, quantified by standard items of gallic acid under 760nm wavelength；
3) pretreatment of sample spectrum：
Sample original spectrum is handled using convolution exponential smoothing (Savitzky-Golay), spectral noise is eliminated, carries
High-resolution and sensitivity；Combined standard normal variate becomes scaling method (Standard normal variate
Transformation, SNV), eliminate solid particle size, the influence of surface scattering and change in optical path length to spectrum.Pretreatment
During need to reject the larger individual data of otherness.
4) calibration model is set up using multivariate regression algorithm：
900~1700nm of pretreatment post-equalization sample set spectroscopic data and polyphenol content is combined first, using partially minimum
Two multiply the Return Law (partial least squares regression, PLSR) modeling, pass through X- load (X-loading
Weight) figure, extracts spectrum characteristic parameter, chooses crest and trough section spectrum, selection characteristic spectrum wavelength is 920~930,965
~972,1052,1149~1153,1240~1290nm wave band, with polyphenol content reuse PLSR modeling, model offspring
Enter to detect sample set spectroscopic data, calculate the relative coefficient (R of polyphenol actual value and predicted value2), set up polyphenol in red bayberry and contain
The R of the optimum prediction model of amount, model predication value and actual value2For 0.9168, root-mean-square error (Root-mean-square
Error, RMSE) value be 0.0957.
5) forecast sample polyphenol content is determined：
Sample characteristic spectral wavelength is scanned, near-infrared high-spectral data is gathered, spectroscopic data is inputted into bayberry polyphenol content
Forecast model, calculating obtains polyphenol content in testing sample.
Spectroscopic data pretreatment, modeling and prediction are operated on The Unscrambler X softwares.
Fig. 2 is the averaged spectrum curve map of the near-infrared high spectrum image of the present invention.
Embodiment three
The method of polyphenol content non-destructive determination in red bayberry based on near-infrared EO-1 hyperion, it is characterised in that the step of this method
It is as follows：
1) foundation of sample spectrum：
Collect the new arbutus sample of different cultivars (300) to be randomly assigned, set up calibration samples collection (200) and examine sample
This collection (100)；The sample concentrated to correction and test samples carries out spectral scan, wherein camera lens with Hyperspectral imager
It is 20cm with sample distance, the time for exposure is 2s, and sample translational speed is 10mm/s, gathers 900~1700nm near infrared bands
High spectrum image, obtains correction and test samples collection spectrum；
2) measure of sample polyphenol content：
Polyphenol content in red bayberry sample is determined using forint- phenol law, sample is extracted using 70% ethanol, takes appropriate extract solution
Reacted with forint phenol, detect, quantified by standard items of gallic acid under 760nm wavelength；
3) pretreatment of sample spectrum：
Sample original spectrum is handled using Gaussian smoothing filter (Gaussian filter), spectral noise is eliminated,
Improve resolution ratio and sensitivity；With reference to multiplicative scatter correction algorithm (Multiplicative scatter correction,
MSC) handle, eliminate solid particle size, the influence of surface scattering and change in optical path length to spectrum.Need to reject in preprocessing process
The larger individual data of otherness.
4) calibration model is set up using multivariate regression algorithm：
900~1700nm of pretreatment post-equalization sample set spectroscopic data and polyphenol content is combined first, using partially minimum
Two multiply the Return Law (partial least squares regression, PLSR) modeling, pass through X- load (X-loading
Weight) figure, extracts spectrum characteristic parameter, chooses crest and trough section spectrum, and selection characteristic spectrum wavelength is 1052,1149~
1153 and 1264nm wave band, PLSR modelings are reused with polyphenol content, and detection sample set spectroscopic data, meter are substituted into after modeling
Calculate the relative coefficient (R of polyphenol actual value and predicted value2), the optimum prediction model of polyphenol content in red bayberry is set up, model is pre-
The R of measured value and actual value2For 0.9003, root-mean-square error (Root-mean-square error, RMSE) value is 0.0745.
5) forecast sample polyphenol content is determined：
Sample characteristic spectral wavelength is scanned, near-infrared high-spectral data is gathered, spectroscopic data is inputted into bayberry polyphenol content
Forecast model, calculating obtains polyphenol content in testing sample.
Fig. 3 be the embodiment of the present invention three provide smoothed processing and multiplicative scatter correction algorithm (MSC) processing after it is near
Infrared high spectrum curve map；
Fig. 4 is X- load (X-loading weight) figure for the bayberry polyphenol that the embodiment of the present invention three is provided, by carrying
The crest and trough of lotus figure can select characteristic wavelength.
Example IV
The method of polyphenol content non-destructive determination in red bayberry based on near-infrared EO-1 hyperion, it is characterised in that the step of this method
It is as follows：
1) foundation of sample spectrum：
Collect the new arbutus sample of different cultivars (500) to be randomly assigned, set up calibration samples collection (300) and examine sample
This collection (200)；The sample concentrated to correction and test samples carries out spectral scan, wherein camera lens with Hyperspectral imager
It is 15cm with sample distance, the time for exposure is 3.5s, and sample translational speed is 15mm/s, gathers 900~1700nm near-infrared ripples
Section high spectrum image, obtains correction and test samples collection spectrum；
2) measure of sample polyphenol content：
Polyphenol content in red bayberry sample is determined using forint- phenol law, sample is extracted using 80% ethanol, takes appropriate extract solution
Reacted with forint phenol, detect, quantified by standard items of gallic acid under 760nm wavelength；
3) pretreatment of sample spectrum：
Sample original spectrum is handled using median filter smoothness of image (Median filter smoothing), eliminated
Spectral noise, improves resolution ratio and sensitivity；With reference to multiplicative scatter correction algorithm (Multiplicative scatter
Correction, MSC) processing, eliminate solid particle size, the influence of surface scattering and change in optical path length to spectrum.Pretreatment
During need to reject the larger individual data of otherness.
4) calibration model is set up using multivariate regression algorithm：
900~1700nm of pretreatment post-equalization sample set spectroscopic data and polyphenol content is combined first, using partially minimum
Two multiply the Return Law (partial least squares regression, PLSR) modeling, pass through X- load (X-loading
Weight) figure, extracts spectrum characteristic parameter, chooses crest and trough section spectrum, selection characteristic spectrum wavelength is 924~928,965
~972,1052,1149~1153 and 1264nm wave band, PLSR modelings are reused with polyphenol content, detection is substituted into after modeling
Sample set spectroscopic data, calculates the relative coefficient (R of polyphenol actual value and predicted value2), set up in red bayberry polyphenol content most
The R of excellent forecast model, model predication value and actual value2For 0.9214, root-mean-square error (Root-mean-square error,
RMSE) value is 0.0841.
5) forecast sample polyphenol content is determined：
Sample characteristic spectral wavelength is scanned, near-infrared high-spectral data is gathered, spectroscopic data is inputted into bayberry polyphenol content
Forecast model, calculating obtains polyphenol content in testing sample.
The bayberry polyphenol forecast model of Fig. 5 present invention, degree of fitting illustrates that the model can be preferably pre- up to 92.14%
Survey polyphenol content in red bayberry.
Finally, the present invention can be summarized with others without prejudice to the concrete form of the spirit or central characteristics of the present invention.Cause
This, no matter from the point of view of that point, the embodiment above of the invention can only all be considered the description of the invention and can not limit
The present invention, claims indicate the scope of the present invention, and above-mentioned explanation does not point out the scope of the present invention, therefore,
Any change in the implication and scope suitable with claims of the present invention, is all considered as being included in claims
In the range of.
Claims (4)
1. a kind of method of polyphenol content non-destructive determination in red bayberry based on near-infrared EO-1 hyperion, it is characterised in that the step of this method
It is rapid as follows：
1) foundation of sample spectrum：
Collect the new arbutus sample of different cultivars to be randomly assigned, set up calibration samples collection and test samples collection；To correcting and examining
Sample in sample set carries out spectral scan with Hyperspectral imager, and wherein camera lens and sample distance are 10~40cm, are exposed
It is 0.5~4s between light time, sample translational speed is 5~15mm/s, gathers 900~1700nm near infrared band high spectrum images,
Obtain calibration samples collection spectrum and test samples collection spectrum；
2) measure of sample polyphenol content：
Polyphenol content in red bayberry sample is determined using forint- phenol law, sample uses percent by volume to be carried for 30~90% ethanol
Take, take appropriate extract solution to be reacted with forint phenol, detect, quantified by standard items of gallic acid under 760nm wavelength；
3) pretreatment of sample spectrum：
Sample original spectrum is handled using exponential smoothing, spectral noise is eliminated, resolution ratio and sensitivity is improved；Combined standard
Normal variate becomes scaling method or multiplicative scatter correction algorithm process, eliminates solid particle size, surface scattering and change in optical path length
Influence to spectrum；
4) calibration model is set up using multivariate regression algorithm：
The calibration samples collection spectroscopic data and polyphenol content of 900~1700nm after pretreatment is combined first, using offset minimum binary
The Return Law is modeled, by X- load diagrams, extracts spectrum characteristic parameter, chooses crest and trough section spectrum, characteristic spectrum wave-length coverage
920~930,960~980,1030~1075,1140~1160,1240~1290nm, to model obtained part for the first time
Or whole correction feature spectral wavelength data and polyphenol content reuse multivariate regression algorithm modeling, detection sample is substituted into after modeling
This collection spectroscopic data, calculates the relative coefficient R of polyphenol actual value and predicted value2, optimization features described above spectral region to R2It is more than
0.9, choose R2Corresponding characteristic spectrum wave-length coverage during maximum, is obtained best modeled characteristic spectrum wave-length coverage, is built with this
The optimum prediction model of polyphenol content in vertical red bayberry.
5) forecast sample polyphenol content is determined：
Sample characteristic spectral wavelength is scanned, near-infrared high-spectral data is gathered, spectroscopic data is inputted into bayberry polyphenol content prediction
Model, calculating obtains polyphenol content in testing sample.
2. the method for polyphenol content non-destructive determination in the red bayberry according to claim 1 based on near-infrared EO-1 hyperion, it is special
Levy and be described step 3) exponential smoothing that uses includes rolling average exponential smoothing, convolution exponential smoothing, Gaussian smoothing filter, intermediate value
One or more in filtering.
3. the method for polyphenol content non-destructive determination in the red bayberry according to claim 1 based on near-infrared EO-1 hyperion, it is special
Levy and be described step 3) in preprocessing process, when finding that individual data items differ greatly, reject the individual differed greatly.
4. the method for polyphenol content non-destructive determination in the red bayberry according to claim 1 based on near-infrared EO-1 hyperion, it is special
Levy and be that described optimal characteristics spectral wavelength ranges are 924~928,965~972,1052,1149~1153 and 1264nm.
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