CN103344597A - Anti-flavored-interference near infrared non-destructive testing method for internal components of lotus roots - Google Patents
Anti-flavored-interference near infrared non-destructive testing method for internal components of lotus roots Download PDFInfo
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- 238000012315 univariate regression analysis Methods 0.000 abstract 1
- 238000012545 processing Methods 0.000 description 9
- 238000001514 detection method Methods 0.000 description 6
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- CIWBSHSKHKDKBQ-JLAZNSOCSA-N Ascorbic acid Chemical compound OC[C@H](O)[C@H]1OC(=O)C(O)=C1O CIWBSHSKHKDKBQ-JLAZNSOCSA-N 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
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- ZZZCUOFIHGPKAK-UHFFFAOYSA-N D-erythro-ascorbic acid Natural products OCC1OC(=O)C(O)=C1O ZZZCUOFIHGPKAK-UHFFFAOYSA-N 0.000 description 2
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
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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
- G01N21/3563—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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- 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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Abstract
The invention provides an anti-flavored-interference near infrared non-destructive testing method for internal components of lotus roots, belonging to the technical field of the near infrared non-destructive testing. The invention comprises collecting lotus root samples flavored by salt or sugar with different concentrations, collecting near-infrared spectral information and physical data of the internal components of the samples, pre-treating spectrum data, building a near infrared spectroscopy model under a background of the salt or the sugar with the different concentrations by using a partial least squares method, performing correlation analysis and univariate regression analysis for the near infrared spectroscopy of salt or sugar content and the internal components of the lotus roots, correcting interferences of changes of the salt or sugar concentrations on the near infrared non-destructive testing for the internal components of the lotus roots, and finally building a near infrared spectroscopy model for the internal components of the flavored lotus roots on a basis of the near infrared spectroscopy model under the background of the salt or sugar with the different concentrations, wherein the model of the flavored lotus roots is adapt to changes in the concentrations of the salt or the sugar. The method effectively corrects interferences of changes of the salt or sugar concentrations on the near infrared non-destructive testing for the internal components of the lotus roots, and realizes rapid, accurate and real-time nondestructive test for the internal components of the lotus roots flavored by the salt or the sugar with different concentrations.
Description
Technical field
The method of the lotus rhizome internal component near infrared Non-Destructive Testing that a kind of anti-seasoning is disturbed relates to the method for utilizing near-infrared spectrum technique to carry out the Non-Destructive Testing of seasoning lotus rhizome internal component particularly.Belong to the near infrared technical field of nondestructive testing.
Background technology
Lotus rhizome is the perennial large-scale aquatic herbaceous plant of Nymphaeceae Nelumbo, is the characteristic aquatic vegetable of China.Along with further going deep into of reform and opening-up, the lotus rhizome cultivated area of China constantly enlarges, reached more than 300,000 mu at present, mainly be distributed in the Yangtze river basin and on the south the each province, it is the most concentrated wherein to distribute with Taihu Lake, Hongchehu Lake, Dongting Lake, Poyang Lake isoperimetric border district district especially, and the gross annual output amount reaches more than 2,000,000 tons.The nutritional labeling of lotus rhizome is extremely abundant, mineral matters such as starch-containing, robust fibre, protein, carrotene, thiamine, lactochrome, niacin and calcium, phosphorus, iron, it can be edible and medicinal, has functions such as heat-clearing, relieving summer-heat, treatment diarrhoea, dysentery and dizziness.Processing exploitation, foreign exchange earning to the lotus rhizome product at present strengthened dynamics, successively develop more than ten individual serial more or less a hundred converted products such as salt marsh, fresh-keeping, quick-frozen, poach, lotus root juice beverage, lotus root starch product, fast food mixings dish, wherein sugaring, increase day by day with the market demand of the seasoned food of processing such as salt.
Lotus rhizome is subjected to factor affecting such as artificial and natural in growth course, individual difference is bigger; In the processing processing procedure, through the variable concentrations seasoning, quality discrepancy is bigger.In order to ensure the integrated quality of seasoning lotus rhizome, be far from being enough based on the differentiation of surfaces such as color and luster, shape and size, and the detection of carrying out seasoning lotus rhizome internal component is very important.At present at home, the means that fruit-vegetable quality detects are backward relatively, the overwhelming majority rests on the primitive stage of identifying by artificial sense, this subjective assessment method is subjected to personal experience, color condition effect such as rate, mood, degree of fatigue and light respectively, labor capacity is big in the operating process, production efficiency is low, error is bigger, and great majority rest on the qualitative discrimination, its subjectivity, accuracy are relatively poor, this causes the second-rate of China's exported product to a great extent, very different, lack competitiveness in the international market.Another part then relies on the analytical chemistry method to detect, and makes integrality and the edibility that need destroy fruits and vegetables in this way, and is comparatively complicated, wastes time and energy, and is difficult to realize quick, pollution-free and harmlessization detection.The present invention adopts near infrared Dynamic Non-Destruction Measurement method can effectively overcome the deficiency that existing conventional sense exists, realize the synchro measure of the important indicators such as starch, robust fibre and protein of variable concentrations seasoning lotus rhizome, have fast, non-destructive, no reagent analysis, safety, efficient, low cost and measure characteristics such as various ingredients simultaneously, to operation instruction, the quality classification of fruits and vegetables, and reduce sampling waste etc. and all have very high using value.
Near-infrared spectral analysis technology is more at the quality Non-Destructive Testing application report of fruit at present, and in the correlative study at home, Liu Yande (2005, Zhejiang University's doctorate paper) has studied the near infrared lossless detection method of pol and the acidity of fruit.Li Xin (2007, Agricultural University Of Shenyang's Master's thesis) has studied the method for utilizing near infrared technology Non-Destructive Testing apple pear quality, has related to soluble solid and the index of quality such as total reducing sugar, acidity, vitamin C, water cut and single fruit weight of apple pear.Xia Junfang etc. (2007) utilize near infrared spectrum accurately to predict the oranges and tangerines Vitamin C content.Cao Xia etc. (2013) use near-infrared diffuse reflection spectrum technology Non-Destructive Testing mango pol.But the report of this technology aspect vegetables is few, relevant report about the Non-Destructive Testing of lotus rhizome internal component near infrared only has one piece both at home and abroad, and Zhang Yongjun etc. (2008) have studied the near infrared light spectrum model of compositions such as lotus rhizome moisture, pol, robust fibre and hardness.The present invention is different with it to be, the object of research is not fresh feed, but the seasoning lotus rhizome that processing is handled.Primary study of the present invention different salt or the sugared concentration seasoning influence to the near infrared spectrum Non-Destructive Testing of lotus rhizome internal component, and disposal route has been proposed.
About the research of component concentration difference to the near infrared Non-Destructive Testing influence of sample, present domestic report is less.Li Yong etc. (2005) have studied the influence of moisture difference to the robustness of near-infrared spectrum analysis model, respectively from spectrum pre-service, valid interval choose and theoretical analysis has been carried out in three aspects such as overall calibration model, do not carry out the correction of model.Zhang Lingshuai etc. (2005) have analyzed the moisture of three different gradients to the influence of near infrared mensuration aleuronat content, and testing sample should keep suitable moisture when drawing near infrared mensuration aleuronat content.But do not study the relation of component concentration and the model scope of application.The present invention is different with it to be, analyzed the near infrared light spectrum model under different salt or the sugared concentration background earlier, pass through correlation analysis and single argument regretional analysis to the near infrared spectrum of salt or sugared content and lotus rhizome internal component again, revise salt or sugared concentration change to the interference of the near infrared Non-Destructive Testing of lotus rhizome internal component, set up the near-infrared spectrum analysis model that adapts to the seasoning lotus rhizome internal component of salinity or sugared concentration change at last.
Summary of the invention
The objective of the invention is to overcome the deficiency that existing conventional sense exists in seasoning fruits and vegetables context of detection, be the near-infrared analysis model that background is set up each concentration gradient with different salinity or sugared concentration, and the near infrared spectrum of salt or sugared content and lotus rhizome internal component correlation analysis and single argument regretional analysis have been carried out, determine the quantitative relationship of the predicted value increment of salt or sugared concentration and lotus rhizome internal component, revise salt or sugared concentration change then to the interference of near infrared Non-Destructive Testing, set up the near-infrared spectrum analysis model that adapts to the seasoning lotus rhizome internal component of salinity or sugared concentration change at last.The invention provides a kind of near-infrared spectrum technique that utilizes and carry out seasoning lotus rhizome internal component lossless detection method, it can realize harmlessization detection, and is easy and simple to handle, and practicality is good, the reliability height.
Technical scheme of the present invention: the method for the lotus rhizome internal component near infrared Non-Destructive Testing that a kind of anti-seasoning is disturbed, gather the lotus rhizome sample sets through different salinity or sugared concentration seasoning; The physicochemical data reference value of the near infrared light spectrum information of collected specimens and internal component then; The spectroscopic data pre-service; Back employing partial least square method is set up the near infrared spectrum calibration model under its variable concentrations gradient background; The near infrared spectrum of salt or sugared content and lotus rhizome internal component is carried out correlation analysis and overall calibration model is set up in the single argument regretional analysis; Revise salt or sugared concentration change to the interference of the near infrared Non-Destructive Testing of lotus rhizome internal component, last near infrared spectrum model based under variable concentrations gradient background is set up the near-infrared spectrum analysis model of the seasoning lotus rhizome internal component that adapts to salinity or sugared concentration change; Be the parameter of seasoning lotus rhizome internal component to be measured by this model with the near infrared spectrum information translation of testing sample, realize the Non-Destructive Testing of seasoning lotus rhizome internal component; Step is:
(1) preparation of sample sets: gather not seasoning and 5%, the lotus rhizome sample of 10%, 15%, 20% salinity or sugared concentration seasoning, choosing with difference respectively is the required calibration set of modeling and the forecast set of background with salt or sugaring concentration, and wherein the quantitative proportion of calibration set and forecast set is 4:1; With unforseen in contrast the group, seasoning as experimental group;
(2) near infrared spectra collection of sample: the seasoning lotus rhizome sample that step (1) is obtained uses near infrared spectroscopy instrument to carry out spectra collection, adopts the diffuse reflection absorption spectroscopy, and test parameters is made as: sweep limit is 4000～10000cm
-1, resolution is 8cm
-1, scanning times is 16 times; During near-infrared spectral measurement, complete one section clean lotus rhizome is placed on the diffuse reflection probe reposefully, each sample need carry out spectral measurement 4 times, lay respectively at 4 relative positions at maximum gauge place, avoid tangible scratch, scar class surface imperfection as far as possible, spectrum in 4 measurements is averaged, obtain the averaged spectrum of every section lotus rhizome;
(3) mensuration of sample interior composition physicochemical data reference value: after calibration set and forecast set sample carried out spectra collection, to its mensuration of carrying out the reference value of internal component, the result all represented with butt as early as possible.Content of starch is measured with reference to GB/T5009.9-2008 " mensuration of starch in the food "-acid-hydrolysis method; Crude fiber content is measured with reference to GB/T5009.10-2003 " coarse-fibred mensuration in the plant food "; Protein content is with reference to GB/T5009.5-2010 " protein measuring in the food ";
(4) pre-service of spectroscopic data: near infrared spectra collection and handle by TQ Analyst and realize, pretreated method have that centralization, canonical variable conversion, additional scatter correction, orthogonal signal are proofreaied and correct, level and smooth, small echo denoising, differentiate variation and genetic algorithm Wavelength optimization; Adopt which kind of preprocess method or do not need pre-service to select according to the quality of spectrum and the concrete condition of background interference, when using the preprocess method of spectrum, can be the independent use of a certain method of said method, also can be being used in combination of above-mentioned several method; Calculate by repeatedly optimizing, obtain the modeling optimization parameter of different salt or sugared concentration seasoning lotus rhizome internal component;
(5) foundation of the near infrared spectrum calibration model of variable concentrations gradient: in conjunction with the spectroscopic data quantitative test function of mating in the TQ analyst spectral analysis software and Matlab software, adopt partial least square method to set up forecast model to the spectroscopic data of the sample of pretreated calibration set and forecast set and the reference value of measuring starch, robust fibre and the protein component of the lotus rhizome that obtains, and model is optimized; The measurement of concetration precision of the standard deviation RMSEC of employing coefficient R, calibration set sample and the model of standard deviation RMSEP of forecast set sample is assessed; Calibration model is distinguished called after Model1(0% from low to high according to salt or sugared concentration), Model2(mass concentration 5%), Model3(10%), Model4(15%), Model5(20%); The facies relationship number average of the calibration model under each concentration background shows that greater than 0.9200 estimated performance is better;
(6) foundation of overall calibration model: the near infrared spectrum to salt or sugared concentration and lotus rhizome internal component carries out correlation analysis and single argument regretional analysis, and the near infrared spectrum that draws salt or sugared concentration and lotus rhizome internal component is negative correlation; Predicted value increment △ C according to the calculating of the model under each salt or sugared concentration background lotus rhizome internal component sets up salt or sugared concentration C
SaltOr C
SugarWith the quantitative relationship of the simple regression of the predicted value increment △ C of lotus rhizome internal component, this increment of deduction from model predication value then, thus revise salt or sugared content to the interference of the near infrared Non-Destructive Testing of seasoning lotus rhizome internal component;
Wherein, the computing formula of △ C is:
Cei is the predicted value of i sample of experimental group; Cci is the predicted value of i sample of control group; N is the sample number under each concentration gradient background, and n is 20;
(7) foundation of near-infrared spectrum analysis model: at last on the basis of the calibration model of variable concentrations gradient, set up the near-infrared spectrum analysis model of the seasoning lotus rhizome internal component that adapts to salt or sugared concentration change;
(8) mensuration of testing sample internal component: will be updated to the predicted value that obtains the testing sample internal component in the near-infrared spectrum analysis model of step (7) through the near infrared spectrum of the pretreated testing sample of step (3), calculate the predicted value increment by the quantitative relation formula in the step (6), this increment of deduction namely obtains the predicted value revised from predicted value then.
Described near infrared spectrum is 4000～10000cm
-1Near infrared spectrum in the wavelength coverage is gathered the near infrared light spectrum information with the near-infrared diffuse reflectance technology, and resolution is 8cm
-1
Described lotus rhizome internal component is one or more in starch, robust fibre and the protein equal size, all adopts National Standard Method to obtain reference value.
Preferably, the near infrared light spectrum information of described collection has carried out pre-service, and described pre-service is polynary scatter correction, differentiate conversion, eliminate the baseline wander of spectroscopic data or the influence of mild background interference, and carry out smoothing processing, and eliminate random noise, improve signal to noise ratio (S/N ratio).
Preferably, the near infrared spectrum of described salt or sugared content and lotus rhizome internal component has carried out correlation analysis and single argument regretional analysis, has revised salt or sugared concentration change effectively to the interference of near infrared Non-Destructive Testing.
The spectroscopic data quantitative test function and the Matlab software that mate in the described near infrared spectrum model use TQ analyst spectral analysis software adopt partial least square method to set up mathematical model, and model obtains after optimizing repeatedly.
Described seasoning is sugaring or with the salt seasoning, its concentration range is 5%～20%.
Beneficial effect of the present invention: compared with prior art, the present invention has following advantage:
1, sample need not any processing, can realize that polycomponent measures simultaneously.
2, use existing near-infrared spectrometers device, spectroscopic data is through polynary scatter correction, differentiate and processing such as level and smooth, and carried out correlation analysis and single argument regretional analysis, revised salt or sugared concentration change effectively to the interference of near infrared Non-Destructive Testing.
3, the model of setting up is applicable to the near infrared Non-Destructive Testing of different salt or sugared concentration change.
Embodiment
For technological means, creation characteristic that the present invention is realized, reach purpose and effect is easy to understand, below in conjunction with specific embodiment, further set forth the present invention.Enforcement software and hardware of the present invention mainly contains parts such as near infrared spectrometer, stoichiometry software and computing machine.Whole implementation process is described as follows:
Embodiment 1 is with the method for the lotus rhizome internal component near infrared Non-Destructive Testing of salt seasoning
1, the preparation of sample sets.Gather not seasoning and 5%, 10%, the lotus rhizome sample of 15%, 20% salinity seasoning is chosen respectively with difference and is added the required calibration set of modeling and the forecast set that salinity is background, and wherein the quantitative proportion of calibration set and forecast set is about 4:1.With unforseen group in contrast, other are as experimental group.
2, the spectra collection of sample.Use near infrared spectroscopy instrument to carry out spectra collection to the above-mentioned seasoning lotus rhizome sample that obtains.Adopt the diffuse reflection absorption spectroscopy, test parameters is made as: sweep limit is 4000～10000cm
-1, resolution is 8cm
-1, scanning times is 16 times.During near-infrared spectral measurement, complete one section clean lotus rhizome is placed on the diffuse reflection probe reposefully, each sample need carry out spectral measurement 4 times, lay respectively at 4 relative positions at maximum gauge place, avoid tangible surface imperfection (scratch, scar etc.) as far as possible, spectrum in 4 measurements is averaged, make the averaged spectrum that obtains every section lotus rhizome.
3, the mensuration of sample reference value.After calibration set and forecast set sample carry out spectra collection with regard to as early as possible to its mensuration of carrying out the reference value of internal component, the result all represents with butt.Content of starch is measured with reference to GB/T5009.9-2008 " mensuration of starch in the food "-acid-hydrolysis method; Crude fiber content is measured with reference to GB/T5009.10-2003 " coarse-fibred mensuration in the plant food "; Protein content is with reference to GB/T5009.5-2010 " protein measuring in the food ".
4, the pre-service of spectroscopic data.Near infrared spectra collection and processing realize by TQ Analyst.For baseline wander or the influence of background interference gently that reduces spectroscopic data, eliminate random noise, improve signal to noise ratio (S/N ratio), when setting up model correlation parameter being adjusted and optimized is one of main means that improve model prediction ability and prediction effect.Pretreated method has that centralization, canonical variable conversion, additional scatter correction, orthogonal signal are proofreaied and correct, level and smooth, small echo denoising, differentiate variation and genetic algorithm Wavelength optimization etc.Adopt which kind of preprocess method or do not need pre-service to select according to the quality of spectrum and the concrete condition of background interference, when using the preprocess method of spectrum, can be the independent use of a certain method of said method, also can be being used in combination of above-mentioned several method.Calculate by repeatedly optimizing, obtain modeling optimization parameter such as the table 1 of different salinity seasoning lotus rhizome internal components.
The modeling optimization parameter of the different salinity seasoning of table 1 lotus rhizome internal component
5, the foundation of the calibration model of different gradients.In conjunction with the spectroscopic data quantitative test function of mating in the TQ analyst spectral analysis software and Matlab software, to the spectroscopic data of the sample of pretreated calibration set and forecast set with carry out said method and measure the reference value of the compositions such as starch, robust fibre and protein of the lotus rhizome that obtains and adopt partial least square method to set up forecast model, and model is optimized.Adopt related coefficient (R), the standard deviation (RMSEC) of calibration set sample and the standard deviation (RMSEP) of forecast set sample that the measurement of concetration precision of model is assessed.Calibration model is ordered respectively from low to high according to salinity and is Model1(0%), Model2(5%), Model3(10%), Model4(15%), Model5(20%), it the results are shown in Table 2.The facies relationship number average of the calibration model under each concentration background shows that greater than 0.9200 estimated performance is better.Wherein the RMSEP of Model2～5 is less than the RMSEP of Model1, and predictive ability and the precision of these explanation Model2～5 will be higher than Model1.This is because salt can not produce near ir absorption peaks, but can change the moisture of lotus rhizome to the interference of near infrared Non-Destructive Testing, thus the near infrared Non-Destructive Testing of remote effect lotus rhizome internal component.
Calibration model under the different salinity backgrounds of table 2
6, the foundation of overall calibration model.Near infrared spectrum to salinity and lotus rhizome internal component carries out correlation analysis and single argument regretional analysis, and the near infrared spectrum that draws salinity and lotus rhizome internal component is negative correlation.Predicted value increment △ C according to the calculating of the model under each salinity background lotus rhizome internal component the results are shown in Table 3.Set up salinity C
SaltWith the quantitative relationship (seeing Table 4) of the simple regression of the predicted value increment △ C of lotus rhizome internal component, this increment of deduction from model predication value then changes interference to the near infrared Non-Destructive Testing of seasoning lotus rhizome internal component thereby revise salt content.At last on the basis of the calibration model of variable concentrations gradient, set up the near-infrared spectrum analysis model (seeing Table 2) that adapts to the seasoning lotus rhizome internal component that salinity changes.
Wherein, the computing formula of △ C is:
Cei is the predicted value of i sample of experimental group; Cci is the predicted value of i sample of control group; N is the sample number under each gradient background, and n is 20;
The predicted value increment of lotus rhizome internal component under the different salinity of table 3
| ? | Starch concentration | Protein concentration | Robust fibre concentration |
| ? | Prediction increment △ C | Prediction increment △ C | Prediction increment △ C |
| Model1 | 0.0000 | 0.0000 | 0.0000 |
| Model2 | -0.1388 | -0.0255 | -0.0131 |
| Model3 | -0.2009 | -0.0351 | -0.0199 |
| Model4 | -0.3102 | -0.0503 | -0.0264 |
| Model5 | -0.3821 | -0.0628 | -0.0352 |
Table 4 salinity C
SaltQuantitative relation formula with the predicted value increment △ C of lotus rhizome internal component
7, the mensuration of testing sample internal component.To be updated to the predicted value that obtains the testing sample internal component in the near-infrared spectrum analysis model of having set up through the near infrared spectrum as the pretreated testing sample of step 3, calculate the predicted value increment by the quantitative relation formula in the step 6 (seeing Table 4), this increment of deduction namely obtains the predicted value revised from predicted value then.
The method of the lotus rhizome internal component near infrared Non-Destructive Testing of embodiment 2 sugaring seasonings
1, the preparation of sample sets.Gather not seasoning and 5%, 10%, the lotus rhizome sample of 15%, 20% sugared concentration seasoning, other are with embodiment 1.
2, the spectra collection of sample.With embodiment 1.
3, the mensuration of sample reference value.With embodiment 1.
4, the pre-service of spectroscopic data.With implementing 1.Modeling optimization parameter such as the table 5 of different sugar concentration seasoning lotus rhizome internal component.
The modeling optimization parameter of table 5 different sugar concentration seasoning lotus rhizome internal component
5, the foundation of the calibration model of different gradients.The modeling method that adopts the results are shown in Table 6 with embodiment 1.The facies relationship number average of the calibration model under each concentration background shows that greater than 0.9100 estimated performance is better.Wherein the RMSEP of Model2～5 is greater than the RMSEP of Model1, and predictive ability and the precision of these explanation Model2～5 will be lower than Model1.This is because the sugaring seasoning has made more than the lotus rhizome a kind of component that near ir absorption peaks is arranged, thereby influences the near infrared Non-Destructive Testing of lotus rhizome internal component.
Calibration model under the table 6 different sugar concentration background
6, the foundation of overall calibration model.Near infrared spectrum to sugared concentration and lotus rhizome internal component carries out correlation analysis and single argument regretional analysis, draws the being proportionate property of near infrared spectrum of sugared concentration and lotus rhizome internal component.The calculating of predicted value increment △ C the results are shown in Table 7 with embodiment 1.Set up sugared concentration C
SugarWith the quantitative relationship (seeing Table 8) of the predicted value increment △ C of each composition, this increment of deduction from model predication value then, thus revise sugared content to the interference of the near infrared Non-Destructive Testing of seasoning lotus rhizome internal component.At last on the basis of the calibration model of variable concentrations gradient, set up the near-infrared spectrum analysis model (seeing Table 6) of the seasoning lotus rhizome internal component that adapts to sugared concentration change.
The predicted value increment of lotus rhizome internal component under the table 7 different sugar concentration
The sugared concentration C of table 8
SugarQuantitative relation formula with the predicted value increment △ C of lotus rhizome internal component
7, the mensuration of testing sample internal component.With embodiment 1.
Claims (1)
1. the method for the lotus rhizome internal component near infrared Non-Destructive Testing of an anti-seasoning interference is characterized in that gathering the lotus rhizome sample sets through different salinity or sugared concentration seasoning; The physicochemical data reference value of the near infrared light spectrum information of collected specimens and internal component then; The spectroscopic data pre-service; Back employing partial least square method is set up the near infrared spectrum calibration model under its variable concentrations gradient background; The near infrared spectrum of salt or sugared content and lotus rhizome internal component is carried out correlation analysis and overall calibration model is set up in the single argument regretional analysis; Revise salt or sugared concentration change to the interference of the near infrared Non-Destructive Testing of lotus rhizome internal component, last near infrared spectrum model based under variable concentrations gradient background is set up the near-infrared spectrum analysis model of the seasoning lotus rhizome internal component that adapts to salinity or sugared concentration change; Be the parameter of seasoning lotus rhizome internal component to be measured by this model with the near infrared spectrum information translation of testing sample, realize the Non-Destructive Testing of seasoning lotus rhizome internal component; Step is:
(1) preparation of sample sets: gather not seasoning and 5%, the lotus rhizome sample of 10%, 15%, 20% salinity or sugared concentration seasoning, choosing with difference respectively is the required calibration set of modeling and the forecast set of background with salt or sugaring concentration, and wherein the quantitative proportion of calibration set and forecast set is 4:1; With unforseen in contrast the group, seasoning as experimental group;
(2) near infrared spectra collection of sample: the seasoning lotus rhizome sample that step (1) is obtained uses near infrared spectroscopy instrument to carry out spectra collection, adopts the diffuse reflection absorption spectroscopy, and test parameters is made as: sweep limit is 4000～10000cm
-1, resolution is 8cm
-1, scanning times is 16 times; During near-infrared spectral measurement, complete one section clean lotus rhizome is placed on the diffuse reflection probe reposefully, each sample need carry out spectral measurement 4 times, lay respectively at 4 relative positions at maximum gauge place, avoid tangible scratch, scar class surface imperfection as far as possible, spectrum in 4 measurements is averaged, obtain the averaged spectrum of every section lotus rhizome;
(3) mensuration of sample interior composition physicochemical data reference value: after calibration set and forecast set sample carried out spectra collection, to its mensuration of carrying out the reference value of internal component, the result all represented with butt as early as possible; Content of starch is measured with reference to GB/T5009.9-2008 " mensuration of starch in the food "-acid-hydrolysis method; Crude fiber content is measured with reference to GB/T5009.10-2003 " coarse-fibred mensuration in the plant food "; Protein content is with reference to GB/T5009.5-2010 " protein measuring in the food ";
(4) pre-service of spectroscopic data: near infrared spectra collection and handle by TQ Analyst and realize, pretreated method have that centralization, canonical variable conversion, additional scatter correction, orthogonal signal are proofreaied and correct, level and smooth, small echo denoising, differentiate variation and genetic algorithm Wavelength optimization; Adopt which kind of preprocess method or do not need pre-service to select according to the quality of spectrum and the concrete condition of background interference, when using the preprocess method of spectroscopic data, be the independent use of a certain method of said method, or being used in combination of above-mentioned several method; Calculate by repeatedly optimizing, obtain the modeling optimization parameter of different salt or sugared concentration seasoning lotus rhizome internal component;
(5) foundation of the near infrared spectrum calibration model of variable concentrations gradient: in conjunction with the spectroscopic data quantitative test function of mating in the TQ analyst spectral analysis software and Matlab software, adopt partial least square method to set up forecast model to the spectroscopic data of the sample of pretreated calibration set and forecast set and the reference value of measuring starch, robust fibre and the protein component of the lotus rhizome that obtains, and model is optimized; The measurement of concetration precision of the standard deviation RMSEC of employing coefficient R, calibration set sample and the model of standard deviation RMSEP of forecast set sample is assessed; Calibration model is named respectively from low to high according to salt or sugared concentration: 0% is Model1, and 5% is Model2, and 10% is Model3, and 15% is Model4, and 20% is Model5; The facies relationship number average of the calibration model under each concentration background shows that greater than 0.920 estimated performance is better;
(6) foundation of overall calibration model: the near infrared spectrum to salt or sugared concentration and lotus rhizome internal component carries out correlation analysis and single argument regretional analysis, and the near infrared spectrum that draws salt or sugared concentration and lotus rhizome internal component is negative correlation; Predicted value increment △ C according to the calculating of the model under each salt or sugared concentration background lotus rhizome internal component sets up salt or sugared concentration C
SaltOr C
SugarWith the quantitative relationship of the simple regression of the predicted value increment △ C of lotus rhizome internal component, this increment of deduction from model predication value then, thus revise salt or sugared content to the interference of the near infrared Non-Destructive Testing of seasoning lotus rhizome internal component;
Wherein, the computing formula of △ C is:
Cei is the predicted value of i sample of experimental group; Cci is the predicted value of i sample of control group; N is the sample number under each concentration gradient background, and n is 20;
(7) foundation of near-infrared spectrum analysis model: at last on the basis of the calibration model of variable concentrations gradient, set up the near-infrared spectrum analysis model of the seasoning lotus rhizome internal component that adapts to salt or sugared concentration change;
(8) mensuration of testing sample internal component: will be updated to the predicted value that obtains the testing sample internal component in the near-infrared spectrum analysis model of step (7) through the near infrared spectrum of the pretreated testing sample of step (3), calculate the predicted value increment by the quantitative relation formula in the step (6), this increment of deduction namely obtains the predicted value revised from predicted value then.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105092523A (en) * | 2015-08-25 | 2015-11-25 | 江南大学 | Scheme based on near infrared technology and used for detecting phosphorous in water |
| CN105699301A (en) * | 2015-12-31 | 2016-06-22 | 深圳市芭田生态工程股份有限公司 | Method for determining coarse cellulose in agricultural product by means of spectral method |
| CN106164646A (en) * | 2014-03-28 | 2016-11-23 | 江原大学校产学协力团 | Utilize near-infrared spectroscopy to analyze the method for numerous food Middle nutrition component content of different material and form simultaneously |
| WO2020152380A1 (en) | 2019-01-22 | 2020-07-30 | Universidad De Sevilla | Portable device and method for non-invasive blood glucose level estimation |
| CN113310943A (en) * | 2021-05-22 | 2021-08-27 | 福州大学 | Lotus root starch adulteration identification method based on machine learning |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109856072A (en) * | 2019-03-13 | 2019-06-07 | 西北农林科技大学 | Kiwi berry based on Vis/NIR expands fruit detection method and device |
| CN117368146B (en) * | 2023-12-08 | 2024-03-12 | 苏陀科技(北京)有限公司 | Rapid detection method for mycelium protein content |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102279168A (en) * | 2011-07-20 | 2011-12-14 | 浙江大学 | Near-infrared spectroscopic technology-based method for fast and undamaged analysis of nutritional quality of whole cottonseed |
| EP2418474A1 (en) * | 2010-08-02 | 2012-02-15 | OOO "Novye Energeticheskie Technologii" | Near infrared spectroscopy method for simultaneously determining the components of raw milk |
| CN102353644A (en) * | 2011-06-30 | 2012-02-15 | 上海海洋大学 | Rapid near infrared spectroscopy method for simultaneously detecting moisture content and protein content of Trichiurus japonicus surimi |
| US20130080070A1 (en) * | 2011-09-23 | 2013-03-28 | Dow Agrosciences Llc | Chemometrics for near infrared spectral analysis |
-
2013
- 2013-05-06 CN CN201310167960.2A patent/CN103344597B/en active Active
-
2014
- 2014-05-03 CH CH00658/14A patent/CH708057B1/en not_active IP Right Cessation
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2418474A1 (en) * | 2010-08-02 | 2012-02-15 | OOO "Novye Energeticheskie Technologii" | Near infrared spectroscopy method for simultaneously determining the components of raw milk |
| CN102353644A (en) * | 2011-06-30 | 2012-02-15 | 上海海洋大学 | Rapid near infrared spectroscopy method for simultaneously detecting moisture content and protein content of Trichiurus japonicus surimi |
| CN102279168A (en) * | 2011-07-20 | 2011-12-14 | 浙江大学 | Near-infrared spectroscopic technology-based method for fast and undamaged analysis of nutritional quality of whole cottonseed |
| US20130080070A1 (en) * | 2011-09-23 | 2013-03-28 | Dow Agrosciences Llc | Chemometrics for near infrared spectral analysis |
Non-Patent Citations (5)
| Title |
|---|
| NIU XIAOYING ET AL: "A feasibility study on quantitative analysis of glucose and fructose in lotus root powder by FT-NIR spectroscopy and chemometrics", 《FOOD CHEMISTRY》 * |
| 于海燕等: "农产品品质近红外光谱分析结果影响因素研究综述", 《农业工程学报》 * |
| 李勇等: "影响近红外光谱分析结果准确性的因素", 《核农学报》 * |
| 李勇等: "近红外水分稳健分析模型研究", 《光谱学与光谱分析》 * |
| 苏东林等: "近红外光谱分析技术在我国大宗水果品质无损检测中的应用研究进展", 《食品工业科技》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106164646A (en) * | 2014-03-28 | 2016-11-23 | 江原大学校产学协力团 | Utilize near-infrared spectroscopy to analyze the method for numerous food Middle nutrition component content of different material and form simultaneously |
| CN106164646B (en) * | 2014-03-28 | 2019-01-04 | 江原大学校产学协力团 | Analyze the method for nutrient composition content in the numerous food of different material and form simultaneously using near-infrared spectroscopy |
| CN105092523A (en) * | 2015-08-25 | 2015-11-25 | 江南大学 | Scheme based on near infrared technology and used for detecting phosphorous in water |
| CN105699301A (en) * | 2015-12-31 | 2016-06-22 | 深圳市芭田生态工程股份有限公司 | Method for determining coarse cellulose in agricultural product by means of spectral method |
| CN105699301B (en) * | 2015-12-31 | 2019-05-28 | 深圳市芭田生态工程股份有限公司 | A method of utilizing crude fibre in spectrographic determination agricultural product |
| WO2020152380A1 (en) | 2019-01-22 | 2020-07-30 | Universidad De Sevilla | Portable device and method for non-invasive blood glucose level estimation |
| CN113310943A (en) * | 2021-05-22 | 2021-08-27 | 福州大学 | Lotus root starch adulteration identification method based on machine learning |
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