CN105044024A - Method for nondestructive testing of grape berries based on near infrared spectrum technology - Google Patents
Method for nondestructive testing of grape berries based on near infrared spectrum technology Download PDFInfo
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Abstract
The invention discloses a method for nondestructive testing of grape berries based on the near infrared spectrum technology, and particularly relates to a method for identifying table grape berries of different varieties and maturity degrees and under different insect attack conditions based on the near infrared spectrum technology. The spectral preprocessing method and the chemometrics modeling method are combined to build a quantitative analysis prediction model with grape inclusions as the test index on grape berries of different varieties and maturity degrees and under different insect attack conditions. The near infrared spectrum technology is adopted to identity the interior quality of different grape samples for test. The method has the advantages of being high in testing speed, high in efficiency and low in cost, is suitable for nondestructive testing of quality and safety of agricultural products, and can be used for effectively identifying grape berries of different varieties and maturity degrees and under different insect attack conditions. A series of studies suggest that the combination of the near infrared spectrum technology and the chemometrics method has great potential and development prospects in the fields of quick prediction of inclusions of grapes and variety identification.
Description
Technical field
The present invention relates to a kind of near-infrared spectrum technique that utilizes to the fast non-destructive detection method of Table Grape different cultivars, degree of ripeness and insect pest fruit.
Background technology
Traditional fruit quality is differentiated and stage division is wasted time and energy, and is vulnerable to the interference of human factor (as sense of smell, the sense of taste, hobby), and lacks discrimination standard that is objective and rationality to the nutritional labeling of fruit inside.And chemical analysis detects after needing to carry out fragmentation to fruit one by one, the limited sample size often detected, is difficult to the representativeness ensureing sampling, thus cannot realizes Fast nondestructive evaluation.The detection of rear streamline adopted by some existing fruits and stage equipment mostly just carries out rough classification according to the index such as fruit size or weight, and the specificity of equipment requires higher, and service efficiency is low.Therefore, develop a kind of quick, efficient, harmless fruit detection technique, to meet the needs of the extensive attributional analysis of fruit and classification process, improving the standardization of fruit postharvest handling, is problem demanding prompt solution in current Fruits production.
In recent years, near infrared light spectral imaging technology attracts wide attention as a kind of lossless detection method, because it has many-sided advantages such as detection speed is fast, efficiency is high, cost is low, be applied to the relevant industries fields such as medicine, chemical industry, agricultural, food more and more.Range of application mainly comprises the deciding grade and level price etc. of the content of related chemical constituents in quantitative testing product, the qualification of product category and differentiation and product.At present, due to the inhomogeneity of fruit inclusions distribution, and the diversity that spectral analysis data disposal route causes model to set up, require that we optimize further to the fruit fast non-destructive detection method based on near-infrared spectrum technique analysis.
Summary of the invention
The object of the invention is to provide a kind of near-infrared spectrum technique that utilizes to the fast non-destructive detection method of Table Grape different cultivars, degree of ripeness and insect pest fruit, can only carry out analyzing the problem detected to homogeneous material such as such as tablet, liquid etc. to solve art methods, expand this technology range of application; Solve art methods need to detected object carry out pre-service (as tealeaves need pulverize, fruit will squeeze the juice) technical matters, simplify technical Analysis step; Solve art methods and only single the or minority index of quality can carry out the problem of analytical model foundation to fruit, improve the comprehensive of quality evaluation; Solve the problem that in art methods, spectral analysis data disposal route causes analytical model poor for applicability, improve identification effect and the stability of analytical model; To solve in art methods and can only carry out the problem of qualitative analysis for the variety classes fruit very large to difference; Solve in art methods, the characteristic wavelength site chosen is few, narrow range, is difficult to the problem containing more spectral information.
Concrete, the present invention relates to a kind of method of based on near-infrared spectrum technique, grape fruit being carried out to Non-Destructive Testing, it is characterized in that, comprise the steps:
(1) material prepares, and choose different cultivars grape fruit, each kind chooses at least 10 sample composition sample sets;
(2) choose modeling sample, according to the ratio of 3:1, each sample set is divided at random modeling collection and checking collection;
(3) the original near infrared spectrum of all modeling collection and checking collection sample is gathered;
(4) inclusions desired value in chemical analysis working sample is utilized;
(5) pre-service is carried out to the near infrared spectrum collected in step (3);
(6) in TQAnalyst9 software, Suggest navigational aids is used automatically to select suitable wave-number range;
(7) near infrared spectra quantitative models is set up, wave-number range step (6) obtained is as modeling wave-number range, inclusions desired value in step (4), as standard value, utilizes step (5) pretreated near infrared spectrum to set up Near-Infrared Quantitative Analysis model;
(8) inspection of Near-Infrared Quantitative Analysis model accuracy, respectively the near infrared spectrum inclusions desired value actual with it of checking collection sample is substituted into Near-Infrared Quantitative Analysis model, carry out the inspection of Near-Infrared Quantitative Analysis model accuracy, according to predictor calculation prediction standard deviation RMSEP, if its value meets requirement of experiment, then representative model is feasible, otherwise, repeat step (5)-(7), until meet;
(9) the inclusions desired value of Near-Infrared Quantitative Analysis model determination testing sample is utilized.
In the embodiment that the present invention one is concrete, described detection method, the grape variety wherein in step (1) is selected from golden finger, than high button, liquor-saturated gold is fragrant, rattan is harvested, huge peak, Wen Ke, the summer black, huge rose, sub-, the Fuji apple in capital.
In the embodiment that the present invention one is concrete, described detection method, step (1) comprise select prematurity, colour-change period, period in maturity stage three fruit and damaged by vermin grape fruit composition sample sets.
In the embodiment that the present invention one is concrete, described detection method, wherein in step (3), the concrete grammar of near infrared spectra collection is: adopt Antaris II near infrared spectrometer that ThermoFisher company produces, due to the small volume of grape, be equipped with precision positioning annex, adopt high sensitivity InGaAs detecting device, be placed to after room temperature until sample, each sample equatorial line is evenly chosen in 4 sample area and carry out spectra collection, each scanning, spectrum repeats 32 times automatically, averages.
In the embodiment that the present invention one is concrete, described detection method, wherein the inclusions index described in step (4) is soluble solid index, pol index, total phenol index; Wherein pol index comprises total reducing sugar, fructose, sucrose index.
In the embodiment that the present invention one is concrete, described detection method, wherein the assay method of soluble solid index is after grape fruit peeling and seed, takes after pulp centre squeezes juice, adopts refractometer mensuration soluble solid content.
In the embodiment that the present invention one is concrete, described detection method, wherein the detection of pol index comprises: each kind gets 2 ~ 3 grapes, peeling and seed, adds liquid nitrogen and be ground to powder after chopping stirs; Get 2g powder to add 80% ethanol and be settled to 20mL, under room temperature condition, leave standstill lixiviate 30min; Therefrom get 1mL suspension, be settled to 50mL with 80% ethanol.Total reducing sugar index adopts sulfuric acid anthrone method to measure, and fructose and sucrose index adopt Resorcinol Method to measure.
In the embodiment that the present invention one is concrete, described detection method, wherein the assay method of total phenol index is Forint phenol method (GB/T8313-2008).
In the embodiment that the present invention one is concrete, described detection method, wherein uses chemo metric software to carry out multicomponent signal correction (MSC), the process of first order derivative differential and Norris smoothing processing successively to all spectrum in step (5).
In a specific embodiment of the present invention, between step (7) and step (8), also comprise the step adopting stechiometry to correct set up model; Preferably, PLS method is selected.
PLS method is a kind of multivariate calibration methods based on factorial analysis, for obtaining best major component because of subnumber, sets up optimum prediction model.The method has the very strong ability providing information, and the calibration model set up is more stable, has stronger antijamming capability.PLS method has now become multivariate statistical method the most frequently used in Chemical Measurement.
In the embodiment that the present invention one is concrete, described detection method, wherein in step (6), wave-number range is 9842.89 ~ 8018.56cm
-1with 8014.71 ~ 4088.35cm
-1.
In a concrete embodiment of the present invention, described detection method, wherein step (9) comprising:
Steps A, near infrared spectrum universal model is imported in corresponding near infrared spectrometer;
Step B, utilize this near infrared spectrometer collection to remain the original near infrared spectrum of all testing samples, the original near infrared spectrum obtained then can be input in model by instrument, draws the main inclusions desired value of sample, until all samples measures complete.
In the embodiment that the present invention one is concrete, described detection method, wherein also comprises and utilizes the method for qualitative analysis to carry out taxonomic identification to the near infrared spectrum of each sample set, sets up the qualitative classification model of each sample set.
In the embodiment that the present invention one is concrete, be included in each sample set and choose 9 ~ 21 samples, gather its original near infrared spectrum, use chemo metric software (as TQ9.0 software) to set up the DA qualitative classification model of different grape variety, degree of ripeness, insect pest; Adopt this software to carry out principal component analysis (PCA) to standard spectrum selected before, according to the principal component analysis (PCA) result of standard spectrum, calculate the mahalanobis distance of each sample spectra to each standard spectrum, taxonomic identification is carried out to each grape sample.
Described detection method, uses chemo metric software (as TQ9.0 software), sets up the near infrared qualitative classification model of different grape variety, degree of ripeness, insect pest; In each sample set, choose 9 ~ 21 samples, gather its original near infrared spectrum, use chemo metric software such as TQ9.0 software to set up the DA qualitative classification model of different grape variety, degree of ripeness, insect pest.This software carries out principal component analysis (PCA) to standard spectrum selected before, according to the principal component analysis (PCA) result of standard spectrum, calculates the mahalanobis distance of each sample spectra to each standard spectrum, carries out taxonomic identification to each grape sample.
Particularly, in the present invention, grape near infrared qualitative classification qualification, comprises the steps:
(1) material prepares, and choose different cultivars grape fruit, each kind chooses at least 10 sample composition sample sets;
(2) choose modeling sample, according to the ratio of 3:1, each sample set is divided at random modeling collection and checking collection;
(3) gather the original near infrared spectrum of all modeling collection and checking collection sample and calculate standard spectrum;
(4) chemo metric software such as TQ9.0 software is used to set up the DA qualitative analysis model of different grape variety and degree of ripeness and disease and pest; This software carries out principal component analysis (PCA) to standard spectrum selected before, according to the principal component analysis (PCA) result of standard spectrum, calculates the mahalanobis distance of each sample spectra to each standard spectrum, carries out taxonomic identification to each grape sample.
Preferably, the grape variety in step (1) is selected from golden finger, than high button, liquor-saturated gold is fragrant, rattan is harvested, huge peak, Wen Ke, the summer black, huge rose, sub-, the Fuji apple in capital; Preferably can select in step (1) prematurity, colour-change period, period in maturity stage three fruit and damaged by vermin fruit composition sample sets.
Plays spectrum of the present invention is the averaged spectrum of all samples in each sample sets.In the present invention, after gathering original spectrum, calculate standard spectrum.
Compared with prior art, technical advantage of the present invention is:
1, disposable modeling, reduction model are set up and maintenance cost
The present invention establishes qualitative, the quantitative model of near infrared being applicable to the main inclusion content of grape and detecting, and use series of preprocessing method in modeling process, disposable foundation can identify the qualutative model of multi items, differing maturity, insect pest grape fruit; Disposable foundation can detect the near infrared quantitative model of the main inclusion content of multi items grape, there is extremely strong feasibility, multi items, degree of ripeness, the differentiation of insect pest grape and qualification can be completed with a qualutative model, the mensuration of the main inclusion content of grape can be completed with a quantitative model, greatly reduce modeling cost and model maintenance cost.
2, accuracy rate is measured high
The present invention have selected specific wave-number range, and make the testing result accuracy rate of whole detection method high, analytical effect is good.
3, Model Practical is strong
In modeling process, use first order derivative to eliminate the impact of sample integral time, model can detect with different integral time when reality uses; Greatly reduce the complexity of model, substantially increase the practicality of model, meet practical application request, near infrared universal model can apply on easy nir instrument.
Accompanying drawing explanation
Figure 1A-1B shows the characteristic wavelength scope that " summer is black " grape fruit mixing index model is set up
Fig. 2 A-2B shows: in " summer is black " grape fruit mixing index model, soluble solid predicts the outcome
Fig. 3 A-3D shows total phenol in " summer is black " grape fruit mixing index model, total reducing sugar, fructose, the predicting the outcome of sucrose
Embodiment
Below in conjunction with concrete embodiment, the invention will be further described.
Embodiment 1
(1) quantitative test
For " summer is black " grape fruit mixing index model, Figure 1A-1B is the characteristic wavelength of " summer is black " grape fruit sample set, and as can be seen from Figure 1A-1B, curve of spectrum two ends noise is larger, assist the spectral range automatically chosen to have two sections by TQ software, be respectively 9842.89 ~ 8018.56cm
-1(Figure 1A) He 8014.71 ~ 4088.35cm
-1(Figure 1B).By the model tuning effect analysis to conventional preprocessing procedures, choosing multiplicative scatter correction (MSC)+first order derivative (1stderivative)+Norris is smoothly the best pretreatment mode of " summer is black " grape fruit model, and the evaluation of modeling result comprises related coefficient, root-mean-square error, absolute error three part.
Related coefficient is divided into modeling collection (Calibration) related coefficient and checking collection (Validation) related coefficient.Modeling collection related coefficient investigates the model predication value of modeling collection sample and the correlativity of reference value.Related coefficient higher (numerical value is more close to 1), then correlativity is higher.Checking collection related coefficient investigates the checking collection model predication value of sample and the correlativity of reference value, is one of important parameter of reflection model prediction performance; But checking collection does not participate in modeling, only participates in verification model.Modeling collection error mean square root (RMSEC) represents predicting the outcome of modeling collection sample and the root mean square (RMSE) of difference between actual result, and for the statistics of predicated error shows, result is more better close to 0.Checking collection error mean square root (RMSEP) represents predicting the outcome and the root mean square of difference between actual result of checking collection sample, and for the statistics of predicated error show, result is more better close to 0, is one of important parameter reflecting model prediction performance.
What Fig. 2 A-2B showed is the predicting the outcome of soluble solid index in " summer is black " grape fruit mixing index model, what Fig. 2 A showed is related coefficient and error mean square root, Fig. 2 B is absolute error figure, and total phenol, total reducing sugar, fructose, sucrose index related coefficient are as shown in figs. 3 a-3d.Related coefficient between the measured value of the total phenol of " summer is black " grape fruit, total reducing sugar, fructose, sucrose and soluble solid and NIRS predicted value and error mean square root as shown in table 1.Concentrate in modeling, the related coefficient of total phenol is lower, is 0.77, and all the other are all more than 0.92, and modeling collection root-mean-square error (RMSEC) is between 0.022 ~ 0.265, and characterization model predicts the outcome well; Concentrate in checking, the related coefficient of sucrose and total reducing sugar is lower, is respectively 0.78 and 0.82, and all the other are all more than 0.91, and checking collection root-mean-square error (RMSEP) is between 0.081 ~ 0.981, and model aggregate level is high; Although the related coefficient of some index is lower, because mixture model applicability is wide, so this result can meet the needs of on-line checkingi.
The PLS mixing index modeling statistics result of the total phenol of table 1 " summer is black " grape fruit, total amount, fructose, sucrose, soluble solid
The optimum prediction model of the total phenol of foundation " Wen Ke " grape fruit, total reducing sugar, fructose, sucrose and soluble solid 5 indexs of using the same method, its preprocess method is that multiplicative scatter correction (MSC)+first order derivative (1stderivative)+Norris is level and smooth.Each component predicts the outcome as shown in table 2, and model prediction result is good.
The PLS mixing index modeling statistics result of the total phenol of table 2 " Wen Ke " grape fruit, total amount, fructose, sucrose, soluble solid
After TQ model is built up, namely working model carries out quantitative test to unknown sample, prediction single sample is for " Wen Ke " grape fruit, " Wen Ke " grape fruit spectrum of mode to random selecting adopting external certificate is unknown sample, is predicted by the mixture model that unknown sample spectrum imports " Wen Ke " grape fruit five component contents.Predict the outcome in table 3.Obtain the total phenol of " Wen Ke " grape fruit, fructose, sucrose, soluble solid, the predicted value of total reducing sugar five component contents and error amount, predict the outcome ideal.
The component content prediction result of table 3 " Wen Ke " grape fruit unknown sample
(2) qualitative analysis
In order to differentiate different grape variety, utilize discriminatory analysis (DA) method in TQAnalyst9 software in qualitative analysis, to the summer black, huge rose, liquor-saturated gold is fragrant, rattan is harvested, amount to 114 grape samples set up qualitative variety ecotype model than high button, Asia, capital, Fuji apple, golden finger, Ju Feng, warm gram 10 different cultivars.
Model, through repeatedly optimizing, obtains optimum efficiency.Evaluating is as follows: the wave-number range that TQAnalyst9 software is chosen automatically is 4119.20 ~ 9881.46cm
-1, with SNV+ first order derivative+Norris smoothly for the best preprocess method of modeling sets up sizing analytical model, the correct recognition rata of model is 92.11%.
Claims (10)
1. based on near-infrared spectrum technique, grape fruit is carried out to a method for Non-Destructive Testing, it is characterized in that, comprise the steps:
(1) material prepares, and choose different cultivars grape fruit, each kind chooses at least 10 sample composition sample sets;
(2) choose modeling sample, according to the ratio of 3:1, each sample set is divided at random modeling collection and checking collection;
(3) the near infrared spectrum raw data of all modeling collection and checking collection sample is gathered;
(4) inclusions desired value in chemical analysis working sample is utilized;
(5) pre-service is carried out to the near infrared spectrum collected in step (3);
(6) in TQAnalyst9 software, Suggest navigational aids is used automatically to select suitable wave-number range;
(7) near infrared spectra quantitative models is set up, wave-number range step (6) obtained is as modeling wave-number range, inclusions desired value in step (4), as standard value, utilizes step (5) pretreated near infrared spectrum to set up Near-Infrared Quantitative Analysis model;
(8) inspection of Near-Infrared Quantitative Analysis model accuracy, substitutes near infrared quantitative model by the near infrared spectrum of checking collection sample and actual inclusions desired value thereof respectively, tests to the accuracy of near infrared quantitative model; According to predictor calculation prediction standard deviation (RMSEP), if its value meets requirement of experiment, then representative model is feasible, otherwise, repeat step (5)-(7), until meet;
(9) the inclusions desired value of Near-Infrared Quantitative Analysis model determination testing sample is utilized.
2. detection method according to claim 1, the grape variety wherein in step (1) is selected from golden finger, than high button, liquor-saturated gold is fragrant, rattan is harvested, huge peak, Wen Ke, the summer black, huge rose, sub-, the Fuji apple in capital.
3. detection method according to claim 1, step (1) comprise select prematurity, colour-change period, period in maturity stage three fruit and damaged by vermin fruit composition sample sets.
4. detection method according to claim 1, wherein in step (3), the concrete grammar of near infrared spectra collection is: adopt Antaris II near infrared spectrometer that ThermoFisher company produces, due to the small volume of grape, be equipped with precision positioning annex, adopt high sensitivity InGaAs detecting device, be placed to after room temperature until sample, each sample equatorial line is chosen in equally distributed 4 sample area and carry out spectra collection, each scanning, spectrum repeats 32 times automatically, averages.
5. detection method according to claim 1, wherein the inclusions index described in step (4) is soluble solid index, pol index, total phenol index; Wherein pol index comprises total reducing sugar, fructose, sucrose index.
6. detection method according to claim 4, wherein the assay method of soluble solid index is after grape fruit peeling and seed, gets grape berry and squeezes juice, adopts refractometer to measure the content of soluble solid.
7. detection method according to claim 1, wherein uses chemo metric software to carry out multicomponent signal correction (MSC), the process of first order derivative differential and Norris smoothing processing successively to all spectrum in step (5).
8. detection method according to claim 1, wherein in step (6), wave-number range is 9842.89 ~ 8018.56cm
-1, and 8014.71 ~ 4088.35cm
-1.
9. detection method according to claim 1, wherein also comprises and utilizes method for qualitative analysis to carry out taxonomic identification to the near infrared spectrum of each sample set, sets up the step of each sample set qualitative classification model.
10. detection method according to claim 9, be included in each sample set and choose 9 ~ 21 samples, gather its original near infrared spectrum, use chemo metric software (as TQ9.0 software) to set up the DA qualitative classification model of different grape variety, degree of ripeness, insect pest; Adopt this software to carry out principal component analysis (PCA) to standard spectrum selected before, according to the principal component analysis (PCA) result of standard spectrum, calculate the mahalanobis distance of each sample spectra to each standard spectrum, taxonomic identification is carried out to each grape sample.
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| CN117074353A (en) * | 2023-08-18 | 2023-11-17 | 广东省农业科学院设施农业研究所 | Nondestructive detection method and system for litchi fruit Di-moths |
| CN117074353B (en) * | 2023-08-18 | 2024-05-03 | 广东省农业科学院设施农业研究所 | Nondestructive detection method and system for litchi fruit Di-moths |
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