CN111795943A - Method for nondestructive detection of exogenous doped sucrose in tea based on near infrared spectrum technology - Google Patents
Method for nondestructive detection of exogenous doped sucrose in tea based on near infrared spectrum technology Download PDFInfo
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Abstract
The invention relates to the technical field of tea quality detection, in particular to a method for nondestructive detection of exogenous sucrose in tea based on a near infrared spectrum technology. The invention aims to solve the problems of difficult manual discrimination, time and labor waste in physicochemical detection and the like of the sugared black tea, realize the nondestructive, rapid and accurate identification and quantitative detection of the sugar content of the sugared black tea, and provide a theoretical method and a scientific basis for the qualitative and quantitative detection of the exogenous sucrose in the black tea.
Description
Technical Field
The invention relates to the technical field of tea quality detection, in particular to a method for nondestructive detection of exogenous doped sucrose in tea based on a near infrared spectrum technology.
Background
In recent years, due to the preference of consumers on black tea, the black tea industry in China continues to keep a steady growth situation, the yield of the black tea is gradually increased, and according to the data of the national statistical bureau, the yield of the black tea in 2018 reaches 23.33 ten thousand tons, and is increased by 251.9% compared with 2008. With the rise of the black tea industry, the variety of the black tea is gradually diversified, the high-end black tea gradually moves to the visual field of people, and the sales volume is increased year by year. At present, a small part of high-grade black tea in the market is famous and unsound because some illegal vendors add exogenous sucrose in black tea processing so as to achieve the effect of the high-grade black tea, and the sucrose added in the black tea processing can influence the sensory quality judgment of finished tea in aspects of aroma, color and the like, so that consumers are difficult to distinguish, the order of the black tea market is seriously influenced, and the benefits of tea farmers are harmed; in addition, the sucrose has the characteristics of easy moisture absorption, easy deterioration, easy bacterial breeding and the like, so that the quality safety of the black tea is greatly influenced.
At present, the method for detecting the content of exogenous sucrose in black tea at home and abroad mainly uses chemical reagents, for example, Chinese invention patent with publication number CN107589081A and patent name "an anti-interference rapid detection method for exogenous sucrose doped in tea", and utilizes a cross-linked polyvinylpyrrolidone-activated carbon combined adsorbent to remove the interference of matrix components in a tea sample extracting solution on the color development reaction and detection of resorcinol, thereby realizing the qualitative and quantitative rapid determination of the exogenous sucrose in the tea processing process. Although the method is high in precision, the method needs related instruments, has certain scientific research literacy, needs to extract or brew tea leaves, is time-consuming and labor-consuming, is high in economic cost, and is not suitable for wide popularization for consumers and tea makers. Therefore, it is very important to research a nondestructive fast method for the exogenous sucrose in the black tea.
As a novel nondestructive detection technology, the near infrared spectrum technology utilizes the absorption of hydrogen-containing groups in substances to near infrared light with the wavelength range of 780-2526 nm due to electron transition, and because the content of the hydrogen-containing groups in the components of agricultural products is high, the near infrared spectrum has a good effect on the detection of the agricultural products, is widely applied in the fields of component content prediction, classification and identification, rot identification, real-time monitoring and the like in recent years, and is developed and matured gradually. The near infrared spectrum technology is also subjected to related research in the processing fields of tea withering, fermentation and the like, and some tea enterprises (such as small-pot tea) also establish intelligent production lines based on near infrared detectors, so that a good effect is achieved, but qualitative and quantitative detection of exogenous sucrose in black tea is not reported.
Disclosure of Invention
In view of the above, the invention aims to provide a method for nondestructive detection of exogenous sucrose in tea based on a near infrared spectrum technology, and aims to solve the problems of difficult manual identification, time and labor waste in physicochemical detection and the like of sugared black tea, realize nondestructive, rapid and accurate identification and quantitative detection of sugar content of sugared black tea, and provide theoretical methods and scientific bases for qualitative and quantitative detection of exogenous sucrose in black tea.
The invention solves the technical problems by the following technical means:
the method comprises the steps of performing near infrared spectrum scanning on black tea samples containing different exogenous sucrose amounts, performing standard normal variable transformation algorithm processing on original spectrum data obtained by scanning, performing variable screening by adopting a continuous projection algorithm, performing principal component analysis, establishing a detection model by using optimal principal components, inputting near infrared spectrum data of the black tea samples to be detected into the detection model, and realizing discrimination and content detection of the exogenous sucrose in the black tea.
Further, the method comprises the steps of:
s1, preparing and obtaining black tea samples with different sugar contents;
s2, performing near infrared spectrum scanning on the black tea sample by using a near infrared spectrum analyzer, and acquiring original spectrum data of the black tea sample;
s3, randomly dividing an original spectrum data set into a training set sample and a prediction set sample by adopting a Kennard-Stone method;
s4, preprocessing original spectral data by adopting a standard normal variable transformation algorithm, and then performing variable screening by adopting a continuous projection algorithm to obtain characteristic wavelengths;
s5, carrying out PCA dimension reduction analysis on the preprocessed full-waveband and characteristic wavelength spectrum data respectively, and then establishing a full-waveband spectrum-based PLS-DA detection model of the exogenous doped sucrose and an SPA-PLS-DA detection model of the exogenous doped sucrose based on the characteristic wavelength spectrum by taking the optimal principal component processed by PCA as input;
s6, respectively programming and establishing a PLS-DA detection model and an SPA-PLS-DA detection model in matlab software in a computer client, and communicating the computer client with a near infrared spectrometer, inputting spectral data obtained by scanning a black tea sample to be detected by the near infrared spectrometer into the PLS-DA detection model and the SPA-PLS-DA detection model, so as to realize the discrimination of the content of the external sucrose in the black tea and the online real-time detection of the content of the sugar.
In step S1, different amounts of exogenous sucrose are added during the rolling process of black tea processing, and the black tea samples with different sugar contents are: black tea samples without added sucrose, with 250g sucrose per 10kg twisted leaf, 500g sucrose per 10kg twisted leaf and 750g sucrose per 10kg twisted leaf.
Further, in the step S2, 20 ± 0.5g of black tea samples with different sugar contents are respectively weighed, evenly spread in quartz culture dishes with the specification of phi 70mm × 10mm, the tops of the tea leaves are flush with the upper surfaces of the dish bodies, and the quartz culture dishes are placed on a near infrared spectrometer for near infrared spectrum scanning and collection.
Further, in the step S2, the detection wavelength range of the near infrared spectrometer is 900-1700 nm, and the resolution is 4cm-1The number of scans per sample was 30.
Further, in the step S2, when the near infrared spectrum scan is performed on the black tea sample, after each scan is completed, the tea leaves are turned and stirred, so as to improve the model accuracy and better reflect the whole information of the sugared black tea.
Further, the ratio of the data amount of the training set samples to the prediction set samples is 2: 1.
Further, in the step S4, the characteristic wavelengths sensitive to the sugar content are 190nm, 206nm, 227nm, 300nm, 481nm, 502nm, 538nm, 573nm, 598nm, 621nm, 627nm, 684nm, 714nm, 732nm, 737nm, and 741nm, respectively.
The invention has the beneficial effects that:
(1) the invention defines the average spectrum information of black tea samples with different sugar contents, and the result shows that the average spectrum of the black tea samples without adding cane sugar and with adding exogenous cane sugar has obvious difference, and the difference between the average spectrum of the black tea samples with adding exogenous cane sugar with different contents is smaller, but the difference is obvious at the positions of absorption peaks of 300nm and 580nm and reflection valleys of 200nm and 400 nm.
(2) According to the invention, the original spectrum is preprocessed by an SNV method, the SPA is adopted to extract characteristic wavelengths, the PCA is adopted to reduce the dimension of the preprocessed full-waveband and characteristic wavelength data, the optimal principal components of the full-waveband and characteristic wavelength spectrum data are used as model input quantities to respectively establish an analysis model, and the result shows that the detection model established based on the characteristic wavelengths has higher precision after the SPA variable screening.
(3) Compared with a PLS-DA detection model established based on a full-wave band, the SPA-PLS-DA detection model established based on the characteristic wavelength has the advantages that the number of the used variables is 16, the number of the principal components is 5, the number of the principal components and the number of the variables are compressed, the data dimensionality is reduced through principal component analysis, the calculation load can be reduced through less principal component input, the calculation speed is accelerated, and the timeliness requirement of online monitoring in production can be met.
(4) The near-infrared detection technology used by the invention has the advantages of low cost, convenience, rapidness, strong stability, good repeatability and the like, and is an ideal means for detecting the content of the exogenous sucrose in the black tea.
Drawings
FIG. 1 is a graph of the average spectrum of different sugar content black tea samples according to the invention;
FIG. 2 is a distribution of characteristic wavelengths extracted from raw spectral data by SPA method in the present invention;
FIG. 3 is a three-dimensional loading map of the first three PCA principal components of the full-band spectrum of the present invention;
FIG. 4 is a three-dimensional loading plot of the first three PCA principal components of the characteristic wavelength spectrum of the present invention;
FIG. 5 is a diagram showing the prediction results of a PLS-DA detection model based on full-band building;
FIG. 6 is a diagram of prediction accuracy of a PLS-DA detection model prediction set established based on full-band spectra;
FIG. 7 is a histogram of a PLS-DA detection model prediction based on full band building;
FIG. 8 is a plot of predicted scatter plots based on a full-band PLS-DA detection model established;
FIG. 9 is a diagram of the prediction result of the SPA-PLS-DA detection model established based on characteristic wavelengths;
FIG. 10 is a diagram of the prediction accuracy of a prediction set of an SPA-PLS-DA detection model established based on characteristic wavelengths;
FIG. 11 is a predicted histogram of the SPA-PLS-DA detection model based on characteristic wavelengths;
FIG. 12 is a scatter diagram predicted by the SPA-PLS-DA detection model based on characteristic wavelengths.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention relates to a method for nondestructive detection of exogenous sucrose doped in tea based on near infrared spectroscopy, which comprises the steps of collecting prepared black tea samples with different sugar contents, extracting average spectrum information of the samples, then carrying out SNV (single nucleotide polymorphism) pretreatment and PCA (principal component analysis), establishing PLS-DA (partial least squares-data analysis) and SPA-PLS-DA (spatial feature analysis) models according to different principal component numbers, and qualitatively and quantitatively analyzing the condition of the exogenous sucrose doped in the black tea. The method comprises the following steps:
s1, preparing and obtaining black tea samples with different sugar contents;
s2, performing near infrared spectrum scanning on the black tea sample by using a near infrared spectrum analyzer, and acquiring original spectrum data of the black tea sample;
s3, randomly dividing an original spectrum data set into a training set sample and a prediction set sample by adopting a Kennard-Stone method;
s4, preprocessing original spectral data by adopting a standard normal variable transformation algorithm, and then performing variable screening by adopting a continuous projection algorithm to obtain characteristic wavelengths;
s5, carrying out PCA dimension reduction analysis on the preprocessed full-waveband and characteristic wavelength spectrum data respectively, and then establishing a full-waveband spectrum-based PLS-DA detection model of the exogenous doped sucrose and an SPA-PLS-DA detection model of the exogenous doped sucrose based on the characteristic wavelength spectrum by taking the optimal principal component processed by PCA as input;
s6, respectively programming and establishing a PLS-DA detection model and an SPA-PLS-DA detection model in matlab software in a computer client, and communicating the computer client with a near infrared spectrometer, inputting spectral data obtained by scanning a black tea sample to be detected by the near infrared spectrometer into the PLS-DA detection model and the SPA-PLS-DA detection model, so as to realize the discrimination of the content of the external sucrose in the black tea and the online real-time detection of the content of the sugar.
The method comprises the following specific steps:
the method of the embodiment needs a near-infrared spectrometer, a computer client, a data line, a quartz culture dish with the specification of phi 70mm multiplied by 10mm, matlab software and the like, the near-infrared spectrometer is provided with a data interface, and data obtained by scanning of the near-infrared spectrometer can be imported into the computer client through the data line or a hard disk. Before the near-infrared spectrometer is used, the near-infrared spectrometer is subjected to self-inspection firstly to ensure that the near-infrared spectrometer is in a normal working state, and self-inspection contents comprise an energy test, an X-axis frequency correction, a Y-axis repeatability test and the like and are carried out according to conventional operation.
1. Obtaining black tea samples with different sugar contents
Picking tea leaves with the tenderness of one bud and one leaf, and the variety of the picked tea leaves is Fuding white, naturally spreading the picked tea leaves in time, adding exogenous sucrose with different contents during natural withering, wherein in order to ensure that the established model has higher generalization performance, the adding amount of the exogenous sucrose during natural withering is respectively no addition (label 1), 250g of sucrose (label 2) is doped into every 10kg of withered tea leaves, 500g of sucrose (label 3) is doped into every 10kg of withered tea leaves, and 750g of sucrose (label 4) is doped into every 10kg of withered tea leaves. According to the result of the moisture meter (taking an average value by measuring three times), when the moisture of the withered leaves is 60 percent, rolling by using a 40-type rolling machine, wherein the rolling mode is as follows: air kneading for 15min → light kneading for 10min → heavy kneading for 5min → light kneading for 5min, 45min totally, and fermenting for 4h in an artificial climate box under the conditions that the fermentation temperature is 30 ℃ and the relative humidity is more than or equal to 90 percent to obtain the black tea sample to be detected.
2. Performing near infrared spectrum scanning on the black tea sample, and acquiring original spectrum data of the black tea sample by using spectrum acquisition software on a computer
When near infrared spectrum data are collected, respectively weighing 20 +/-0.5 g of black tea samples with different sugar contents, uniformly paving the black tea samples in quartz culture dishes with the specification of phi 70mm multiplied by 10mm, enabling the tops of the tea leaves to be flush with the upper surfaces of dish bodies, placing the quartz culture dishes on a near infrared spectrometer for near infrared spectrum scanning collection, wherein the detection wavelength range of the near infrared spectrometer is 900-1700 nm, and the resolution is 4cm-1And with air as a reference, collecting the spectrum for 30 times for each sample to obtain 120 pieces of original spectrum data, storing the spectrum data by contrasting corresponding labels, and exporting all the data after the collection is finished. Due to near infrared lightThe spectrometer is mostly fixed point monitoring when gathering the sample spectrum, can't acquire the spectral information of sample whole area, for the model that makes the establishment possess better robustness and generalization, improves the model precision, reflects the whole information of sugaring black tea better, so gather at every turn and accomplish the back, before the collection next time, do the processing of stirring to tealeaves, because the collection region is the same and lead to the spectral information coincidence phenomenon's that acquires emergence when avoiding gathering the sample. After the sample spectrum data are collected, the original spectrum data are imported into a computer client, and the matlab software is used for extracting the average spectra of black tea samples with different sugar contents, as shown in fig. 1, the average spectrum curves of different samples have detailed differences due to different amounts of exogenous sucrose doped in the black tea, and the method can be used for distinguishing models.
When near infrared spectrum data is collected, in order to reduce the influence of noise in the original spectrum, the part with larger noise in the spectrum of 1650nm-1700nm is removed.
The data in fig. 1 clearly shows the average spectrum information of black tea samples with different sugar contents, and the result shows that the average spectra of black tea samples without adding cane sugar and black tea samples with adding exogenous cane sugar have obvious difference, and although the difference between the average spectra of black tea samples with adding exogenous cane sugar with different contents is small, the difference is obvious at the positions of absorption peaks of 300nm and 580nm and reflection valleys of 200nm and 400 nm.
3. Preprocessing of raw spectral data
The method comprises the steps of randomly dividing 120 original spectrum data sets into training set samples and prediction set samples by adopting a Kennard-Stone (K-S) method, training and optimizing a model, correcting the established model by utilizing the training set samples, verifying the optimized established model by utilizing the prediction set samples, wherein the data volume ratio of the training set samples to the prediction set samples is 2:1, the training set samples contain 80 original spectrum data, and the prediction set samples contain 40 original spectrum data. In order to reduce the influence of noise in the spectral information and scattering phenomena caused by the surface of the tea leaves on the data accuracy, the original spectral data are preprocessed by a standard normal transformation (SNV) algorithm.
4. Screening of variables and establishment of detection model
The variable screening is carried out on the original spectral data pretreated by the SNV method by adopting an SPA method, the characteristic wavelength sensitive to the sugar content in the original data is extracted, 16 characteristic wavelengths are extracted, namely 190nm, 206nm, 227nm, 300nm, 481nm, 502nm, 538nm, 573nm, 598nm, 621nm, 627nm, 684nm, 714nm, 732nm, 737nm and 741nm are extracted, and the distribution situation of the extracted characteristic wavelengths is shown in figure 2.
Then, the preprocessed full-waveband and characteristic wavelength spectrum data are respectively subjected to PCA dimensionality reduction analysis, a three-dimensional load graph based on the first three PCA main components of the full-waveband spectrum data is obtained and is shown in FIG. 3, the accumulated contribution rate of characteristic values corresponding to the first three PCA main components, namely PC1, PC2 and PC3, is 99.81%, most of information can be represented, and the contribution rates of PC1, PC2 and PC3 are 81.99%, 17.32% and 0.50% respectively; fig. 4 shows a three-dimensional load graph of the first three PCA principal components based on the characteristic wavelength spectrum data, wherein the cumulative contribution ratios of the characteristic values corresponding to the first three PCA principal components, i.e., PC1 ', PC 2', and PC3 ', are 99.83%, which can represent most information, and the contribution ratios of PC 1', PC2 ', and PC 3' are 87.34%, 12.29%, and 0.20%, respectively. As can be seen from fig. 3 and 4, the scatter distribution of the characteristic wavelength samples extracted by the SPA is more concentrated, but the sample distribution areas with different sugar contents are different, and some areas are crossed, so that further model identification is required.
The partial least squares discriminant analysis (PLS-DA) is a multivariate statistical method integrating the basic functions of principal component analysis, canonical correlation analysis and multivariate regression analysis, and the analysis process can eliminate the mutually overlapped parts in a plurality of information, so that the analysis data is more accurate and reliable. The partial least squares discriminant analysis method of the embodiment is programmed in Matlab software, and four types of black tea samples with different sugar contents are classified and discriminated by using main component data extracted from the near infrared spectrum.
In order to compare the prediction effects of the analysis model of the content of the exogenous sucrose in the black tea established based on the full-wave band spectrum and the characteristic wavelength spectrum, the optimal main component after PCA treatment is used as input, and the detection models of the exogenous sucrose are respectively established based on the full-wave band spectrum and the characteristic wavelength spectrum. The method comprises the steps of establishing a PLS-DA detection model based on an optimal principal component of a full-band spectrum as a model input quantity, wherein a prediction result graph of the PLS-DA detection model established based on the full-band spectrum is shown in FIG. 5, a prediction accuracy graph of a PLS-DA detection model prediction set established based on the full-band spectrum is shown in FIG. 6, a prediction histogram of the PLS-DA detection model established based on the full-band spectrum is shown in FIG. 7, and a prediction scatter diagram of the PLS-DA detection model established based on the full-band spectrum is shown in FIG. 8; an SPA-PLS-DA detection model is established based on the optimal principal components of 16 characteristic wavelength spectrums as model input quantities, FIG. 9 is a prediction result graph of the SPA-PLS-DA detection model established based on characteristic wavelengths, FIG. 10 is a prediction accuracy graph of a prediction set of the SPA-PLS-DA detection model established based on characteristic wavelengths, FIG. 11 is a prediction histogram of the SPA-PLS-DA detection model established based on characteristic wavelengths, and FIG. 12 is a prediction scatter graph of the SPA-PLS-DA detection model established based on characteristic wavelengths. The specific classification of the sugared black tea samples by the test model is shown in table 1.
Table 1: PLS-DA and SPA-PLS-DA model discrimination results established based on full-wave band and characteristic wavelength
The recognition accuracy of a PLS-DA discrimination model established based on full-waveband spectral data on a training set and a prediction set is 95% and 87.5%, wherein the sample recognition accuracy is 100%, 3 samples without cane sugar are not recognized, and 2 samples with 250g of cane sugar doped into 10kg of withered leaves are not recognized; the recognition accuracy of the SPA-PLS-DA discrimination model established based on the characteristic wavelength spectrum data on the training set and the prediction set is 96.25 percent and 95 percent, wherein the sample recognition accuracy is 100 percent, and 2 samples doped with 250g of cane sugar for every 10kg of withered leaves are not recognized.
The data results in fig. 5 and fig. 9 show that when the number of principal components is 6 and the number of lv is 5, the performance of the PLS-DA model established based on the full band is the best, and the recognition rates of the corresponding training set and prediction set are 95% and 87.5%. When the number of the principal components is 5 and the number of lv is 5, the SPA-PLS-DA model established based on the characteristic wavelengths has the best performance, and the recognition rates of the corresponding training set and the prediction set are 96.25% and 95%. The PLS-DA model established based on the full-wave band has performance inferior to that of the SPA-PLS-DA model established based on the characteristic wavelength, after the spectral data are screened by SPA variables, 97.9% of redundant information in the spectral data is eliminated, the number of used principal components is reduced from 6 to 5, the data dimensionality can be reduced through principal component analysis, the calculation burden can be reduced through less principal component input, the calculation speed of the model is accelerated, therefore, the SPA-PLS-DA model is more suitable for the timeliness requirement of online monitoring in production, and the nondestructive detection and the rapid discrimination of the exogenous sucrose content of the sugared black tea can be realized.
In addition, correlation coefficients Rc obtained by a PLS-DA detection model established based on full-waveband spectral data are 0.9925, Rp is 0.9955, correlation coefficients Rc obtained by an SPA-PLS-DA detection model established based on characteristic wavelength spectral data are 0.9937, Rp is 0.9956, after characteristic wavelengths are extracted by SPA, 97.9% of redundant information is removed, and the correlation coefficients of a training set and the correlation coefficients of a prediction set are improved.
5. Discrimination of exogenous doped sucrose black tea and rapid detection of sugar content
The data line is connected to a computer client and used for receiving near infrared spectrum data of online detection, a PLS-DA detection model and an SPA-PLS-DA detection model are respectively established at the computer client by using matlab software, finally, the detection model is guided into the near infrared spectrometer through transmission media such as the data line, the near infrared spectrum data acquired by the near infrared spectrometer are input into the detection model, discrimination of sugared black tea and dynamic online real-time detection of the content of external cane sugar in the black tea can be realized on an operation interface of the near infrared spectrometer, after the detection is finished, an instrument automatically calculates, a sugar content result is directly displayed on the operation interface, and the dynamic online real-time detection of the content of the external cane sugar in the black tea is realized.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims. The techniques, shapes, and configurations not described in detail in the present invention are all known techniques.
Claims (8)
1. The method is characterized in that a black tea sample containing different amounts of exogenous sucrose is subjected to near infrared spectrum scanning, original spectrum data obtained by scanning is subjected to standard normal variable transformation algorithm processing, variable screening is performed by adopting a continuous projection algorithm, then principal component analysis is performed, a detection model is established by using the optimal principal component, near infrared spectrum data of the black tea sample to be detected are input into the detection model, and discrimination and content detection of the exogenous sucrose in the black tea are realized.
2. The method for nondestructive testing of sucrose exogenously doped in tea based on near infrared spectroscopy as claimed in claim 1, wherein said method comprises the steps of:
s1, preparing and obtaining black tea samples with different sugar contents;
s2, performing near infrared spectrum scanning on the black tea sample by using a near infrared spectrum analyzer, and acquiring original spectrum data of the black tea sample;
s3, randomly dividing an original spectrum data set into a training set sample and a prediction set sample by adopting a Kennard-Stone method;
s4, preprocessing original spectral data by adopting a standard normal variable transformation algorithm, and then performing variable screening by adopting a continuous projection algorithm to obtain characteristic wavelengths;
s5, carrying out PCA dimension reduction analysis on the preprocessed full-waveband and characteristic wavelength spectrum data respectively, and then establishing a full-waveband spectrum-based PLS-DA detection model of the exogenous doped sucrose and an SPA-PLS-DA detection model of the exogenous doped sucrose based on the characteristic wavelength spectrum by taking the optimal principal component processed by PCA as input;
s6, respectively programming and establishing a PLS-DA detection model and an SPA-PLS-DA detection model in matlab software in a computer client, and communicating the computer client with a near infrared spectrometer, inputting spectral data obtained by scanning a black tea sample to be detected by the near infrared spectrometer into the PLS-DA detection model and the SPA-PLS-DA detection model, so as to realize the discrimination of the content of the external sucrose in the black tea and the online real-time detection of the content of the sugar.
3. The method for nondestructive testing of sucrose doped in tea leaves based on near infrared spectrum technology as claimed in claim 2, wherein in the step S1, different amounts of exogenous sucrose are added during the kneading process of black tea processing, and the black tea samples with different sugar contents are respectively: black tea samples without added sucrose, with 250g sucrose per 10kg twisted leaf, 500g sucrose per 10kg twisted leaf and 750g sucrose per 10kg twisted leaf.
4. The method for nondestructive detection of sucrose doped in tea leaves based on near infrared spectroscopy as claimed in claim 2, wherein in step S2, 20 ± 0.5g of black tea samples with different sugar contents are respectively weighed and evenly spread in quartz petri dishes with a specification of Φ 70mm × 10mm, the tops of the tea leaves are flush with the upper surface of the dish body, and the quartz petri dishes are placed on a near infrared spectrometer for near infrared spectrum scanning and collection.
5. The nondestructive testing method for the exogenous sucrose-doped tea leaves based on the near infrared spectrum technology of claim 4, wherein in the step S2, the detection wavelength range of the near infrared spectrometer is 900-1700 nm, and the resolution is 4cm-1The number of scans per sample was 30.
6. The method for nondestructive testing of sucrose doped in tea leaves based on near infrared spectrum technology as claimed in claim 5, wherein in step S2, during scanning of black tea sample in near infrared spectrum, after each scanning acquisition is completed, tea leaves are stirred.
7. The method for nondestructive testing of sucrose doped in tea leaves based on near infrared spectrum technology as claimed in claim 2, wherein the ratio of the data volume of the training set sample to the data volume of the prediction set sample is 2: 1.
8. The method for nondestructive detection of sucrose exogenously doped in tea based on near infrared spectroscopy as claimed in claim 2 wherein in said step S4, the characteristic wavelengths sensitive to sugar content are 190nm, 206nm, 227nm, 300nm, 481nm, 502nm, 538nm, 573nm, 598nm, 621nm, 627nm, 684nm, 714nm, 732nm, 737nm and 741nm, respectively.
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