CN111795943A - A method for non-destructive detection of exogenous sucrose in tea based on near-infrared spectroscopy - Google Patents

Abstract

The invention relates to the technical field of tea quality detection, in particular to a method for nondestructively detecting exogenous sucrose doped in tea based on near-infrared spectroscopy technology. The original spectral data is processed by the standard normal variable transformation algorithm, and the continuous projection algorithm is used for variable screening, and then the principal component analysis is performed to establish a detection model with the optimal principal component, and the near-infrared spectral data of the black tea sample to be detected is input into the detection. In the model, the discrimination and content detection of exogenous sucrose in black tea are realized. The invention aims to solve the problems of difficulty in manual identification of sugar-sweetened black tea and time-consuming and labor-intensive physical and chemical detection, realizes nondestructive, rapid and accurate identification and quantitative detection of sugar content of sugar-sweetened black tea, and provides a theoretical method for qualitative and quantitative detection of exogenous sucrose in black tea and scientific basis.

Description

A method for non-destructive detection of exogenous sucrose in tea based on near-infrared spectroscopy

technical field

The invention relates to the technical field of tea quality detection, in particular to a method for nondestructively detecting exogenous doped sucrose in tea based on near-infrared spectroscopy technology.

Background technique

In recent years, due to consumers' love for black tea, my country's black tea industry has continued to maintain a steady growth trend, and the output of black tea has also gradually increased. According to the data of the National Bureau of Statistics, the output of black tea in 2018 reached 233,300 tons, an increase of 251.9% over 2008. . With the rise of the black tea industry, there are more and more types of black tea, and high-end black tea is gradually moving into people's field of vision, and the sales volume is increasing year by year. At present, there are a small number of high-grade black teas on the market, but the name is not true. The reason is that some unscrupulous traders add exogenous sucrose in the processing of black tea, in order to achieve the effect of high-grade black tea. The sensory quality evaluation of sucrose makes it difficult for consumers to distinguish, which seriously affects the order of the black tea market and harms the interests of tea farmers; in addition, because sucrose is easy to absorb moisture, easy to deteriorate, and easy to breed bacteria, it has a great impact on the quality and safety of black tea. Impact.

At present, the methods for detecting the content of exogenous sucrose in black tea at home and abroad mainly use chemical reagents. The polyvinylpyrrolidone-activated carbon combined adsorbent removes the interference of the matrix components in the tea-like extract on the color reaction and detection of resorcinol, and realizes the qualitative and quantitative rapid determination of sucrose added during tea processing. Although the above method has high precision, it requires the help of relevant instruments and certain scientific research literacy, and it is necessary to extract or brew tea leaves, which is time-consuming, laborious, and has high economic costs. It is not suitable for consumers and tea traders. promotion. Therefore, it is particularly important to develop a non-destructive and rapid method for exogenous sucrose in black tea.

As a new type of non-destructive testing technology, near-infrared spectroscopy technology uses the hydrogen-containing groups in substances to absorb near-infrared light with a wavelength range of 780-2526 nm due to the transition of electrons. Therefore, NIR spectroscopy has achieved good results in the detection of agricultural products. In recent years, it has been widely used in the fields of component content prediction, classification identification, rot identification, and real-time monitoring, and has gradually developed and matured. Near-infrared spectroscopy technology has also been studied in the processing fields of tea withering and fermentation. Some tea companies (such as small pots of tea) have also established intelligent production lines based on near-infrared detectors, and achieved good results. The qualitative and quantitative detection of source sucrose has not been reported yet.

SUMMARY OF THE INVENTION

In view of this, the object of the present invention is to provide a method for non-destructively detecting exogenous sucrose in tea based on near-infrared spectroscopy, aiming to solve the problems such as difficulty in manual identification of sugar-sweetened black tea and time-consuming and laborious physical and chemical detection, and realize non-destructive and rapid detection of sugar-sweetened black tea. , Accurate identification and quantitative detection of sugar content provide theoretical methods and scientific basis for the qualitative and quantitative detection of exogenous sucrose in black tea.

The present invention solves the above-mentioned technical problems through the following technical means:

A method for non-destructive detection of exogenous sucrose in tea based on near-infrared spectroscopy. The method is to scan black tea samples containing different amounts of exogenous sucrose by near-infrared spectroscopy, and perform a standard normal variable transformation algorithm on the original spectral data obtained by scanning. process, and use continuous projection algorithm to screen variables, then carry out principal component analysis, establish a detection model with the optimal principal component, and input the near-infrared spectral data of the black tea sample to be detected into the detection model, so as to realize the detection of exogenous sucrose in black tea. Discrimination and content detection.

Further, the method includes the following steps:

S1. Prepare and obtain black tea samples with different sugar content;

S2. Use a near-infrared spectrum analyzer to scan the black tea sample by near-infrared spectrum, and collect and obtain the original spectral data of the black tea sample;

S3. Use the Kennard-Stone method to randomly divide the original spectral data set into training set samples and prediction set samples;

S4. Use the standard normal variable transformation algorithm to preprocess the original spectral data, and then use the continuous projection algorithm to filter variables to obtain characteristic wavelengths;

S5. Perform PCA dimensionality reduction analysis on the preprocessed full-band and characteristic wavelength spectral data respectively, and then use the PCA-processed optimal principal components as input to establish a full-band spectrum-based PLS-DA detection of exogenously doped sucrose model, and the establishment of a SPA-PLS-DA detection model for exogenously doped sucrose based on characteristic wavelength spectra;

S6. Program the PLS-DA detection model and the SPA-PLS-DA detection model in the matlab software in the computer client, respectively, and connect the computer client to the near-infrared spectrometer, and the near-infrared spectrometer scans the black tea sample to be tested to obtain The spectral data is input into the PLS-DA detection model and the SPA-PLS-DA detection model to realize the discrimination of the exogenous sucrose content in black tea and the online real-time detection of the sugar content.

Further, in the step S1, different contents of exogenous sucrose are added during the black tea processing and rolling process. The black tea samples with different sugar contents are: no added sucrose, 250 g of sucrose per 10 kg of kneaded leaves, and 10 kg of kneaded leaves mixed with sucrose. A sample of black tea with 500 g of sucrose and 750 g of sucrose per 10 kg of rolled leaves was added.

Further, in the step S2, 20±0.5g of black tea samples with different sugar contents were weighed, and were evenly spread in a quartz petri dish with a specification of Φ70mm×10mm, the top of the tea leaves was flush with the upper surface of the dish, and the quartz The petri dish was placed on a near-infrared spectrometer for near-infrared spectral scanning collection.

Further, in the step S2, the detection wavelength range of the near-infrared spectrometer is 900-1700 nm, the resolution is 4 cm ^-1 , and the number of scans for each sample is 30 times.

Further, in the step S2, when near-infrared spectrum scanning is performed on the black tea sample, after each scan and collection is completed, the tea leaves are subjected to a stirring process, so as to improve the model accuracy and better reflect the overall information of the sugar-sweetened 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 respectively 190 nm, 206 nm, 227 nm, 300 nm, 481 nm, 502 nm, 538 nm, 573 nm, 598 nm, 621 nm, 627 nm, 684 nm, 714 nm, 732 nm, 737 nm and 741 nm.

Beneficial effects of the present invention:

(1) The present invention clarifies the average spectral information of black tea samples with different sugar contents, and the results show that there is a significant gap between the average spectra of black tea samples without sucrose and those with exogenous sucrose added, and the average spectra of black tea samples with different contents of exogenous sucrose added. Although the gap between them is small, there are significant differences between the absorption peaks at 300nm and 580nm and the reflection valleys at 200nm and 400nm.

(2) The present invention preprocesses the original spectrum by the SNV method, uses SPA to extract the characteristic wavelength, and uses PCA to reduce the dimension of the preprocessed full-band and characteristic wavelength data, and uses the optimal principal component of the full-band and characteristic wavelength spectral data The analysis models were established as the model input, and the results showed that the detection model based on the characteristic wavelength was more accurate after the SPA variables were screened.

(3) The present invention shows that, compared with the PLS-DA detection model established based on the whole waveband, the SPA-PLS-DA detection model established by the characteristic wavelength uses 16 variables, 5 principal components, and the number of principal components and variables. All of them are compressed, and the data dimension is reduced by principal component analysis. Less principal component input can reduce the computational burden, speed up the calculation speed, and meet the timeliness requirements of online monitoring in production.

(4) The near-infrared detection technology used in the present invention has the advantages of low cost, convenience and quickness, strong stability and good repeatability, and is an ideal means for detecting the content of exogenous sucrose in black tea.

Description of drawings

Fig. 1 is the average spectral curve of different sugar content black tea samples in the present invention;

Fig. 2 is the distribution situation of the characteristic wavelength that adopts SPA method to the original spectral data extraction in the present invention;

Fig. 3 is the three-dimensional loading diagram of the first three PCA principal components of full-band spectrum in the present invention;

Fig. 4 is the three-dimensional loading diagram of the first three PCA principal components of characteristic wavelength spectrum in the present invention;

Fig. 5 is the prediction result of the PLS-DA detection model established based on the full band;

Fig. 6 is the prediction accuracy chart of the prediction set of the PLS-DA detection model established based on the full-band spectrum;

Fig. 7 is the prediction bar chart of the PLS-DA detection model established based on the full band;

Fig. 8 is the prediction scatter diagram of the PLS-DA detection model established based on the full band;

Fig. 9 is the prediction result graph of the SPA-PLS-DA detection model established based on the characteristic wavelength;

Fig. 10 is the prediction accuracy chart of the prediction set of the SPA-PLS-DA detection model established based on the characteristic wavelength;

Fig. 11 is the prediction bar chart of the SPA-PLS-DA detection model established based on characteristic wavelength;

Figure 12 is a scatter plot of prediction of the SPA-PLS-DA detection model established based on characteristic wavelengths.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The method for non-destructively detecting exogenous sucrose in tea based on near-infrared spectroscopy of the present invention collects prepared black tea samples with different sugar contents, extracts the average spectral information of the samples, and then performs SNV method pretreatment and PCA analysis, and uses different main The PLS-DA and SPA-PLS-DA models were established based on the number of components, which could be used to qualitatively and quantitatively analyze the exogenous sucrose doped in black tea. The method includes the following steps:

S1. Prepare and obtain black tea samples with different sugar content;

S2. Use a near-infrared spectrum analyzer to scan the black tea sample by near-infrared spectrum, and collect and obtain the original spectral data of the black tea sample;

S3. Use the Kennard-Stone method to randomly divide the original spectral data set into training set samples and prediction set samples;

S4. Use the standard normal variable transformation algorithm to preprocess the original spectral data, and then use the continuous projection algorithm to filter variables to obtain characteristic wavelengths;

S5. Perform PCA dimensionality reduction analysis on the preprocessed full-band and characteristic wavelength spectral data respectively, and then use the PCA-processed optimal principal components as input to establish a full-band spectrum-based PLS-DA detection of exogenously doped sucrose model, and the establishment of a SPA-PLS-DA detection model for exogenously doped sucrose based on characteristic wavelength spectra;

S6. Program the PLS-DA detection model and the SPA-PLS-DA detection model in the matlab software in the computer client, respectively, and connect the computer client to the near-infrared spectrometer, and the near-infrared spectrometer scans the black tea sample to be tested to obtain The spectral data is input into the PLS-DA detection model and the SPA-PLS-DA detection model to realize the discrimination of the exogenous sucrose content in black tea and the online real-time detection of the sugar content.

details as follows:

The method of this embodiment requires the use of a near-infrared spectrometer, a computer client, a data cable, a quartz petri dish with a size of Φ70mm×10mm, and matlab software. The data is imported into the computer client. Before using the near-infrared spectrometer, perform a self-check on the instrument to ensure that the instrument is in normal working condition. The self-check content includes energy test, X-axis frequency correction and Y-axis repeatability test, etc., all of which are carried out according to routine operations.

1. Obtain samples of black tea with different sugar content

The tenderness of the picked tea leaves is one bud and one leaf, and the variety is Fuding Dabai. The picked tea leaves are spread out naturally in time, and different contents of exogenous sucrose are added during natural withering, in order to ensure that the established model has a high generalization. , the amount of exogenous sucrose added during natural withering is respectively no addition (label 1), 250g sucrose (label 2) per 10kg wither leaves, 500g sucrose (label 3) per 10kg wither leaves, and 10kg per 10kg wither leaves. Add 750 g of sucrose (label 4). According to the results of the moisture analyzer (the average value of three measurements), when the moisture content of the withered leaves is 60%, the 40-type kneading machine is used for kneading. Heavy kneading for 5 min → light kneading for 5 min, totaling 45 min, and then fermenting for 4 h in an artificial climate box at a fermentation temperature of 30 °C and a relative humidity of ≥90% to obtain a black tea sample to be tested.

2. Scan the black tea samples by near-infrared spectroscopy, and use the spectral acquisition software on the computer to obtain the original spectral data of the black tea samples

When collecting near-infrared spectral data, 20 ± 0.5 g of black tea samples with different sugar contents were weighed and evenly spread in a quartz petri dish with a size of Φ70 mm × 10 mm. The top of the tea leaves was flush with the upper surface of the dish body. The quartz petri dish was placed on a near-infrared spectrometer for near-infrared spectrum scanning and collection. The near-infrared spectrometer used was in the detection wavelength range of 900 to 1700 nm, with a resolution of 4 cm ^-1 . Taking air as a reference, each sample was collected 30 times of spectrum, a total of 30 spectra were collected. Obtain 120 original spectral data, save them against the corresponding labels, and export all the data after the acquisition is completed. Since the near-infrared spectrometer collects the sample spectrum mostly for fixed-point monitoring, the spectral information of the entire sample area cannot be obtained. In order to make the established model have better robustness and generalization, improve the model accuracy, and better reflect the sugar-sweetened black tea. Therefore, after each collection is completed and before the next collection, the tea leaves are turned over to avoid the phenomenon of complete overlap of the acquired spectral information due to the same collection area during sample collection. After collecting the sample spectral data, import the original spectral data into the computer client, and use the matlab software to extract the average spectra of black tea samples with different sugar content, as shown in Figure 1, due to the difference in the amount of exogenous sucrose doped in the black tea, resulting in different The average spectral curve of the samples has detailed differences, which can be used for model discrimination.

When collecting near-infrared spectral data, in order to reduce the influence of noise in the original spectrum, the noisy part of the 1650nm-1700nm spectrum is removed.

The data in Figure 1 clarifies the average spectral information of black tea samples with different sugar content. The results show that there is a significant difference between the average spectra of black tea samples without sucrose and those with exogenous sucrose. The average spectra of black tea samples with different amounts of exogenous sucrose Although the difference between them is small, there are significant differences between the absorption peaks at 300nm and 580nm and the reflection valleys at 200nm and 400nm.

3. Preprocessing of raw spectral data

The Kennard-Stone (K-S) method is used to randomly divide the 120 original spectral data sets into training set samples and prediction set samples, which are used for model training and optimization. The ratio of the data volume of the training set samples to the prediction set samples is 2:1, the training set samples contain 80 original spectral data, and the prediction set samples contain 40 original spectral data. In order to reduce the influence of noise in the spectral information and the scattering phenomenon caused by the tea surface on the accuracy of the data, the standard normal Ztransformation (SNV) algorithm was used to preprocess the original spectral data.

4. Screening of variables and establishment of detection models

The original spectral data preprocessed by the SNV method was filtered by the SPA method, and the characteristic wavelengths sensitive to sugar content in the original data were extracted, and 16 characteristic wavelengths were extracted, namely 190nm, 206nm, 227nm, 300nm, 481nm, 502nm, 538nm, 573nm, 598nm, 621nm, 627nm, 684nm, 714nm, 732nm, 737nm and 741nm, the distribution of the extracted characteristic wavelengths is shown in Figure 2.

Then, the preprocessed full-band and characteristic wavelength spectral data were subjected to PCA dimensionality reduction analysis, and the three-dimensional loading diagram of the first three PCA principal components based on the full-band spectral data was obtained as shown in Figure 3. The first three PCA principal components That is, the cumulative contribution rate of the eigenvalues corresponding to PC1, PC2, and PC3 is 99.81%, which can represent most of the information. The contribution rates of PC1, PC2, and PC3 are 81.99%, 17.32%, and 0.50%, respectively; The three-dimensional loading diagram of each PCA principal component is shown in Figure 4. The cumulative contribution rate of the eigenvalues corresponding to the first three PCA principal components, namely PC1', PC2', and PC3', is 99.83%, which can represent most of the information. PC1', PC1', PC3' The contribution rates of PC2' and PC3' are 87.34%, 12.29% and 0.20%, respectively. As can be seen from Figure 3 and Figure 4, the characteristic wavelength samples extracted by SPA are more concentrated, but the distribution areas of samples with different sugar content are also different, and some areas still have crossovers. Therefore, further model identification is required. .

Partial least squares discriminant analysis (PLS-DA) is a multivariate statistical method that integrates the basic functions of principal component analysis, canonical correlation analysis and multiple regression analysis. Data is more accurate and reliable. The partial least squares discriminant analysis method in this embodiment is programmed in Matlab software, and uses the principal component data extracted from the near-infrared spectrum to classify and discriminate four types of black tea samples with different sugar contents.

In order to compare the prediction effect of the analysis model for exogenous sucrose content in black tea established based on full-band and characteristic wavelength spectra, the optimal principal components after PCA treatment were used as input, and the detection models of exogenous sucrose content were established based on full-band and characteristic wavelength spectra, respectively. . That is, based on the optimal principal component of the full-band spectrum as the model input, the PLS-DA detection model is established. Figure 5 is the prediction result of the PLS-DA detection model established based on the full-band spectrum, and Figure 6 is the PLS-DA established based on the full-band spectrum. The prediction accuracy chart of the detection model prediction set, Figure 7 is the prediction bar chart of the PLS-DA detection model established based on the full band, and Figure 8 is the prediction scatter plot of the PLS-DA detection model established based on the full band; based on 16 characteristic wavelength spectra The optimal principal component is used as the model input to establish the SPA-PLS-DA detection model. Figure 9 is the prediction result of the SPA-PLS-DA detection model established based on the characteristic wavelength, and Figure 10 is the SPA-PLS-DA established based on the characteristic wavelength. The prediction accuracy of the detection model prediction set, Figure 11 is the prediction bar chart of the SPA-PLS-DA detection model established based on the characteristic wavelength, and Figure 12 is the prediction scatter diagram of the SPA-PLS-DA detection model established based on the characteristic wavelength. The specific classification of sugar-sweetened black tea samples by the detection model is shown in Table 1.

Table 1: Discrimination results of PLS-DA and SPA-PLS-DA models based on full-band and characteristic wavelengths

The recognition accuracy of the PLS-DA discriminant model based on the full-band spectral data for the training set and the prediction set is 95% and 87.5%, of which the correct rate of sample recognition is 100%, and 3 samples without added sucrose are not recognized, each 10kg Two samples of withered leaves mixed with 250g sucrose were not identified; the recognition accuracy of the SPA-PLS-DA discriminant model based on the characteristic wavelength spectral data for the training set and the prediction set was 96.25% and 95%, and the sample identification accuracy was 96.25% and 95%. 100%, 2 were not identified in samples spiked with 250 g of sucrose per 10 kg of withered leaves.

The data results in Figure 5 and Figure 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 based on the full band is the best, and the recognition rate of the corresponding training set and prediction set is 95% and 87.5%. %. When the number of principal components is 5 and the number of lv is 5, the performance of the SPA-PLS-DA model based on the characteristic wavelength is the best, and the recognition rates of the corresponding training set and prediction set are 96.25% and 95%. The performance of the PLS-DA model based on the whole waveband is not as good as that of the SPA-PLS-DA model based on the characteristic wavelength. After the spectral data is screened by the SPA variable, 97.9% of the redundant information in the spectral data is eliminated, and the number of principal components used is 6 The number is reduced to 5, and the principal component analysis can reduce the data dimension. Fewer principal component inputs can reduce the computational burden and speed up the calculation speed of the model. Therefore, the SPA-PLS-DA model is more suitable for the timeliness requirements of online monitoring in production. , which can realize non-destructive detection and rapid identification of exogenous sucrose content in sugar-sweetened black tea.

In addition, the correlation coefficient Rc obtained by the PLS-DA detection model established based on the full-band spectral data is 0.9925, Rp is 0.9955, and the correlation coefficient Rc obtained by the SPA-PLS-DA detection model established based on the characteristic wavelength spectral data is 0.9937, and Rp is 0.9956, 97.9% redundant information is removed after the feature wavelength is extracted by SPA, and both the training set correlation coefficient and the prediction set correlation coefficient are improved.

5. Discrimination of exogenous sucrose-doped black tea and rapid detection of sugar content

Connect the data cable to the computer client to receive the near-infrared spectral data of online detection, and use the matlab software on the computer client to establish the PLS-DA detection model and the SPA-PLS-DA detection model respectively, and finally pass the detection model through the data. The transmission medium such as line is imported into the near-infrared spectrometer, and the near-infrared spectral data collected by the near-infrared spectrometer is input into the detection model. In the operation interface of the near-infrared spectrometer, the identification of sugar-sweetened black tea and the dynamic online real-time detection of exogenous sucrose content in black tea can be realized. , and after the detection is completed, the instrument automatically calculates and directly displays the results of sugar content on the operation interface, realizing dynamic online real-time detection of exogenous sucrose content in black tea.

The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced. Without departing from the spirit and scope of the technical solutions of the present invention, all of them should be included in the scope of the claims of the present invention. The technology, shape, and structural part that are not described in detail in the present invention are all well-known technologies.

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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