Quantitative prediction and visualization of key physical and chemical components in black tea fermentation using hyperspectral imaging

Yang, Chongshan; Zhao, Yan; An, Ting; Liu, Zhongyuan; Jiang, Yongwen; Li, Yaqi; Dong, Chunwang

doi:10.1016/j.lwt.2021.110975

Cited by 42 publications

(17 citation statements)

References 16 publications

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“…Many reports indicated that selecting some important spectral variables represents better prediction outcomes than spectra containing redundant variables [ 39 , 40 ]. For example, by comparing the models using the CARS algorithm for processed and unprocessed spectral data, Yang et al found that the characteristic wavelength model established after screening was better than the full wavelength model [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

“…For example, in 2021, Ye et al used hyperspectral images to estimate the non-galloyl and galloyl types of catechins in new shoots of green tea, and the determination coefficient (R 2 ) of the estimation model can exceed 0.79 [ 24 ]. Yang et al established a model to quantitatively predict the main endoplasmic components of Congou black tea under a different fermentation time series [ 25 ]. Dong et al applied hyperspectral technology with the chemometrics method to predict the catechin content of tea leaves at different fermentation times [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Mao

Wang

et al. 2022

Foods

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The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Mao

Wang

et al. 2022

Foods

View full text Add to dashboard Cite

show abstract

“…As multivariable (high-dimensional) data are extracted from hyperspectral images; they contain many inter-band correlations, resulting in long data processing times and low accuracy and robustness of the models [ 48 , 49 ]. After the SNV spectral pretreatment, the CARS algorithm was employed to identify the optimal wavelengths that carry the most information, which is useful for determining the moisture content, L *, a *, b *, hardness, and elasticity.…”

Section: Resultsmentioning

confidence: 99%

Real-Time Monitoring of the Quality Changes in Shrimp (Penaeus vannamei) with Hyperspectral Imaging Technology during Hot Air Drying

Zhang

Wang

et al. 2022

Foods

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Hot air drying is the most common processing method to extend shrimp’s shelf life. Real-time monitoring of moisture content, color, and texture during the drying process is important to ensure product quality. In this study, hyperspectral imaging technology was employed to acquire images of 104 shrimp samples at different drying levels. The water distribution and migration were monitored by low field magnetic resonance and the correlation between water distribution and other quality indicators were determined by Pearson correlation analysis. Then, spectra were extracted and competitive adaptive reweighting sampling was used to optimize characteristic variables. The grey-scale co-occurrence matrix and color moments were used to extract the textural and color information from the images. Subsequently, partial least squares regression and least squares support vector machine (LSSVM) models were established based on full-band spectra, characteristic spectra, image information, and fused information. For moisture, the LSSVM model based on full-band spectra performed the best, with residual predictive deviation (RPD) of 2.814. For L*, a*, b*, hardness, and elasticity, the optimal models were established by LSSVM based on fused information, with RPD of 3.292, 2.753, 3.211, 2.807, and 2.842. The study provided an in situ and real-time alternative to monitor quality changes of dried shrimps.

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“…Before data acquisition, the machine was preheated for 30 min; we then set the resolution at 2.8 nm, the sampling interval at 0.67 nm, the exposure time at 4.2 ms, the sample conveyor speed at 1.24 mm/s, the spacing between the sample and the lens at 22.6 cm, and the light intensity at 103 cd. After acquisition, we carried out black and white plate correction again, following the methods of Yang Chongshan et al [ 15 ]. The process of data acquisition is shown in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Dong

Yang

Liu

et al. 2021

Sensors

Self Cite

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Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.

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Quantitative prediction and visualization of key physical and chemical components in black tea fermentation using hyperspectral imaging

Cited by 42 publications

References 16 publications

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Real-Time Monitoring of the Quality Changes in Shrimp (Penaeus vannamei) with Hyperspectral Imaging Technology during Hot Air Drying

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

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