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

Abstract

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.

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

“…Despite the expectation that HC, LDA, KNN, Log-Reg, Nai-Bay, RF, SGD, SVM, and Tree [ 12 , 31 ] would have the highest precision and accuracy, our study found otherwise. Instead, SVM, PLS, and RF algorithms showed the highest accuracy for lettuce pigment phenotyping when used with a regression model [ 20 , 36 ].…”

“…Our study proposed that simple methods combined with AIAs (AdB, CN2, G-Boo, and NN algorithms) can classify lettuce varieties with high accuracy and precision based on results from at least eleven lettuce plants [ 1 ]. Therefore, algorithms such as iPLSR, PLSR, LDA, RF, SGD, and SVR over VIs were used to predict and classify optical leaf properties with high precision [ 12 , 29 ]. Some research reports successful prediction of pigments by VIs, with models developed by the best-performing RF, KNN, SGD, and Tree algorithms.…”

“…This is of great importance, considering that the global food economy is estimated at approximately $750 billion annually, and AIAs can help reduce food waste and improve consumer satisfaction. Accordingly, the integration of AIAs and hyperspectral data mining, deep learning, and machine learning techniques promises to be a promising approach for crop plant classification [ 1 , 7 , 12 ].…”

Transcriptomic analysis of the effect of shaking and tumbling degree on quality formation of Wuyi rock tea

“…According to the traditional picking method, Wuyi rock tea is mainly picked with one bud and three leaves. During the withering process, fresh tea leaves begin to lose a lot of water and soften, and substances in the leaves begin to transform and accumulate, laying the foundation for subsequent processing (Mao et al., 2022). Shaking and tumbling makes the edges of the leaves of the tea trees begin to break, and the leaves are affected by external stresses (Zeng et al., 2019).…”

Low temperature response index for monitoring freezing injury of tea plant

“…In this study, HSI system (GaiaField-Pro, Jiangsu Dualix Spectral Image Technology Co. Ltd) was used to collect spectral information of tea leaves in the range of 397-1008 nm. The composition, specifications and parameters of the HSI system were the same as those in reference ( Mao et al., 2022 ). The spatial resolution of hyperspectral images collected in this study was 960 × 1101 (Space × Spectrum), the spectral resolution was 3.5 nm and the number of spectral channels was 176.…”