Recent advances review in tea waste: High-value applications, processing technology, and value-added products

Use of Fermented Black Tea (Camellia sinensis) Factory Wastes in Standard Rat Diets

Although there are studies on the use of different tea extracts in animal diets, there are no studies on the use of fermented black tea factory production waste (FTFW) in rodent diets. This study aims to evaluate the effects of FTFW, considered an environmentally hazardous waste, in standard rat diets regarding nutritional, histopathological, and biochemical parameters. In this study, 40 male Wistar albino rats (70 days old, with a live weight of 200-250 g) were divided into four groups, each containing 10 rats. They were fed standard rat diets for 63 days, with varying amounts (0%, 3%, 5%, and 10%) of tea production waste. At the end of the experiment, biochemical analysis of blood and histopathological analysis of all organs were performed. There was no significant difference between the groups in terms of body weight, internal organ weights, or serum biochemical parameters (p > 0.05). No pathological findings were observed in any of the groups. The group receiving 10% tea waste, which had the highest total polyphenol level (1.42 ppm), also showed the highest serum total antioxidant status (TAS) (p < 0.05). In contrast, this group had the lowest total oxidant status (TOS) and oxidative stress index (OSI) levels (p < 0.05). The study concluded that FTFW can be included in the pellet diet of rats without changing their general health status and that a potential environmental pollutant can be used in rat diets.

Enzyme-assisted dynamic extraction as a promising method to produce high-quality fresh tea juice from summer tea leaves

To improve the flavor of green tea juice processed from summer tea leaves, the enzyme-assisted dynamic extraction method was applied. The chemical components and taste of fresh green tea juice prepared under different extraction conditions with enzyme treatment (EM: tannase or its combination with protease/cellulase/pectinase) were investigated. The results showed that EM could reduce the bitterness and astringency of the tea juice by up to 40 % and 39 %, and enhance the sweet aftertaste to 132 %. These improvements resulted from a significant decrease in ester catechins and an increase in non-ester catechins and gallic acid. In addition, it was also found that the tea juice extracted with EM had less sediment (73 %) and better stability. Therefore, combined dynamic extraction with EM for the extraction of green tea leaves could effectively improve the taste, and increase the extraction rate of tea juice, promoting the utilization rate of abandon-plucked tea leaves in summer.

Prediction of Total Soluble Solids in Apricot Using Adaptive Boosting Ensemble Model Combined with NIR and High-Frequency UVE-Selected Variables

Total soluble solids (TSSs) serve as a crucial maturity indicator and quality determinant in apricots, influencing harvest timing and postharvest management decisions. This study develops an advanced framework integrating adaptive boosting (Adaboost) ensemble learning with high-frequency spectral variables selected by uninformative variable elimination (UVE) for the rapid non-destructive detection of fruit quality. Near-infrared (NIR) spectra (1000~2500 nm) were acquired and then preprocessed through robust principal component analysis (ROBPCA) for outlier detection combined with z-score normalization for spectral pretreatment. Subsequent data processes included three steps: (1) 100 continuous runs of UVE identified characteristic wavelengths, which were classified into three levels-high-frequency (≥90 times), medium-frequency (30-90 times), and low-frequency (≤30 times) subsets; (2) the development of the base optimal partial least squares regression (PLSR) models for each wavelength subset; and (3) the execution of adaptive weight optimization through the Adaboost ensemble algorithm. The experimental findings revealed the following: (1) The model established based on high-frequency wavelengths outperformed both full-spectrum model and full-characteristic wavelength model. (2) The optimized UVE-PLS-Adaboost model achieved the peak performance (R = 0.889, RMSEP = 1.267, MAE = 0.994). This research shows that the UVE-Adaboost fusion method enhances model prediction accuracy and generalization ability through multi-dimensional feature optimization and model weight allocation. The proposed framework enables the rapid, non-destructive detection of apricot TSSs and provides a reference for the quality evaluation of other fruits in agricultural applications.

Robust Brewed Tea Waste/Reduced Graphene Oxide Hydrogel for High Performance Flexible Supercapacitors

Tea waste contains various substances with phenolic hydroxyl groups, including lignin, tannins, tea polyphenols, etc., which are rarely utilized. In this study, tea waste was directly dispersed with graphene oxide to prepare tea waste/reduced graphene oxide (TW/rGO) hydrogel through a one-step hydrothermal method. The prepared hydrogel presented a continuous three-dimensional porous structure and exhibited good mechanical properties with a compressive strength of 53.4 ± 4.0 kPa. It also showed excellent electrochemical performance as an electrode material. Its specific capacitance reached 434.7 F g-1 at a current density of 1 A g-1, and its capacitance retention was 55.8% when the current density was increased to 100 A g-1. In addition, an TW/rGO assembled all-solid-state supercapacitor demonstrated a superior specific capacitance of 372.8 F g-1 and a competitive energy density of 12.9 Wh kg-1 at 1 A g-1.