The main substances of rice are starches, which vary their metabolism during storage. We conducted a series of tests including rice physicochemical properties, edible quality, starch content and chain length distribution along with starch structure variation to disclose the shift of rice quality by observing the changes of rice during storage. The results showed that: (1) the rice deterioration occurred as time passed, and the germination rate decreased from 70.8% to 29.4% during the storage; (2) fatty acid values increased significantly during long-term storage; (3) electrical conductivity increased as time passed; and (4) the two-year-storage rice showed significantly decreased viscosity and edible quality after sensory evaluation, decreased hardness and damaged surface area of starch granules as storage time passed. Additionally, the damaged surface area of starch granules increased with storage time. Fourier transform infrared spectroscopy (FTIR) showed that the short-range order and spiral degree of rice starch first decreased in the first year and then increased over the storage time. Furthermore, X-ray diffraction showed that the main starch of rice was A-type crystalline. Meanwhile, apparent amylose content increased from 31.00% to 33.85%, then decreased to 31.75%. The peak viscosity reduced from 2735.00 mPa·s to 2163.67 mPa·s and the disintegration value was brought down from 1377.67 mPa·s to 850.33 mPa·s. Based on the results, rice should not be stored for more than 2 years under suitable granary conditions to maintain it at a good quality.

The camellia seed cake proteins (CP) used in this study were individually hydrolyzed with neutral protease, alkaline protease, papain, and trypsin. The results showed that the hydrolysate had the highest ACE inhibitory activity at 67.36 ± 0.80% after four hours of neutral protease hydrolysis. Val-Val-Val-Pro-Gln-Asn (VVVPQN) was then obtained through ultrafiltration, Sephadex G-25 gel chromatography separation, LC-MS/MS analysis, and in silico screening. VVVPQN had ACE inhibitory activity with an IC₅₀ value of 0.13 mg/mL (198.66 μmol/L), and it inhibited ACE in a non-competitive manner. The molecular docking indicated that VVVPQN can combine with ACE to form eight hydrogen bonds. The results of the stability study showed that VVVPQN maintained high ACE-inhibitory activity in weakly acidic and neutral environments and that heat treatment (20–80 °C) and Na⁺, Mg²⁺, as well as Fe³⁺ metal ions had little effect on the activity of VVVPQN. Moreover, it remained relatively stable after in vitro simulated gastrointestinal digestion. These results revealed that VVVPQN identified in camellia seed cake has the potential to be applied in functional food or antihypertensive drugs.

Tea is one of the world's most widely consumed non‐alcoholic beverages and is a rich source of bioactive compounds. The synthesis and accumulation of catechins, central flavonols in the secondary metabolism of the tea plant, not only define the bitterness and astringency of the tea infusion but also form the chemical cornerstone of its remarkable antioxidant activity, playing a key physiological role in the plant's abiotic stress tolerance. Catechin biosynthesis involves a complex enzymatic network in tea plants ( Camellia sinensis ). With the development of molecular biotechnology, the biosynthetic and chemical pathways have been progressively clarified, and the roles of key enzymes have been increasingly understood. Moreover, accumulating evidence shows that catechin distribution displays tissue‐specific patterns, providing important references for genetic improvement and utilization of tea resources. This review summarizes current knowledge on catechins and their derivatives, including biosynthesis and applications. It also highlights the specific accumulation of monomeric and polymeric catechins in different tissues and germplasm and discusses their prospective applications in functional foods, pharmaceuticals, and other health‐related industries. This work synthesizes the biosynthesis, tissue accumulation, and multifaceted health benefits of catechins, offering a theoretical basis for optimizing tea germplasm and deep‐processing. It underscores the nutrient and medicinal potential of catechins, thereby informing the development of functional foods and pharmaceuticals and enhancing the value chain of the tea industry.

The ‘Kyoho’ (Vitis labruscana) grape is one of the mainly fresh fruits; it is important to accurately segment the grape bunch and to detect its maturity level for the construction of an intelligent grape orchard. Grapes in the natural environment have different shapes, occlusion, complex backgrounds, and varying illumination; this leads to poor accuracy in grape maturity detection. In this paper, an improved Mask RCNN-based algorithm was proposed by adding attention mechanism modules to establish a grape bunch segmentation and maturity level detection model. The dataset had 656 grape bunches of different backgrounds, acquired from a grape growing environment of natural conditions. This dataset was divided into four groups according to maturity level. In this study, we first compared different grape bunch segmentation and maturity level detection models established with YoloV3, Solov2, Yolact, and Mask RCNN to select the backbone network. By comparing the performances of the different models established with these methods, Mask RCNN was selected as the backbone network. Then, three different attention mechanism modules, including squeeze-and-excitation attention (SE), the convolutional block attention module (CBAM), and coordinate attention (CA), were introduced to the backbone network of the ResNet50/101 in Mask RCNN, respectively. The results showed that the mean average precision (mAP) and mAP_0.75 and the average accuracy of the model established with ResNet101 + CA reached 0.934, 0.891, and 0.944, which were 6.1%, 4.4%, and 9.4% higher than the ResNet101-based model, respectively. The error rate of this model was 5.6%, which was less than the ResNet101-based model. In addition, we compared the performances of the models established with MASK RCNN, adding different attention mechanism modules. The results showed that the mAP and mAP_0.75 and the accuracy for the Mask RCNN50/101 + CA-based model were higher than those of the Mask RCNN50/101 + SE- and Mask RCNN50/101 + CBAM-based models. Furthermore, the performances of the models constructed with different network layers of ResNet50- and ResNet101-based attention mechanism modules in a combination method were compared. The results showed that the performance of the ResNet101-based combination with CA model was better than the ResNet50-based combination with CA model. The results showed that the proposed model of Mask RCNN ResNet101 + CA was good for capturing the features of a grape bunch. The proposed model has practical significance for the segmentation of grape bunches and the evaluation of the grape maturity level, which contributes to the construction of intelligent vineyards.