To have a deeper understanding on the moisture migration patterns and microstructural changes of Green Sichuan Pepper during the hot-air drying process, the low-field nuclear magnetic resonance (LF-NMR) technology and scanning electron microscopy (SEM) methodology were adopted to analyze the moisture distribution, migration patterns and microscopic structural changes under different drying temperatures (45, 55 and 65 °C). The LF-NMR scanning results showed that the internal moisture of the Green Sichuan Pepper mainly includes bound water, immobilized water and free water, which can be respectively symbolized by the relaxation time ranges of T₂₁ (0.1–10 ms), T₂₂ (10–500 ms) and T₂₃ (500–10,000 ms). The immobilized water accounts for 83.72% of the internal water, resulting in the significant drying difficulty of Green Sichuan Pepper. During the drying process, the content of immobilized water and free water exhibited a decreasing trend, while the bound moisture content initially increased and then decreased. In addition, the LF-NMR analysis showed that the parameters peak area A₂ demonstrated a high correlation with the moisture content of Green Sichuan Pepper, enabling the prediction of moisture content changes during the drying process. Additionally, the SEM results showed that the pore degree and pore density on the pericarp surface of Green Sichuan Pepper perform significant changes during the drying process, which might be a good explanation for revealing some commonly recognized drying phenomena on Green Sichuan Pepper hot-air drying. In summary, the findings presented in the present work provide some new insights into the moisture distribution, migration patterns and microstructural changes of Green Sichuan Pepper, which can offer theoretical guidance for optimizing the drying process of Green Sichuan Pepper.

The fruits of Cornus officinalis are used not only as a popular health food to tonify the liver and kidney, but also as staple materials to treat dementia and other age-related diseases. The pharmacological function of C. officinalis fruits with or without seeds is controversial for treating some symptoms in a few herbal prescriptions. However, the related metabolite and pharmacological information between its pericarps and seeds are largely deficient. Here, comparative metabolomics analysis between C. officinalis pericarps and seeds were conducted using an ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry, and therapeutic effects were also evaluated using several in vitro bioactivity arrays (antioxidant activity, α-glucosidase and cholinesterase inhibitory activities, and cell inhibitory properties). A total of 499 secondary metabolites were identified. Thereinto, 77 metabolites were determined as key differential metabolites between C. officinalis pericarps and seeds, and the flavonoid biosynthesis pathway was identified as the most significantly different pathway. Further, 47 metabolites were determined as potential bioactive constituents. In summary, C. officinalis seeds, which demonstrated higher contents in total phenolics, stronger in vitro antioxidant activities, better α-glucosidase and butyrylcholinesterase inhibitory activities, and stronger anticancer activities, exhibited considerable potential for food and health fields. This work provided insight into the metabolites and bioactivities of C. officinalis pericarps and seeds, contributing to their precise development and utilization.

This study proposes a systematic performance analysis and evaluation framework for vegetable cold-chain drying equipment, aiming to enhance its energy efficiency, automation level, and standardization. First, common drying technologies are compared to assess their effects on energy consumption, drying efficiency, and product quality. Subsequently, key energy consumption metrics are established for this equipment. Furthermore, a comprehensive performance evaluation index system is developed using a fusion approach based on machine learning techniques that combine Boosting algorithms with multiple kernel functions.

Aiming at the low qualification rate and high damage caused by the lack of identification, localization, and posture estimation of tea buds in the mechanical harvesting process of famous tea, a framework of lightweight detection + PCA-skeleton fusion posture estimation was proposed. Based on the YOLOv8n model, the StarNet backbone network was introduced to enable lightweight detection, and the ASF-YOLO multi-scale attention module was embedded to improve the feature fusion ability. Based on the detection frame, the GrabCut-Watershed fusion segmentation was employed to obtain the bud mask. Combined with PCA and skeleton extraction algorithms, the main direction deviations of bent buds and clasped leaves were solved by Bézier curve fitting, and the morphology–posture dual-factor scoring model was thereby constructed to realize the picking ranking. Compared with the original YOLOv8n model, the results showed that the detection accuracy and mAP50 of the Improved model decreased to 85.6% and 90.5%, respectively, and the recall rate increased to 81.7%. Meanwhile, the calculation load of the improved model was reduced by 23.6%, reaching 6.8 GFLOPs, indicating a significant improvement in lightweight. The morphology–posture dual-factor scoring model achieved a score of 0.88 for a single bud in vertical direction (θ ≈ 90°), a score of approximately 0.66–0.71 for buds with partially unfolded leaves and slightly bent buds, and a score of 0.48–0.53 for severely bent and overlapped buds. The results of this study have the potential to guide the picking robotic arms to preferentially pick tea buds with high adaptability and provide a reliable visual solution for low-loss and high-efficiency mechanized harvesting of famous tea in complex tea gardens.