Taking the extraction yield of Bletilla striata polysaccharide (BSP) as the index and taking the type of deep eutectic solvents (DESs), extraction time, extraction temperature, DES water content, and solid–liquid ratio as the investigation factors, single-factor and Box–Behnken response surface tests were carried out to optimize the extraction process of BSP. Thus, the antioxidant activity of BSP on DPPH radicals, ABTS radicals and ferric reducing antioxidant power were determined. The results showed that the most suitable deep eutectic solvent was DES-2, namely choline chloride-urea. The optimal extraction conditions for BSP were an extraction time of 47 min, extraction temperature of 78 °C, water content of 35%, and solid–liquid ratio of 1:25. Under this optimized condition, the extraction yield of BSP was able to reach (558.90 ± 8.83) mg/g, and recycling studies indicated the good cycle stability of the DES. Antioxidant results showed that BSP had superior antioxidant activity and had a dose–response relationship with drug concentration. Compared with Bletilla striata polysaccharide obtained via conventional hot water extraction (BSP-W), the extraction yield of BSP obtained through this method (BSP-2) increased by 36.77%, the scavenging activity of DPPH radicals increased by 24.99%, the scavenging activity of ABTS radicals increased by 41.16%, and the ferric reducing antioxidant power increased by 49.19%. Therefore, DESs as new green reagents and BSP extracted with DESs not only had a high yield but also had strong antioxidant activity.

The maturity of tobacco leaves plays a decisive role in tobacco production, affecting the quality of the leaves and production control. Traditional recognition of tobacco leaf maturity primarily relies on manual observation and judgment, which is not only inefficient but also susceptible to subjective interference. Particularly in complex field environments, there is limited research on in situ field maturity recognition of tobacco leaves, making maturity recognition a significant challenge. In response to this problem, this study proposed a MobileNetV1 model combined with a Feature Pyramid Network (FPN) and attention mechanism for in situ field maturity recognition of tobacco leaves. By introducing the FPN structure, the model fully exploits multi-scale features and, in combination with Spatial Attention and SE attention mechanisms, further enhances the expression ability of feature map channel features. The experimental results show that this model, with a size of 13.7 M and FPS of 128.12, performed outstandingly well on the task of field maturity recognition of tobacco leaves, achieving an accuracy of 96.3%, superior to classical models such as VGG16, VGG19, ResNet50, and EfficientNetB0, while maintaining excellent computational efficiency and small memory footprint. Experiments were conducted involving noise perturbations, changes in environmental brightness, and occlusions to validate the model’s robustness in dealing with the complex environments that may be encountered in actual applications. Finally, the Score-CAM algorithm was used for result visualization. Heatmaps showed that the vein and color variations of the leaves provide key feature information for maturity recognition. This indirectly validates the importance of leaf texture and color features in maturity recognition and, to some extent, enhances the credibility of the model. The model proposed in this study maintains high performance while having low storage requirements and computational complexity, making it significant for in situ field maturity recognition of tobacco leaves.

Cherries, as high-value horticultural products, have long faced preservation challenges due to their perishable nature and limited postharvest longevity. During storage and transportation, these stone fruits are particularly susceptible to quality deterioration and pathological decay, significantly impacting commercial viability and consumer acceptance. Modified atmosphere preservation (MAP) technology has emerged as the predominant preservation method for cherry storage, recognized for its operational safety, environmental controllability, and technical reliability. This review systematically examines the physiological degradation mechanisms of cherries during storage, identifies critical environmental factors influencing decay patterns, and synthesizes recent advancements in MAP applications. The analysis encompasses technological principles and efficacy evaluations of atmospheric modification, with particular emphasis on how regulated temperature, humidity, and gas composition parameters affect the bioactive compound retention, organoleptic properties, and overall eating quality—crucial factors for enhancing consumer satisfaction and market value. Furthermore, this paper critically addresses current technological limitations, including implementation costs, operational complexity, and environmental sustainability concerns. Finally, it proposes innovative optimization strategies and outlines future development trajectories to advance technological refinement and promote sustainable innovation in terms of cherry preservation methodologies.

This work used the natural ingredient stigmasterol as an oleogelator to explore the effect of concentration on the properties of organogels. Organogels based on rapeseed oil were investigated using various techniques (oil binding capacity, rheology, polarized light microscopy, X-ray diffraction, and Fourier transform infrared spectroscopy) to better understand their physical and microscopic properties. Results showed that stigmasterol was an efficient and thermoreversible oleogelator, capable of structuring rapeseed oil at a stigmasterol concentration as low as 2% with a gelation temperature of 5 °C. The oil binding capacity values of organogels increased to 99.74% as the concentration of stigmasterol was increased to 6%. The rheological properties revealed that organogels prepared with stigmasterol were a pseudoplastic fluid with non-covalent physical crosslinking, and the G’ of the organogels did not change with the frequency of scanning increased, showing the characteristics of strong gel. The microscopic properties and Fourier transform infrared spectroscopy showed that stigmasterol formed rod-like crystals through the self-assembly of intermolecular hydrogen bonds, fixing rapeseed oil in its three-dimensional structure to form organogels. Therefore, stigmasterol can be considered as a good organogelator. It is expected to be widely used in food, medicine, and other biological-related fields.

This study investigated the pharmacological and pathological effects of aqueous mulberry leaf extract on type 1 diabetes mellitus mice induced with an intraperitoneal injection of streptozotocin (STZ). Diabetic mice were randomized into six groups: control (normal group), model, metformin-treated mice, and high-dose, medium-dose, and low-dose mulberry. The mulberry-treated mice were divided into high-, medium-, and low-dose groups based on the various doses of aqueous mulberry leaf extract during gavage. The efficacy of the six-week intervention was evaluated by measuring levels of fasting plasma glucose, alkaline phosphatase, alanine aminotransferase, aspartate transaminase, blood urea nitrogen, gamma-glutamyl transferase, glucose, high-density lipoprotein cholesterol, lactate dehydrogenase, and low-density lipoprotein cholesterol and recording body weight. Results revealed that mulberry leaf extract exhibited an ideal hypoglycemic effect, and the high-dose group was the most affected. Histology analysis, glycogen staining and apoptosis detection were used to study the extract’s effects on the liver, kidney, and pancreatic cells of diabetic mice, enabling the assessment of its effectiveness and complications on a clinical and theoretical basis. It was shown that a certain concentration of aqueous mulberry leaf extract repaired the islet cells of type 1 diabetes mellitus mice, promoting normal insulin secretion. Herein, it was confirmed that mulberry leaf could be used to develop new hypoglycemic drugs or functional health food with broad applicability.

In this study, a novel pattern recognition method named boosting–partial least squares–discriminant analysis (Boosting-PLS-DA) was developed for the non-destructive and accurate origin identification of food and medicine homologous substances (FMHSs). Taking Gastrodia elata, Aurantii Fructus Immaturus, and Angelica dahurica as examples, spectra of FMHSs from different origins were obtained by portable near-infrared (NIR) spectroscopy without destroying the samples. The identification models were developed with Boosting-PLS-DA, compared with principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA) models. The model performances were evaluated using the validation set and an external validation set obtained one month later. The results showed that the Boosting-PLS-DA method can obtain the best results. For the analysis of Aurantii Fructus Immaturus and Angelica dahurica, 100% accuracies of the validation sets and external validation sets were obtained using Boosting-PLS-DA models. For the analysis of Gastrodia elata, Boosting-PLS-DA models showed significant improvements in external validation set accuracies compared to PLS-DA, reducing the risk of overfitting. Boosting-PLS-DA method combines the high robustness of ensemble learning with the strong discriminative capability of discriminant analysis. The generalizability will be further validated with a sufficiently large external validation set and more types of FMHSs.