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Meat plays a significant role in human diets, providing a rich source of high-quality protein. With advancements in technology, research in the field of meat preservation has been undergoing dynamic evolution. To gain insights into the development of this discipline, the study conducted an analysis and knowledge structure mapping of 1672 papers related to meat preservation research within the Web of Science Core Collection (WOSCC) spanning from 2001 to 2023. And using software tools such as VOSviewer 1.6.18 and CiteSpace 5.8.R3c allowed for the convenient analysis of the literature by strictly following the software operation manuals. Moreover, the knowledge structure of research in the field of meat preservation was synthesized within the framework of “basic research—technological application—integration of technology with fundamental research,” aligning with the research content. Co-cited literature analysis indicated that meat preservation research could be further categorized into seven collections, as well as highlighting the prominent role of the antibacterial and antioxidant properties of plant essential oils in ongoing research. Subsequently, the future research direction and focus of the meat preservation field were predicted and prospected. The findings of this study could offer valuable assistance to researchers in swiftly comprehending the discipline’s development and identifying prominent research areas, thus providing valuable guidance for shaping research topics.

In order to explore the quality variation and flavor formation of dry-cured donkey leg, the changes in physical–chemical composition, lipolytic, free amino acids content and volatile flavor compounds were investigated in this study. Six fresh, trimmed hind legs with average weight of 8.12 ± 0.8 kg were taken from male Dezhou donkeys slaughtered at the age of 24 months with the average live weight of 240 kg. The entire processing time was eight months long including six stages, specifically: cooling, salting, air-drying, fermenting and aging. Samples were collected at 0 d, 10 d, 20 d, 30 d, 65 d, 105 d and 165 d of processing. The results showed that the pH value remained stable in the range of 6.2~6.6. The moisture and water activity significantly decreased (p < 0.05) during processing. The chloride content, ash, total volatile basic nitrogen (TVB-N) and peroxide value (POV) significantly increased (p < 0.05), from 0.45% to 12.39%, from 3% to 17%, from 1.43 mg/kg to 8.98 mg/kg and from 1.39 g/100 g to 5.26 g/100 g, respectively. The thiobarbituric acid (TBARS) value reached its highest value of 0.39 mg MDA/kg at the end of the salting stage and then decreased to 0.34 mg MDA/kg. Eighteen free amino acids and fifteen free fatty acids were detected, and their contents were significantly increased during processing (p < 0.05). Volatile compounds were analyzed using solid-phase microextraction (SPME) and gas chromatography–mass spectrometry (GC–MS). Among 114 volatile compounds detected in dry-cured donkey leg, aldehydes, esters, alkane and alcohols were more abundant in the final products, with relative concentrations of 41.88%, 5.72%, 5.35% and 5.25%, respectively. Processing significantly affected the physical–chemical properties, which could contribute to the formation of flavor substances of dry-cured donkey leg.

Foreign objects such as kites, nests and balloons, etc., suspended on transmission lines may shorten the insulation distance and cause short-circuits between phases. A detection method for foreign objects on transmission lines is proposed, which combines multi-network feature fusion and random forest. Firstly, the foreign object image dataset of balloons, kites, nests and plastic was established. Then, the Otus binarization threshold segmentation and morphology processing were applied to extract the target region of the foreign object. The features of the target region were extracted by five types of convolutional neural networks (CNN): GoogLeNet, DenseNet-201, EfficientNet-B0, ResNet-101, AlexNet and then fused by concatenation fusion strategy. Furthermore, the fused features in different schemes were used to train and test random forest, meanwhile, the gradient-weighted class activation mapping (Grad-CAM) was used to visualize the decision region of each network, which can verify the effectiveness of the optimal feature fusion scheme. Simulation results indicate that the detection accuracy of the proposed method can reach 95.88%, whose performance is better than the model of a single network. This study provides references for detection of foreign objects suspended on transmission lines.