Recent insights into bonding technologies in restructured meat production: A review.

Restructuring meat products is one way of improving material utilization and economic efficiency. In this process of combining meat pieces or granules to form larger pieces of meat, the additives and processing techniques employed in bonding the restructured meat play crucial roles in the formation of the structure and appearance of the meat while simultaneously reducing nutrient and water loss and enhancing flavor. This study reviews the adhesives commonly used in meat recombination technology, including transglutaminase, glucono-delta-lactone, fibrin, gelatin, and gel emulsifiers such as hydrophilic colloid, phosphate, starch, and cellulose. Additionally, processing technologies such as high-pressure, ultrasonic, vacuum-assisted, microwave, and three-dimensional printing are discussed, with emphasis on their principles, properties, functionalities, and safety. The study further summarizes the application and research progress of various bonding techniques in restructured meat. It analyzes the advantages, challenges, and development prospects of these techniques to provide support for further research in this field.

Establish intelligent detection system to evaluate the sugar smoking of chicken thighs.

The objective of this study was to establish a standardized color detection method to achieve low-cost, rapid, nonintrusive and accurate characterization of the color change of smoked chicken thighs during the smoking process. This study was based on machine vision technology using the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm to establish 3 colorimetric cards for the color of sugar-smoked chicken thighs. The accuracy of the 3 colorimetric cards was verified by the K-medoids algorithm and sensory analysis, respectively. Results showed that all 3 colorimetric cards had significant color gradient changes. From the K-medoids algorithm, the accuracy of the colorimetric card produced by the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm was 87.2, 95.1, and 96.7%, respectively. Meanwhile, the verification results of the sensory analysis showed that the accuracy of the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm colorimetric card was 69.4, 80.9, and 79.2%, respectively. A comparative analysis found that the colorimetric cards produced by the K-means algorithm and K-means algorithm + image noise reduction have excellent accuracy. These 2 colorimetric cards could become a suitable method for rapid, low-cost, and accurate online color monitoring of smoked chicken.