Livestock and poultry processing can often include the slaughtering, cutting, and deep processing into meat products for consumption. Among them, cutting is a pivotal step to determine the shape, quality, and taste of the meat, in order to significantly promote the added value and market competitiveness of the products. Intelligent cutting technology has been gradually used in the field of livestock and poultry processing in recent years, particularly with the development of intelligent technologies, such as artificial intelligence and machine vision. Production efficiency can be expected to enhance product quality with low labor intensity. This review aims to summarize the research advance on intelligent cutting technologies for livestock and poultry processing. Firstly, the current state was reviewed in the research of machine vision, tactile sensing, virtual reality (VR), and computed tomography (CT) in livestock and poultry processing. Each technology contributed uniquely to the precision, efficiency, and quality control. Machine vision was used to precisely and rapidly identify and classify meat cuts, especially for high consistency and quality. Tactile sensing technology was used to detect the physical properties, such as texture and firmness, which were critical for the quality assessment. VR technology was used to offer immersive training environments for workers to improve skill acquisition and operational efficiency. CT technology was to provide detailed imaging for accurate cutting and grading, in order to reduce waste and increase yield. The meat factory unit integrated these technologies into a cohesive system, streamlining operations and maximizing productivity. Some challenges still remained in the field behind these advancements. One major challenge was to integrate these diverse technologies into a seamless automated system, in order to operate under the demanding conditions of meat processing environments with robust hardware. Additionally, standardized protocols and practices were required to ensure the compatibility and interoperability among different systems and technologies. Several solutions were then proposed to develop more advanced machine learning for the high accuracy and efficiency of automated systems. The research and development were invested to enhance the durability and reliability of hardware. The industry-wide standards and best practices were required to establish for the technology integration. Collaboration was also required among researchers, industry stakeholders, and regulatory bodies. Looking ahead, the production efficiency can be improved to ensure food safety, and then enhance the industrial competitiveness in the development trend of intelligent cutting technology for livestock and poultry processing. The continuous evolution of intelligent technologies can also be expected to drive the industry towards greater automation and precision, leading to more sustainable and efficient processing. The findings can provide a comprehensive reference and insights into the current state and future directions of intelligent livestock and poultry processing. The livestock and poultry processing industry can achieve significant advancements, in terms of productivity, quality, and sustainability under intelligent technologies. This progression can also contribute to the broader societal goals of food security and resource efficiency. Therefore, intelligent cutting technologies can be realized to promote the development of intelligence, efficiency, and sustainability in livestock and poultry processing.

This study aims to improve the slicing accuracy and substantial material savings in frozen pork ribs. Three-dimensional reconstruction was also proposed for the quantitative program slicing on porcine large chops. Both the precision and efficiency of the slicing were optimized to enhance resource utilization in the meat processing industry. Advanced technologies were selected to revolutionize traditional meat slicing, ultimately contributing to operational efficiency and cost savings. Several key stages were utilized for the accurate and efficient slicing of the pork ribs. Initially, an experimental platform was developed for the point cloud acquisition. A line laser scanner was employed to capture the point cloud data of frozen pork ribs from multiple angles. The raw data was acquired to preprocess the point cloud. Data collection was realized to extract and isolate the target point clouds necessary for subsequent analysis. The data filtered out the noise and irrelevant information for accurate three-dimensional reconstruction. A registration was then conducted to accurately align the data because the point clouds were acquired from different viewpoints after the preprocessing. A rectangular target block was also utilized to accomplish the alignment in conjunction with the Trimmed Iterative Closest Point (ICP). The rectangular target block then acted as a reference point to align the various point clouds, while the Trimmed ICP algorithm facilitated the precise registration of the data. As such, all point clouds corresponded accurately to the actual physical structure of the pork ribs. Accurate alignment was essential to create a reliable three-dimensional model. The volume of the frozen pork ribs was then estimated after alignment. Graham scan algorithm was used to efficiently compute the convex hull of the point cloud, in order to determine the outer boundaries of the objects. The body slicing was segmented and then accumulated to accurately estimate the volume of irregularly shaped objects, like pork ribs. As such, the precise volume estimation was achieved to determine the appropriate slicing parameters for quality control and consistency in the final product. Finally, the required slicing thickness was calculated, according to the predefined quality requirements. At the same time, it was assumed that the frozen pork ribs exhibited a uniform density distribution throughout their mass. The thickness of each slice was also computed to fully meet the specified quality standards. The slicing process resulted in uniform and high-quality slices of meat. Quantitative slicing experiments were effectively conducted to produce the slices, according to the desired quality metrics. A series of tests were conducted on 15 sets of frozen pork rib samples, in order to validate the effectiveness and reliability of the quantitative slicing. The experimental results indicated that the average absolute error between the actual and the target slice quality was recorded at 8.62 g, with an impressive average relative error of 7.41%. Furthermore, the compliance rate of 85.67% was achieved after slicing. These findings confirmed that the quantitative slicing was effective and accurate in real-world applications. The successful implementation of quantitative slicing can also provide a viable solution to enhance the slicing operations in the meat processing industry. Additionally, the findings can also contribute significantly to the advancement of smart technology. The material wastage was also minimized to maintain the high slicing accuracy. This slicing technique can be integrated to enhance the operational efficiency, product quality, and resource utilization in meat processing facilities, ultimately leading to greater sustainability during operations. This research can also open the avenues to further explore and apply three-dimensional reconstruction in various aspects of food processing and quality evaluation.

Meat and its products, rich in protein, are one of the important potential sources of antioxidant peptides. However, reviews on the generation and application of meat-derived antioxidant peptides are still limited. To understand the research and application progress of meat-derived antioxidant peptides, the main formation pathways and their commercial applications are exhibited, the research methods for the isolation, purification, and identification are summarized, and the influencing factors, evaluation methods, and intestinal absorption pathways are presented in this work. It is summarized that limited degradation by exogenous and endogenous enzymatic hydrolysis is the main pathway for the production of animal-derived antioxidant peptides. Traditional separation, purification, and identification techniques are also applicable to animal-derived antioxidant peptides. The formation of animal-derived antioxidant peptides is affected by many factors, and the intestinal absorption pathways of antioxidant peptides are different. Finally, insufficient and future development directions are provided.