Livestock Product Processing and Quality Control

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Meat".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 1265

Special Issue Editors


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Guest Editor
Department of Animal Science, College of Agriculture, Life and Environment Sciences, Chungbuk National University, Cheongju, Republic of Korea
Interests: fresh meat; processed meat products; livestock carcass characteristics; cell-cultured meat, livestock products
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Food Bioengineering, Jeju National University, Jeju 63243, Republic of Korea
Interests: food processing; food emulsion; animal feed; livestock products

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Guest Editor
Department of Animal Science and Biotechnology, Gyeongsang National University, Jinju 52725, Republic of Korea
Interests: animal by-product utilization; foodtech application; senior-friendly meat products; protein gelation; protein hydrolysate; livestock products

Special Issue Information

Dear Colleagues,

The global demand for safe, nutritious, and high-quality livestock products continues to rise, driven by population growth, urbanization, and changing dietary preferences. Livestock products, including meat, dairy, and eggs, are essential components of human diets, providing vital nutrients and contributing significantly to food security and economic development. However, the production and processing of these products present unique challenges in terms of their safety, quality, and sustainability.

This Special Issue aims to bring together the latest research and innovative approaches in the field of livestock product processing and quality control, including:

  • Automated Slaughter and Processing: Technological advancements that improve the efficiency and welfare standards in slaughtering operations.
  • Chilling, Freezing, and Preservation: Studies on the impact of different preservation methods on the microbiological safety, shelf life, and sensory attributes of livestock products.
  • Microbiological Safety: Research on the prevalence of foodborne pathogens in livestock products and the development of effective interventions to control their spread.
  • Quality Assurance Systems: Examination of various quality assurance systems and their effectiveness in maintaining livestock product quality throughout the supply chain.
  • Sustainability and Environmental Impact: Analysis of the environmental footprint of livestock product processing and the exploration of sustainable practices.
  • Regulatory Compliance and Policy: Discussion on the role of regulations in ensuring livestock product safety and the impact of policy on industry practices.

This Special Issue explores advanced methodologies in processing meat, dairy, and other animal products, especially emphasizing quality assurance and safety through the use of artificial intelligence and machine learning. We invite original research, reviews, and case studies on topics such as environmental monitoring, automated slaughter and processing, precision agriculture, low-carbon practices, antibiotic resistance, and residue testing. Contributions addressing regulatory compliance and emerging technologies like CRISPR and blockchain are also highly valued. This platform aims to enhance the quality, safety, and sustainability of livestock products in the food industry.

Dr. Jungseok Choi
Dr. Ji-Yeon Chun
Dr. Hyun-wook Kim
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • livestock product quality
  • meat products
  • dairy products
  • automated slaughter and processing
  • precision agriculture
  • low-carbon practices
  • shelf-life optimization
  • antibiotic resistance and residue testing

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Published Papers (1 paper)

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Research

12 pages, 2674 KiB  
Article
Hyperspectral Imaging Combined with Machine Learning Can Be Used for Rapid and Non-Destructive Monitoring of Residual Nitrite in Emulsified Pork Sausages
by Woo-Young Son, Mun-Hye Kang, Jun Hwang, Ji-Han Kim, Yash Dixit and Hyun-Wook Kim
Foods 2024, 13(19), 3173; https://doi.org/10.3390/foods13193173 - 6 Oct 2024
Viewed by 935
Abstract
The non-destructive and rapid monitoring system for residual nitrite content in processed meat products is critical for ensuring food safety and regulatory compliance. This study was performed to investigate the application of hyperspectral imaging in combination with machine learning algorithms to predict and [...] Read more.
The non-destructive and rapid monitoring system for residual nitrite content in processed meat products is critical for ensuring food safety and regulatory compliance. This study was performed to investigate the application of hyperspectral imaging in combination with machine learning algorithms to predict and monitor residual nitrite concentrations in emulsified pork sausages. The emulsified pork sausage was formulated with 1.5% (w/w) sodium chloride, 0.3% (w/w) sodium tripolyphosphate, 0.5% (w/w) ascorbic acid, and sodium nitrite at concentrations of 0, 30, 60, 90, 120, and 150 mg/kg, based on total sample weight. Hyperspectral imaging measurements were conducted by capturing images of the cross-sections and lateral sides of sausage samples in a linescan mode, covering the spectral range of 1000–2500 nm. The analysis revealed that higher nitrite concentrations could influence the protein matrix and hydrogen-bonding capacities, which might cause increased reflectance at approximately 1080 nm and 1280 nm. Machine learning models, including XGBoost, CATboost, and LightGBM, were employed to analyze the hyperspectral data. XGBoost demonstrated the best performance, achieving an R2 of 0.999 and a root mean squared error of 0.095, highlighting its high predictive accuracy. This integration of hyperspectral imaging with advanced machine learning algorithms offers a non-destructive and real-time method for monitoring residual nitrite content in processed meat products, noticeably improving quality control processes in the meat industry. Additionally, real-time implementation in industrial settings could further streamline quality control and enhance operational efficiency. Further research should focus on validating these findings with larger sample sizes and more diverse datasets to ensure robustness. Full article
(This article belongs to the Special Issue Livestock Product Processing and Quality Control)
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