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Foods
Special Issues
Livestock Product Processing and Quality Control
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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: 30 September 2025 | Viewed by 3337

Special Issue Editors

Dr. Jungseok Choi

E-Mail Website
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
Dr. Ji-Yeon Chun

E-Mail Website
Guest Editor
Department of Food Bioengineering, Jeju National University, Jeju 63243, Republic of Korea
Interests: food processing; food emulsion; animal feed; livestock products
Dr. Hyun-wook Kim

E-Mail Website
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

17 pages, 304 KB  
Article
Chinese Cabbage Powder and Clove Extract as Natural Alternatives to Synthetic Nitrite and Ascorbate in Clean-Label Pork Sausages
by Jibin Park, Su Min Bae, Yeongmi Yoo, Minhyeong Kim and Jong Youn Jeong
Foods 2025, 14(19), 3316; https://doi.org/10.3390/foods14193316 - 24 Sep 2025
Viewed by 8
Abstract
The objective of this study was to evaluate the potential of clove extract powder (CEP) as a natural curing accelerator in pork sausages produced with pre-converted Chinese cabbage powder (PCCP) as a nitrite source. Sausages were prepared using a 3 × 2 × [...] Read more.
The objective of this study was to evaluate the potential of clove extract powder (CEP) as a natural curing accelerator in pork sausages produced with pre-converted Chinese cabbage powder (PCCP) as a nitrite source. Sausages were prepared using a 3 × 2 × 2 factorial design with three levels of CEP (0, 500, and 1000 ppm), two sodium ascorbate levels (0 and 500 ppm), and two nitrite sources (synthetic sodium nitrite and PCCP). Increasing the level of CEP decreased pH, CIE L*, CIE a*, and residual nitrite, whereas CIE b*, cured meat pigment, total pigment, and curing efficiency were increased (p < 0.05). The inclusion of sodium ascorbate decreased (p < 0.05) residual nitrite levels while enhancing CIE b*, cured meat pigment, and curing efficiency. Compared with sodium nitrite, PCCP treatments retained higher residual nitrite (p < 0.05), although no significant differences (p ≥ 0.05) were observed for instrumental color, cured meat pigment, total pigment, or curing efficiency. These results demonstrate that CEP, when combined with PCCP, effectively promotes the development of cured meat color and enhances pigment stability, suggesting that this combination can serve as a promising alternative to synthetic nitrite and ascorbate in clean-label pork sausages. Full article
(This article belongs to the Special Issue Livestock Product Processing and Quality Control)
20 pages, 607 KB  
Article
The Taste of Sustainability: Sensory Experience and Stated Preference Trade-Offs in Consumer Evaluation of Goat Cheese from Extensive Farming Systems
by Giuseppe Di Vita, Manal Hamam, Luigi Liotta, Vincenzo Lopreiato, Maria Lunetta, Federica Consentino and Daniela Spina
Foods 2025, 14(18), 3197; https://doi.org/10.3390/foods14183197 - 13 Sep 2025
Viewed by 318
Abstract
This research investigates consumer behavior and intention to buy (ITB) for sustainable goat cheese made from milk sourced through extensive farming systems. By integrating sensory experiment with stated preference data on credence and search attributes—such as sustainability claims, labeling, and quality certifications—and analyzing [...] Read more.
This research investigates consumer behavior and intention to buy (ITB) for sustainable goat cheese made from milk sourced through extensive farming systems. By integrating sensory experiment with stated preference data on credence and search attributes—such as sustainability claims, labeling, and quality certifications—and analyzing them using Partial Least Squares Structural Equation Modeling (PLS-SEM), this research offers a comprehensive perspective on the drivers of consumer decision-making, bridging actual sensory perception with hypothetical market choices. The findings clarify the trade-offs consumers are willing to make between taste and sustainability. Notably, the results reveal that a compelling sensory experience can lead consumers to deprioritize sustainability indicators and labeling claims, indicating that when sensory satisfaction is high, informational cues exert less influence on purchase intentions. To deepen the analysis, this study also explores the mediating role of consumer attitude, demonstrating that attitudes significantly translate product perceptions—particularly sensory and extrinsic attributes—into buying intentions. This integrated approach contributes a novel methodological framework and offers both theoretical and practical insights for marketers and policymakers aiming to promote sustainable food choices. Full article
(This article belongs to the Special Issue Livestock Product Processing and Quality Control)
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12 pages, 2674 KB  
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
Cited by 3 | Viewed by 2134
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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