Meat Research

Assessment of antibiotic residues in beef cattle slaughtered in bangladesh: Implications for food safety and public health

MT Kamal, RA Deen, B Ahmed, MA Hashem — Wed, 30 Apr 2025 00:00:00 +0000

This study investigates the concentrations of tetracycline, ciprofloxacin, and enrofloxacin residues in various tissues of beef cattle post-slaughter in Bangladesh, emphasizing their implications for food safety and public health. Residue levels were analyzed across heart, kidney, liver, lung, and muscle tissues, utilizing high-sensitivity detection methods. The findings reveal that Tetracycline is present in all tested tissues, with the highest concentration observed in heart tissue (109.68 ppb) and the lowest in the liver (55.19 ppb), all remaining below the international maximum residue limits (MRLs). Ciprofloxacin was detected primarily in kidney and liver tissues, with concentrations substantially below the detection limits in heart, lung, and muscle. Enrofloxacin showed the highest residue levels in muscle tissues (45.36 ppb) and was undetectable in kidney and liver. These results highlight effective antibiotic residue management in the evaluated tissues, with all concentrations safely below MRLs, ensuring compliance with global food safety standards. However, the observed presence of these antibiotics, particularly the variations in residue levels, underscores the need for stringent monitoring and regulation to prevent potential health risks associated with antibiotic resistance and hypersensitivity reactions. This study contributes to the ongoing efforts to safeguard public health by providing a comprehensive evaluation of antibiotic residues in beef, thereby reinforcing the importance of adherence to pharmacological guidelines and regulatory standards in livestock management.

Effect of irradiation with black cumin (Nigella sativa) on the biochemical properties and microbial population of beef at different days of interval at ambient temperature

M Sadakuzzaman, MAH Bosunia , MRU Amin, MA Hashem — Wed, 30 Apr 2025 00:00:00 +0000

This study explored how combining gamma irradiation with black cumin (Nigella sativa) extract affects the biochemical makeup and microbial levels of beef stored at ambient temperature over time. Fresh, boneless beef samples were first treated with a 2% solution of black cumin extract, then exposed to gamma radiation at doses of 0 (as a control), 3, 5, and 7 kGy using a cobalt-60 (⁶⁰Co) irradiator. Biochemical analyses such as peroxide value (POV), free fatty acids (FFA), and thiobarbituric acid reactive substances (TBARS) were performed on treated samples at 0, 3, 5, and 7 days. Microbial analyses included total coliform count (TCC), total viable count (TVC), total yeast and mould count (TYMC), and the existence of Salmonella spp. and Staphylococcus spp. Results demonstrated significant (p<0.01) increases in POV, FFA, and TBARS values with both greater radiation exposure and prolonged storage which enhanced lipid oxidation. Microbial counts were equally affected by both treatment and time, with large increases in TCC, TVC, and TYMC, whereas Staphylococcus spp. exhibited a general decline and Salmonella spp. varied depending on the treatment. The factorial experimental design indicated considerable interactions (p<0.01) between radiation dose and storage term on most biochemical and microbiological markers. These observations show that irradiation in association with black cumin (Nigella sativa) extract can influence both oxidative stability and microbiological safety of beef and that optimization of dose and storage conditions are important to ensure product quality under ambient temperature.

Effect of different concentration of moringa leaf extract (MLE) on proximate components, cooking loss, pH and color attributes of broiler meat sausage batter

MH Kabir, A Ahmmed, MA Hashem , MS Ali — Wed, 30 Apr 2025 00:00:00 +0000

This study investigated the effects of varying concentrations of Moringa leaf extract (MLE) on the proximate composition, cooking loss, pH, and color attributes (CIE L*, a*, b*) of broiler meat sausage batter. Employing a 3x5 factorial design, the study incorporated various storage periods (0, 14, and 28 days) and treatment groups (control, BHT, and MLE at 0.5%, 1.0%, and 1.5%). Results demonstrated that 1.5% MLE significantly reduced cooking loss (14.73 ± 0.97%) and improved protein retention (21.54 ± 0.07%) compared to the control group, which exhibited a cooking loss of 19.07 ± 3.11% and protein retention of 18.49 ± 1.49%. Additionally, MLE significantly influenced pH, reducing it from 6.69 in the control to values ranging from 6.27 to 6.33 in the treated samples (p < 0.01), while influencing color parameters by increasing yellowness (b = 23.85 at 1.5% MLE) and decreasing redness (a = −2.82). Proximate components, including dry matter, ash, protein, and fat, as well as cooking loss, remained unaffected (p > 0.05). Notably, MLE at a 1.0% concentration was identified as optimal, enhancing antioxidant properties without compromising sensory or nutritional quality. These findings underscore the potential of MLE as a functional, natural ingredients in clean-label meat products, catering to the growing consumer demand for alternatives to synthetic additives aimed at improving the quality and shelf life of broiler meat sausages.

Advancing the meat industry with machine learning: A study of progress, challenges, and potential

M Nayeem, MH Rahman, MA Rahman, MN Haque Haque, MA Hashem — Wed, 30 Apr 2025 00:00:00 +0000

The integration of machine learning (ML) in the meat industry is reshaping traditional practices by introducing data-driven approaches to improve product quality, operational efficiency, safety, and sustainability. This comprehensive overview explores the application of key ML techniques including supervised learning, unsupervised learning, reinforcement learning, and deep learning in various domains such as meat quality assessment, supply chain optimization, adulteration detection, automated processing, and consumer behavior analysis. As ML algorithms become increasingly sophisticated and accessible, their ability to process large datasets from imaging systems, sensors, and chemical analyses enables the detection of complex patterns and the automation of critical decisions. While the benefits of ML in the meat industry are substantial, the adoption of these technologies is not without challenges. Issues such as data availability, high computational requirements, integration with legacy systems, and the need for standardized regulations pose significant barriers. Nonetheless, ongoing technological advancements particularly in the realms of IoT, big data, and predictive analytics are paving the way for more efficient disease prevention strategies, enhanced food safety, and reduced environmental impact. This paper highlights the current state, challenges, and future trends of machine learning applications in the meat industry. It emphasizes the potential of ML to build a more intelligent, transparent, and sustainable meat production ecosystem, ultimately aligning industry practices with modern consumer expectations and global food safety standards.