(1) Background: During COVID-19, disruption in food demand and supply chain led to changes in food choices in response to consumer demand, frequency of delivered items, and production setup during a pandemic. The aim of the study was to evaluate the effect of quality and nutritional attributes on consumer food consumption behavior, attitude, and practices. (2) Methods: In this regard, cross-sectional survey research was conducted through a structured questionnaire. (3) Results: The results of the study showed that there was no difference in the receptiveness of COVID-19 infection between both genders. Quality perspective (p = 0.001) was deemed a significant positive predictor in the change of food consumption patterns during the COVID-19 pandemic. It also stated price (p = 0.045) and purity (p = 0.009) as a quality factor while sugar (p = 0.028) and fiber (p = 0.034) content, as nutritional attributes, influenced the consumption frequency of food groups. The overall experience of online shopping was in the neutral category. (4) Conclusions: It was concluded that food quality cues as well as nutritional attributes affected consumer food choices during the COVID-19 pandemic regardless of gender. Online shopping trends were influenced but overall experience remained neutral during the pandemic.

Meat serves as a prime medium for the growth of foodborne pathogens due to its rich protein content and high water activity, contributing significantly to the global burden of foodborne illnesses. This review synthesizes current advances in meat-borne bacterial pathogen detection with particular emphasis on emerging artificial intelligence (AI)-enabled applications. Major pathogens of concern, including Salmonella, Listeria monocytogenes, Escherichia coli, Campylobacter, Clostridium, and Staphylococcus aureus, are examined in relation to their relevance across the meat supply chain. Recent progress in biosensors (clustered regularly interspaced short palindromic repeats), CRISPR-based assays, isothermal amplification, and metagenomics is evaluated alongside the growing role of AI in automating signal interpretation, enhancing image-based diagnostics, and supporting early contamination prediction. AI-based systems have proved 96.4–104% recovery and 100% bacterial capture ability. Embedding AI methods in a wet lab demands technical and logical modeling, as well as learning and calibration decorum. Nonetheless, AI readiness and full-scale application for meat-borne pathogens surveillance are on the way. Furthermore, additional focus is aligned on meat-borne bacterial pathogen genomic databases, i.e., (NCBI Pathogen Detection, EnteroBase, VFDB, ComBase, and GenBank), which serve as critical training resources for AI models for outbreak tracking, virulence profiling, and antimicrobial resistance (AMR) prediction. By integrating molecular methods, genomic surveillance, and AI-driven analytics, this review presents a framework for strengthening meat safety systems. This will improve early detection capabilities and support data-driven public health interventions in the future.

Goat milk is traditionally known for its nutritional value, greater digestibility, and cultural significance. More recently, goat milk has been explored as a promising natural source of novel bioactive peptides (BAPs) with significant biological activity. BAPs derived from goat milk have emerged as high potential candidates for their therapeutic and health-promoting attributes owing to their diverse functionality as natural antioxidants, antihypertensives, antimicrobials, antidiabetics, anti-hypercholesterolemic, anticancer, as well as immunomodulatory therapeutic aids. The integration of powerful computational biology approaches, such as machine learning and artificial intelligence, with the research in the domains of bioinformatics and food sciences has greatly accelerated and improved the discovery pipelines for milk protein-derived BAPs. This, in turn, has enabled rapid and cost-effective in silico identification, high-throughput screening, and functional prediction of these BAPs prior to being subjected to experimental validation and testing through conventional in vitro/in vivo methods. The utilization of various in silico techniques, such as bioactivity relationship modeling tools, molecular docking, and design of experiment (DoE) approaches for optimizing production, identification, and performance in biological systems, as well as future potential are investigated in the current review.