Current Research in Food Science最新文献

The overuse of antibiotics in food production systems has exacerbated antibiotic resistance in foodborne pathogens, creating significant risks to public health and food safety. This situation makes it imperative to develop practical monitoring tools for tracking antibiotic resistance in foodborne pathogens. However, most existing detection technologies still rely on manual operation and are time-consuming and operationally complex. This prevents them from being used for immediate on-site testing along the food processing chain. Phenotypic detection establishes the functional expression of antibiotic resistance, while genotypic detection elucidates its genetic basis. Through complementary functional validation and mechanism analysis, both approaches collectively provide indispensable evidence for the precise and comprehensive assessment of antibiotic resistance. This review aims to provide concrete solutions for rapid detection by systematically integrating genotypic and phenotypic strategies, while clarifying future research directions for antibiotic resistance detection approaches. This review systematically discusses the diversity of antibiotic resistance, analyzes the advantages and limitations of traditional detection methods, and elaborates in detail on various rapid detection techniques for antibiotic resistance based on genotypes and phenotypes. This review finally summarizes the development trends of emerging technologies and proposes improvement suggestions. Future antibiotic resistance detection technologies will focus on simultaneous analysis of genotypes and phenotypes and move toward fully automated, intelligent, integrated analysis. Ultimately, this review provides robust safeguards for food safety and helps establish a secure, controllable food processing chain.

The microbiota-gut-brain axis (MGBA) is increasingly recognized as a key target for ameliorating major depressive disorder (MDD). This review systematically synthesizes evidence on the bidirectional relationship between gut microbiota dysbiosis and MDD, and delineates the core mechanisms-such as neuroinflammation, neurotransmitter metabolism, and hypothalamic-pituitary-adrenal (HPA) axis dysregulation-through which this axis influences depressive pathogenesis. Further, the intestinal microbiota characteristics related to MDD, the main regulatory pathways, and the potential efficacy of microbiome-targeted intervention measures-including psychobiotics, prebiotics, fecal microbiota transplantation (FMT), and dietary strategies-were sorted out. In the clinical assessment and drug research of depression, the assessment tools are mainly divided into two categories: clinician-rated and self-reported. These two types are often used together to provide multi-dimensional evidence of therapeutic efficacy. Evidence suggests that stress-related intestinal permeability may initiate gut dysbiosis, which in turn can impair barrier function, promote neuroinflammation, disrupt neurotransmitter synthesis, and overactivate the HPA axis, potentially exacerbating depressive symptoms. Interventions targeting the gut microbiota may help reshape microbial communities, increase short-chain fatty acids (SCFAs) and 5-Hydroxytryptamine (5-HT), and dampen inflammatory and stress responses, thereby offering a promising, non-pharmacological avenue for alleviating MDD. This review not only offers a theoretical foundation for microbiota-based therapeutics in MDD but also highlights pathways toward developing safe, effective non-pharmacological strategies for depression management.

Gut microbial colonization is a dynamic balance shaped by host genetics and immunity, microbial ecology, and environmental exposures. This review synthesizes evidence on host barriers and immunity-mucus architecture, antimicrobial peptides, pattern recognition receptors, and secretory IgA-and on genetic loci such as LCT and ABO/FUT2 that modulate nutrient landscapes and strain selection. Microbial adaptability is summarized, including polysaccharide utilization loci and human milk oligosaccharide metabolism, bile salt hydrolase-mediated tolerance, extracellular polysaccharide-driven immune modulation, oxygen-gradient-linked metabolic partitioning, and adhesion mechanisms that secure niche occupancy. Environmental perturbations are evaluated, spanning dietary patterns, protein sources, polyphenols, food additives, pharmaceuticals, and lifestyle factors such as physical activity, circadian alignment, and smoking, which reshape resource competition, barrier integrity, and community resilience. Interaction frameworks that govern stability and dysbiosis are delineated, including competitive inhibition, cross-feeding, quorum sensing, cross-kingdom crosstalk among bacteria, fungi, and phages, and horizontal gene transfer that accelerates adaptation and resistance. Niche elasticity is proposed as a systems metric to quantify stability and recovery after perturbation. Translational strategies combine engineered probiotics, anti-adhesion approaches, and rationally designed phages and lysins with in situ multi-omics to enable mechanism-guided, personalized interventions for food science and microbial engineering.

[This corrects the article DOI: 10.1016/j.crfs.2021.10.008.].

The review focuses on the application of inorganic nanomaterial-based electrochemical sensors in heavy metal detection in food, systematically outlining their technical principles, performance challenges, and development prospects. Heavy metals are among the most concerning food contaminants due to their high toxicity at trace levels, persistence, and bioaccumulation, creating an urgent need for rapid and reliable screening in complex food matrices. Electrochemical sensors-particularly stripping voltammetry-based platforms-offer high sensitivity, low detection limits, and fast response, but their practical performance is frequently constrained by matrix-induced fouling, ion interference, and inconsistent validation protocols. This review focuses on inorganic nanomaterial-engineered working electrodes (metals, metal oxides, carbon nanomaterials, and their composites) for food-related heavy-metal analysis, and provides a food-matrix-oriented structure-function-performance framework that links material roles (selective preconcentration, catalytic signal amplification, and antifouling interfaces) to analytical outcomes (sensitivity, selectivity, stability, and recovery). Representative electrode fabrication routes and commonly used testing settings are summarized together with essential material/electrode characterizations and electrochemical diagnostics. Importantly, we derive actionable conclusions for food-safety deployment: accurate quantification in real foods is dominated by antifouling and selective enrichment rather than sensitivity alone; performance claims should be benchmarked by standardized sample pretreatment, interference panels, and recovery in representative foods; and electrochemical results require cross-validation against reference methods (e.g., inductively coupled plasma mass spectrometry (ICP-MS)/atomic absorption spectroscopy (AAS)). Finally, we propose a practical reporting checklist and highlight scalable pathways toward portable, multiplexed, and regulation-aligned heavy-metal monitoring in diverse food products.