Comprehensive Reviews in Food Science and Food Safety最新文献

Chemical residues in food pose a global health challenge, which necessitates sensitive and reliable detection methods. Current techniques (e.g., chromatography and immunoassays) remain limited by operational complexity, insufficient sensitivity, and poor adaptability for on-site use. Click chemistry has emerged as a novel solution, offering rapid, specific, and ultrasensitive detection of food chemical residues, such as pesticides, antibiotics, and hormones—contaminants that pose significant health threats through chronic exposure. This review outlines the principles and classifications of click reactions, emphasizing their selectivity, mild reaction conditions, and modular design. Integrating biosensors enhances molecular recognition and signal amplification, enabling on-site screening in complex food matrices. We critically evaluate recent click chemistry–based sensors, detailing their design strategies (e.g., probe functionalization and nanomaterial engineering) and optimization via advanced transduction mechanisms. Key challenges—including matrix interference, reagent stability, and cost—are addressed through molecular imprinting, microfluidic miniaturization, and AI-driven system refinement. Future advancements will focus on integrating green chemistry to reduce environmental impact, developing multiplexed platforms for high-throughput detection, and expanding applications to emerging contaminants such as microplastics and biotoxins. These innovations promise to redefine food safety standards with scalable, next-generation analytical tools.

The increasing frequency of extreme weather events, such as heatwaves and heavy rainfall, attributed to climate change, has considerably escalated the multifaceted threat of mycotoxins to agricultural productivity, food security, and public health. This review comprehensively examines the effect of climate change on fungal growth dynamics, mycotoxin production, and exposure patterns. Central to this effect is the phenomenon of niche transition within fungal communities, marked by the expansion of the thermal tolerance of traditional toxigenic species and the emergence of novel thermotolerant fungi via cross-regional migration. In addition, climate change drives the regional migration of mycotoxins and promotes the emergence of “novel” and “masked” mycotoxins by altering substrate effects. Furthermore, climate-driven environmental stress may activate previously silent secondary metabolic gene clusters (or hidden gene clusters) by affecting the regulatory network and epigenetic mechanisms of fungi, thereby reshaping metabolic pathways and inducing differential expression of genes related to toxin synthesis, resulting in a surge in mycotoxin exposure. This review also evaluates current global mycotoxin exposure trends and critically analyzes the limitations of prevailing fungal growth and mycotoxin prediction models, which often fail to explain the nonlinear effects of climate variables. Ultimately, this review underscores the pressing need to redefine the mycotoxin risk framework and develop mechanistic information prediction tools tailored to climate change, thereby warning of new risks associated with global warming.

Approximately one-third of fresh food is wasted globally throughout the supply chain. Machine learning (ML), a key branch of artificial intelligence, enhances postharvest logistics and preservation of fresh food by enabling intelligent sensing, precise evaluation, and adaptive control. However, its application is challenged by data standardization, sensor limitations, product variability, and limited model generalizability. This review summarizes current advanced ML applications in the food supply chain, emphasizing their transformative potential for quality control. We explore ML's ability to integrate multi-omics data for deeper insights into molecular changes during transportation and storage, enabling the development and evaluation of management strategies. Practical applications in grading, sensor technology, and intelligent preservation materials are also evaluated. ML models, such as support vector machine (SVM) and convolutional neural network (CNN), enhance precise grading and quality prediction by analyzing sensory attributes and chemical composition. By capturing complex molecular interactions, ML enables innovative sensor surface design with enhanced sensitivity and specificity. ML-driven sensors further support real-time environmental monitoring, while intelligent packaging materials powered by ML maintain freshness and reduce spoilage through adaptive responses to internal conditions. To ensure model robustness and generalizability, appropriate validation strategies such as cross-validation and external validation are essential. Despite its substantial potential, the widespread adoption of ML still faces challenges, including limited varietal and regional generalization, decision-making transparency, computational demands, limited data availability, and algorithm selection. Addressing these issues is critical for achieving effective and sustainable ML integration in postharvest quality control systems.

Soy sauce (SS), a fermented condiment integral to various global cuisines, has undergone considerable technological advancements while preserving its traditional microbiological processes. This systematic review, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, synthesized findings from 181 peer-reviewed articles to examine trends in SS production and consumption. Our descriptive statistics and thematic analysis revealed five key focus areas: process optimization, microbial fermentation, safety, waste management, and evolving analytical technologies. Advances, such as metagenomics, synthetic biology, and enzyme engineering, have refined fermentation dynamics, improving flavor and production efficiency. Concurrently, sustainability-orientated innovations, including by-product bioconversion, low-sodium formulations, and traceable packaging, support both environmental goals and health-conscious consumption. The integration of multi-omics approaches (e.g., metabolomics, genomics, and transcriptomics) and high-resolution analytical tools (e.g., spectroscopy and sensor-based systems) has further strengthened quality control by enhancing authenticity, safety, and traceability. However, integrating traditional methods with emerging technologies such as precision fermentation, which facilitates targeted microbial control to improve product consistency, remains challenging due to microbial strain incompatibility, scalability issues, and the necessity to maintain cultural authenticity and sensory attributes. It is essential to implement scalable and sustainable solutions that improve microbial function while minimizing hazardous by-products and environmental effects. This review presents an integrated framework connecting five key thematic areas with the core pillars of sustainability: environment, economy, society, technology, and nutrition, offering a foundation for directing future research, policymaking, and industrial practices. Key priorities include the development of salt-tolerant microbial consortia, the valorization of fermentation by-products via circular economy strategies, and the standardization of sustainability certification criteria to facilitate practical implementation.

Staple root and tuber crops (SRTCs), including potatoes, cassava, sweet potatoes, yams, and taros, play vital roles in global nutrition. However, they present significant safety hazards, which poses threats to human health, have garnered widespread public concern, and consequently constrain the further development and utilization of SRTCs. This review systematically examines recent advances in quality and safety control for SRTCs, with a particular focus on the progress and challenges in detection methods and mitigation strategies for the associated hazards. It further proposes the establishment of an integrated farm-to-fork quality control system for SRTCs. This review identifies shared hazard profiles (endogenous toxins, heavy metals, pesticides, mycotoxins, acrylamide, microplastics) across SRTCs and highlights key strategies for mitigation. Significant advancements in rapid detection technologies (sensors, immunoassays, spectral imaging) offer enhanced sensitivity and field applicability compared to traditional methods. Crucially, establishing an integrated farm-to-fork quality control system, incorporating genetic improvement, optimized cultivation/postharvest practices, and innovative processing, is essential for comprehensive hazard reduction. Future research priorities include multiomics approaches, rhizosphere microbiome engineering, and Industry 4.0 integration to further enhance SRTCs safety and utilization. Our work aims to enhance the safety and quality of SRTCs, thereby promoting their broader development and utilization.

The growing demand for healthy diets and wellness has led to significant interest in the development of high-protein yogurts. Milk proteins, particularly caseins and whey proteins, are essential for human nutrition and play a critical role in the formation of the yogurt gel network during fermentation. Recent advances in rheological and microstructural analyses have enhanced our understanding of the complex dairy matrix, revealing its role in determining yogurt functionality. Despite progress, the structure–function relationship in high-protein yogurts remains underexplored, particularly regarding the effects of protein composition, mineral content, casein micelle hydration and voluminosity, culture type, temperature (during ingredient processing, milk heat treatment, and fermentation), pH, and processing methods. Moreover, the interactions between protein–protein, protein–mineral, and protein–lactose components in high-protein–low-fat systems and their impact on protein aggregation and network formation are not fully understood. These uncertainties highlight the need for further experimental research to elucidate the intricate dynamics at play. Given the rapidly evolving understanding of the dairy matrix and the increasing number of emerging studies in the field, further investigation into the factors influencing the production of high-protein yogurts, as next-generation quality dairy products, is warranted.

Cyclospora cayetanensis is a foodborne protozoan parasite that causes cyclosporiasis, a disease transmitted by the consumption of sporulated oocysts, often via contaminated produce. Since 2018, outbreaks traced back to domestically grown produce in the United States have raised growing concern. Despite its public health significance, research is hindered by methodological challenges, including inability to culture in vitro/in vivo and limited genomic characterization. This review examines current knowledge on its occurrence and transmission, detection methods, host–parasite interactions, genetics, and remediation strategies, while also evaluating use of surrogate organisms (Eimeria spp. and Cryptosporidium parvum) to address research gaps. Detection remains challenging due to low oocyst concentrations in environmental and food matrices, requiring highly sensitive molecular assays. Additionally, the lack of standardized sampling methods that are representative of an entire batch of produces further complicates reliable detection and surveillance efforts. The recently implemented Food and Drug Administration (FDA) Bacteriological Analytical Manual (BAM) Chapter 19c method has improved sensitivity but still requires refinement. Although Eimeria spp. offer insight into sporulation and environmental behaviors and C. parvum has supported methodological development, neither fully replicates C. cayetanensis biology; limiting the translatability of surrogate-based research. Remediation strategies are underexplored, though research on related coccidia suggests significant resistances to conventional sanitizers. Genomic advancements, including the identification of distinct C. cayetanensis lineages, provide useful insights, though gaps in genome assemblies limit phylogenetic and functional analyses. Emerging tools, such as metagenomics, single-cell sequencing, and AI-driven bioinformatics, may overcome persistent barriers. Addressing these challenges is essential for improving detection, risk assessment, and guiding policy, all of which will mitigate the public health burden of cyclosporiasis.