Comprehensive Reviews in Food Science and Food Safety最新文献

The intestinal barrier maintains host health through the coordinated integration of mechanical, chemical, immune, and microbial defenses across intestinal segments. Dietary factors are key regulators of barrier integrity, yet the mechanisms by which nutrients act during digestion, absorption, and microbial metabolism from the small intestine to the colon remain incompletely understood, with segment-specific functions often overlooked. This review summarizes recent advances and elucidates how dietary proteins, carbohydrates, lipids, micronutrients, and bioactive compounds shape intestinal barrier function through “nutrient–gut–host” interactions, providing theoretical support for precision nutrition. Specifically, in the small intestine, preclinical evidence demonstrates that dietary proteins, including milk and soy proteins, upregulate tight junction (TJ) expression and support epithelial repair. Rapidly absorbed carbohydrates modulate small-intestinal barrier function via glycemic regulation and incretin signaling, whereas lipids—particularly saturated versus polyunsaturated fatty acids—differentially affect mucosal inflammation and epithelial protection through absorption pathways and chylomicron trafficking. In the colon, fermentable carbohydrates, such as inulin, and fructooligosaccharides promote microbial fermentation and short-chain fatty acid (SCFA) production—especially butyrate—which strengthens epithelial integrity, stimulates mucus secretion, and regulates immune responses. Micronutrients, including zinc, iron, and vitamins, maintain barrier structure, microbial balance, and immune signaling across both intestinal segments, whereas bioactive compounds, such as probiotics, prebiotics, and postbiotics, enhance colonic barrier integrity by upregulating TJs, stimulating mucus secretion, and supporting beneficial microbes. Finally, this review emphasizes the need to assess coordinated dietary effects along the entire small-to-large intestine continuum to better guide targeted nutritional strategies for human health.

Nitrite (NO₂⁻), a critical inorganic nitrogenous compound, has extensive applications in the food industry. However, its excessive intake poses significant health and ecological risks. Therefore, the accurate quantitative detection of NO₂⁻ is of great importance for both scientific research and practical applications. In recent years, optical sensing technology has emerged as a key tool for NO₂⁻ detection in food samples, due to its high sensitivity, rapid response, and user-friendly operation. This paper provides a systematic review of the detection principles underlying typical optical sensing techniques, including ultraviolet–visible absorption spectroscopy/colorimetry, fluorescence spectroscopy, surface-enhanced Raman spectroscopy, and chemiluminescence/electrochemiluminescence. It highlights recent advancements in NO₂⁻ analysis using these techniques, both individually and in multimodal. Despite their many advantages, current optical sensing techniques for NO₂⁻ face several practical challenges, such as limited anti-interference capabilities, difficulties in synergistically optimizing sensitivity and selectivity, and issues with real-sample applicability. This paper explores potential solutions to these challenges in-depth and outlines the primary directions for future research. Optical sensing technology has made important breakthroughs in the field of nitrite detection, exhibiting considerable potential for application. In view of the existing technical problems and practical application challenges, it is urgent to deepen the research, so as to provide more reliable technical support for food quality monitoring.

Starch inclusion complexes (ICs) have a crucial influence on food processing and human health by reducing the rate of enzymatic digestion and maintaining glucose homeostasis, offering benefits for individuals suffering from type 2 diabetes. Therefore, starch ICs have become a promising approach in the fields of food science and pharmacology. This review explores the formation and structure of starch ICs and provides a comprehensive comparison of various composite methods in making starch ICs in terms of efficiency, cost, target material types, and application scope. It also discusses multidimensional characterization techniques that assess the multiscale structure, thermal properties, and digestibility of starch ICs. Finally, insights are raised for the future research direction of starch ICs, highlighting the need for improved orderliness and controllability in the final product structure of starch ICs, which remains an area needing further exploration.

Sweet aftertaste (SA) is a unique taste phenomenon, specifically referring to a long-lasting, pleasant sweetness and comfort in the mouth after consuming certain foods. It has garnered increasing attention as a key driver of innovation in the food industry. However, the mechanisms underlying its formation and the specific material basis responsible for its perception remain inadequately elucidated, impeding the development of foods with an SA. This comprehensive review systematically examines the natural sources and material basis of SA, critically evaluates existing theoretical explanations, and proposes a novel dynamic synergy theory to describe the formation mechanism and enhancement principles of SA. Furthermore, the impact of food processing and manufacturing technologies on the perception and intensity of SA is thoroughly discussed. By integrating emerging technologies such as artificial intelligence and biological taste sensors, this article advances the concept of establishing objective–subjective integrated flavor evaluation approaches tailored to SA. The health implications and current challenges associated with this sensory phenomenon are also analyzed. Finally, grounded in the dynamic synergy theory, this article proposes innovative food design strategies aimed at achieving dual “SA-function” benefits, providing a forward-looking reference for future research and industrial applications in functional and sensory-oriented food design.

The color of food plays a crucial role in purchasing decisions, as consumers frequently associate its appearance with quality. Browning reactions often damage food color, reducing visual appeal and consumer acceptance. Currently, comprehensive understanding has focused on enzymatic browning and nonenzymatic Maillard reactions, whereas complex photooxidation browning (POB) remains inadequately and systematically understood. This review systematically examines the mechanism of POB and its impact on food quality by synthesizing existing literature. We comprehensively analyze the substrates involved, reaction mechanisms, and key influencing factors in POB. Furthermore, we present an updated overview of current inhibition strategies and propose preventive measures to mitigate the POB of food products. POB involves a variety of chemical substrates, including but not limited to nitrogen-containing compounds, polyphenolic compounds, unsaturated lipids, and phytosterols. These substrates primarily undergo POB through three pathways: singlet oxygen-mediated oxidation, free radical chain reactions, and direct photolysis. However, reaction conditions—including light intensity, wavelength, oxygen concentration, photosensitizers, temperature, pH, and packaging materials—significantly impact POB. With these factors in mind, proposed strategies include dynamic light wavelength adjustment, modified atmosphere packaging, antioxidants incorporation, light-blocking materials, nanotechnology applications, and multilayer barrier films with nonmigratory active components. Not only does this review provide readers with a comprehensive understanding of POB, but it also offers actionable directions for future research, which is of vital importance to deal with the increasingly significant impact of light in the food industry.

The integration of intelligent sensing, artificial intelligence (AI), and control technologies is reshaping traditional winemaking into a more data-driven and responsive process. This review proposes the concept of an Intelligent Oenological System (IOS) and critically reviews recent developments in smart winemaking technologies, with a focus on technologies involving Internet of Things (IoT) devices, sensor networks, and AI-based modeling. Key innovations include the deployment of multimodal sensors for real-time monitoring of temperature, pH, dissolved oxygen, spectral signals, and gas evolution across stages from grape maceration to wine aging. Advances in machine learning and digital twin models support predictive control of fermentation, whereas reinforcement learning and inverse design frameworks enable data-informed decision-making toward specific flavor targets. These technologies contribute to improved process precision, reduced resource consumption, and greater product consistency. Nevertheless, challenges, such as data interoperability, model interpretability, and system integration, continue to limit widespread adoption. Emerging solutions involving edge computing and standardized data ontologies are helping to address these issues. By aligning intelligent winemaking with sustainability, traceability, and personalized flavor goals, this review highlights its transformative potential for the future of the wine industry.

Over the past few decades, significant progress has been made in the research of polysaccharides extracted from natural resources, which are often used in functional foods, medicines, cosmetics, and biomedical materials. However, traditional research heavily relies on trial-and-error screening, which is limited by challenges in elucidating structure–function relationships, low preparation efficiency, and poor application adaptability. The integration of artificial intelligence (AI) has provided a critical pathway to overcome these constraints. This review outlines recent AI applications in polysaccharide research, discusses current challenges, and identifies future trends. For polysaccharide extraction, AI employs models such as artificial neural networks and genetic algorithm-backpropagation to optimize processing conditions. Its prediction accuracy often reaches above 0.95, significantly higher than the 0.7–0.8 of traditional response surface methodology models. In practical applications, AI integrates multi-omics data to support personalized polysaccharide scheme design. For instance, graph convolutional networks can correlate structural features with biological activities (e.g., tumor cell inhibition rates and immune cell activation), thereby promoting the development of personalized functional products. However, the field still faces challenges such as inconsistent data quality, limited model interpretability, and difficulties in cross-disciplinary collaboration. Solving these problems is key to advancing AI from a supportive tool to a central driver of innovation in polysaccharide research, with potential impacts on precision medicine, functional foods, and advanced biomaterials.

The global rise in the older adults highlights an urgent need for nutritional strategies capable of mitigating age-related declines in protein digestion and absorption. Simply increasing dietary protein intake is insufficient without improving protein bioavailability of protein in the elderly gastrointestinal tract is not addressed. This review critically evaluates the field and identifies several key findings to guide future studies and product development. First, protein sources and food matrices must be aligned with age-related physiological changes, including reduced digestive enzyme activity, diminished gastrointestinal motility, and alterations in gut microbiota composition. Liquid and semi-solid foods are more suitable for older adults with chewing or swallowing difficulties, and animal proteins generally exhibit higher digestibility than plant proteins in them. Second, emerging food-processing technologies, such as 3D printing, drying, extrusion, and homogenization, show substantial potential for improving protein bioavailability, particularly when combined with personalized formulation strategies. Third, elderly in vitro digestion models currently represent the practically valuable tools for evaluating protein bioaccessibility. Finally, successful translation into practice will require overcoming several key challenges, including the development of high-quality geriatric-specific datasets to support artificial intelligence (AI)-based predictive tools and the establishment of unified bioavailability assessment criteria and labeling frameworks. Collectively, these insights provide a conceptual roadmap to guide the development of scientifically grounded, personalized protein products for older adults.