Metabolomics has become an essential analytical tool for evaluating the safety, quality, and nutritional value of plant-based foods by profiling of small molecules. Through the analysis of metabolites, researchers can obtain important information regarding the nutritional composition, quality, and safety. The significance of this technology lies in its capacity to tackle key issues in food safety and quality, such as identifying and detecting contamination, verifying authenticity, and assessing sensory characteristics. This review provides a comprehensive analysis of the principles of metabolomics, with a specific emphasis on its applications in plant-based food products. This review examines the analytical techniques commonly utilized in metabolomics, including mass spectrometry, nuclear magnetic resonance, and molecular vibrational spectroscopy. Recent developments in the field of metabolomics have shown a growing trend towards integrating metabolomics with other -omics technologies, as well as utilizing machine learning techniques to effectively analyze complex datasets and identify new biomarkers. The multi-omics integration frameworks combine metabolomics with genomic, transcriptomic, and proteomic datasets to provide deeper understanding of food composition, contamination pathways, and quality attributes. The data complexity, metabolite instability, and gaps in metabolite databases possess challenges, however standardized workflows and the utilization of advanced analytical tools are promising solutions. The utilization of interdisciplinary methodologies addresses existing constraints, enabling the development of novel applications that align with the evolving market requirements.

Foodborne pathogens pose a serious threat to public health and present significant food safety risks worldwide. Sensitive and specific detection, combined with reliable signal interpretation and data analysis, is a key step in ensuring the effective identification of foodborne pathogens. Surface-enhanced Raman scattering (SERS) has attracted considerable attention due to its ability to provide fingerprint information and its rapid and non-destructive analytical capabilities. This review aims to provide an updated discussion of this field of research by highlighting the latest findings on SERS sensors mediated by bio-recognition elements (BREs) for foodborne pathogens. It provides a systematic overview of the basic principles and common types of SERS sensors. We have classified and discussed the design principles and practical examples of SERS sensors based on specific and non-specific recognition elements, as well as their synergistic recognition. The applications cover the latest innovations in the detection of individual species and the simultaneous identification of multiple pathogens in food samples. Future efforts aimed at improving the sensitivity, specificity, stability, reproducibility, and practicality of BREs-based SERS sensors are also discussed. This review aims to provide timely and valuable insights into the design and application of high-performance SERS biosensors for rapid food safety assessment.

This study presents a comprehensive evaluation of machine learning approaches for surface-enhanced Raman spectroscopy (SERS)-based prediction of patulin (PAT) concentration in apples, a critical food safety concern due to its toxic effects on health and limitations of conventional laboratory-based detection methods. Three base algorithms, extreme learning machine (ELM), random forest (RF), and support vector machine (SVM), were implemented in combination with three variable selection methods: Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Genetic Algorithm (GA). The investigation focused on optimizing predictive accuracy while reducing computational complexity through effective variable selection. Results demonstrated that SVM-based methods achieved superior performance. UVE-SVM and GA-SVM exhibited exceptional predictive capabilities with Residual Predictive Deviation (RPD) values of 4.0487 and 4.3036, respectively, and outstanding calibration (Rc > 0.99) and prediction (Rp > 0.97) coefficients. This study provided valuable insights for optimizing SERS spectral analysis in food safety applications, recommending UVE-SVM or GA-SVM implementations for the highest accuracy in PAT concentration prediction while emphasizing the crucial role of variable selection in enhancing model performance.

Honey-bee products offer a broad spectrum of uses in food applications. Bee products, are regarded as excellent sources of high-quality nutrients, coming directly from nature with no need for artificial additives. This notion, in addition to their functional properties, makes them idealsuper foods or sources of supplements with beneficial effects on human health. This review covers the quality and safety aspects of daily usage of bee products, in addition to addressing the hazardous contaminants that may occur in bee products. Extensive studies on the applications of bee products in food preservation report their ability to extend the shelf life of food products due to their antioxidant and antimicrobial activity. Additionally, the levels of chemical contaminants in honey and bee products appear to be very low. Honey and bee products do not pose any threat to human health. © 2026 Society of Chemical Industry.

Foodborne pathogens pose persistent threats to global health and food security,necessitating rapid and non-destructive detection technologies compatible with irregular food surfaces.Conventional rigid surface-enhanced Raman scattering(SERS)substrates struggle with poor conformal contact and sampling inefficiency in real-world applications.This review highlights the transformative role of flexible SERS sensors,which combine mechanical adaptability with plasmonic enhancement to enable in situ pathogen detection on complex food matrices.We systematically analyze advances from 2020 to 2025,focusing on three parts:(1)flexible sensing strategies integrating label-free fingerprinting and specific recognition elements to enhance specificity in complex food matrices;(2)flexible substrate designs using natural/synthetic polymers and hybrid composites to balance optical performance and durability;and(3)conformal sampling methods enabling effective pathogen capture.Critical challenges in sensitivity-stability trade-offs,field-portable integration,and spectral reproducibility are being addressed through emerging solutions such as machine learning-assisted calibration and self-cleaning interface prototypes.By bridging material innovation with practical deployment needs,flexible SERS platforms demonstrate practical potential for decentralized food safety monitoring.Future progress hinges on scalable fabrication techniques and AI-driven systems integrating machine learning for predictive monitoring,where real-time pathogen detection synergizes with blockchain-enabled traceability to enable proactive risk management throughout supply chains.

Sidr honey is a natural product, known to be recognized for its significant health-promoting properties and thus in high demand in the global market. Many users believe in its benefits to cure diseases and in its peculiar role in wound healing. Globally, Sidr honey is recognized as one of the most uncommon and high-priced honeys derived from the Sidr tree, Ziziphus spina-christi L. (family: Rhamnaceae). The chemical and physical characteristics of Sidr honey, as well as its nutritional value and biological impact, are discussed in this review article, as well as its use in nanotechnology and its patents. Like other types of honey, Sidr honey is influenced by its botanical origin, climatic conditions, and geographic environment. Due to its composition of simple sugars as well as bioactive ingredients such as minerals, phenolic compounds, vitamins, and enzymes, Sidr honey may be a promising natural sweetener. Antimicrobial, antioxidant, anticancer, and anti-diabetic properties are all enhanced through the administration of Sidr honey.

Array-based sensing platforms have advanced rapidly in recent years, offering strong potential for high-throughout and multiplexed detection. However, their practical translation in food safety remains constrained by insufficient selectivity in complex matrices. Here, we report a synergistic sensing strategy that integrates specific and cross-reactive recognition to construct a high-performance sensor array for rapid and accurate analysis. A genus-specific aptamer is employed as a front-end module to selectively capture and pre-enrich , enabling bulk concentration quantification and effective suppression of non- interference prior to array-based discrimination. Strain-level differentiation is subsequently achieved by exploiting the cross-reactivity of a multivalent antibody targeting O157:H7 across related strains. To further amplify interstrain differences, boronate chemistry is incorporated through 4-mercaptophenylboronic acid and cysteamine, providing differential binding to cis-diol motifs on bacterial surface carbohydrates. The resulting sensor array generates multidimensional response fingerprints and enables the simultaneous identification of representative strains and complex mixtures within 1 h. Accurate bulk quantification and robust discrimination are demonstrated across a wide concentration range in buffer and in complex food matrices, including beef and milk. By benchmarking four machine-learning algorithms, the K-nearest neighbors (KNN) model delivered optimal performance, achieving 100% classification accuracy. Collectively, this work establishes a versatile and translatable sensing platform for rapid, precise detection of foodborne pathogenic , addressing a critical challenge in real-world food safety monitoring.

Array-based sensing platforms have advanced rapidly in recent years, offering strong potential for high-throughout and multiplexed detection. However, their practical translation in food safety remains constrained by insufficient selectivity in complex matrices. Here, we report a synergistic sensing strategy that integrates specific and cross-reactive recognition to construct a high-performance sensor array for rapid and accurate Escherichia coli analysis. A genus-specific aptamer is employed as a front-end module to selectively capture and pre-enrich E. coli, enabling bulk concentration quantification and effective suppression of non-E. coli interference prior to array-based discrimination. Strain-level differentiation is subsequently achieved by exploiting the cross-reactivity of a multivalent antibody targeting E. coli O157:H7 across related strains. To further amplify interstrain differences, boronate chemistry is incorporated through 4-mercaptophenylboronic acid and cysteamine, providing differential binding to cis-diol motifs on bacterial surface carbohydrates. The resulting sensor array generates multidimensional response fingerprints and enables the simultaneous identification of representative E. coli strains and complex mixtures within 1 h. Accurate bulk quantification and robust discrimination are demonstrated across a wide concentration range in buffer and in complex food matrices, including beef and milk. By benchmarking four machine-learning algorithms, the K-nearest neighbors (KNN) model delivered optimal performance, achieving 100% classification accuracy. Collectively, this work establishes a versatile and translatable sensing platform for rapid, precise detection of foodborne pathogenic E. coli, addressing a critical challenge in real-world food safety monitoring.

Electrochemical surface-enhanced Raman spectroscopy (EC-SERS) exploits the molecular specificity of Raman spectroscopy and controllable manipulation offered by electrochemical techniques. This integration enables selective enrichment of electrochemically active molecules at the electrode surface, which simultaneously serves as a nanoparticle-based SERS substrate. The enhanced Raman signal from target molecules located within plasmonic hotspots enables accurate analysis. EC-SERS applications in food analysis are emerging with research expanding to a wider range of contaminants. This review addresses a key gap in EC-SERS by combining analyte-electrode interactions, matrix interferences, and electrical double-layer modulation within a system, which directly connects the interfacial reaction to analytical performance in complex food matrices. To date, this technique has been primarily employed for detecting electroactive molecules, including pesticides, veterinary drugs, and phenolic compounds, in simple matrices such as tap water, juices, fruits, and vegetables. However, the inherent complexity of food matrices presents significant challenges, including electrode fouling, matrix interference, and modulation of the electrical double layer owing to the high salt content. Effective mitigation strategies require improved substrate and electrode designs, selective surface functionalization, and optimized pre-concentration approaches. Future research should focus on enhancing portability and validating the performance in real food samples to facilitate adoption across the supply chain.

Yam possesses significant nutritional and medicinal properties that have positioned it as an important topic in functional food research. Developing scientific processing evaluation systems and detection technologies for yam's internal composition are essential for enhancing standardization and market competitiveness of yambased products. This study was conducted in three sequential phases: initially, the optimal yam variety for steaming processing was identified from four candidate cultivars (Wencheng yam, Crispy yam, Hemp yam, and Iron Stick yam) through comparative evaluation. Subsequently, the correlations between steaming processing properties and compositional characteristics of raw materials were systematically analyzed. Finally, nondestructive detection technology was employed to enable rapid quantification of yam compositional components, thus establishing a technical foundation for standardized yam product development. Among the four yam varieties, Wencheng yam was determined to be the most suitable for steaming processing through sensory evaluation and analytic hierarchy process (AHP). Near-infrared spectroscopy combined with an enhanced Competitive Adaptive Reweighted Sampling-Support Vector Regression (CARS-SVR) algorithm enabled precise detection of four physicochemical indicators. The CARS-SVR model demonstrated superior performance for individual components, achieving Rp2= 0.921 (RMSEP=4.344) for amylopectin and RP2= 0.900 (RMSEP=0.924) for amylose detection. This study offers an efficient detection technology for yam industry modernization.

Pesticides have been used to manage pests and diseases, but their extensive usage could pose environmental and health challenges. This review provides a comprehensive overview of the threats stemming from pesticide contamination within agricultural ecosystems. This examination illuminates the harmful effects of pesticides on non-target organisms and environmental sustainability, highlighting the critical demand for effective and green agricultural practices. By scrutinizing the complexities of pesticide contamination, this review aims to shed light on the issues that threaten biodiversity and ecosystem resilience. The collection of literature data relies on screening databases, including Google Scholar, PubMed, ScienceDirect, and Scifinder. The keywords of the search were a combination of words such as “pesticides”, “contamination", " toxicity", " organisms", "bioremediation ", “nanotechnology”, “safety”, and “nano-pesticides”. Through a detailed examination of current research and practices, the review seeks to offer a nuanced understanding of the extent of the problem and the potential risks associated with pesticide use in agroecosystems. Furthermore, the review delves into innovative approaches and emerging technologies that show promise in combating pesticide contamination. These solutions range from novel application methods to the development of alternative pest management strategies that prioritize environmental sustainability and long-term ecosystem health. Ultimately, this review serves as a reference for policymakers, researchers, and practitioners in the agricultural sector, offering insights into the complexities of pesticide contamination and highlighting pathways towards more sustainable and eco-friendly agricultural practices.

The transition toward sustainable active packaging demands eco-friendly and non-toxic biomaterials with high functional performance. Natural biopolymer-based films are often limited by the inherent hydrophilicity and insufficient functional activity of their constituent materials, while the tendency of plant proteins to aggregate may further compromise the structural and functional properties of protein-polysaccharide films. A bioactive sodium alginate (SA)-based composite film was fabricated by incorporating modified rice residue protein (RRP) (MP1: single pH-shifted RRP, MP2: ultrasound-assisted pH-shifted RRP), and its application potential in the postharvest preservation of blueberries was further evaluated. The optimized SA/MP2 film exhibited excellent surface hydrophobicity with a water contact angle of 102.10° (pure SA: 69.6°), 8.9% reduced water vapor permeability, augmented thermal stability, and enhanced mechanical properties, as well as improved UV–vis barrier characteristics. Additionally, the DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging capacity of the SA/MP2 film was determined to be 32.89% compared with 20.13% observed for the neat SA film. Afterward, the SA/MP2–5% film effectively extended the shelf life of blueberries by reducing the weight loss and decay rate, while maintaining fruit hardness and soluble solids content. The microflora analysis revealed that beneficial epiphytic yeast genera within the blueberry fungal community under MP2 intervention were predominantly Kondoa (16%), Symmetrospora (8%), and Aureobasidium (2%). These findings indicated that the developed multifunctional active film has great application potential for sustainable food packaging, and provides a promising strategy for high-value utilization of agricultural waste.

Despite substantial progress in biosensor development, achieving reliable sensitivity and selectivity under real-world conditions remains challenging, particularly in complex and heterogeneous sample matrices. While sensitivity has historically been the primary focus of biosensor optimization, selectivity often emerges as a limiting factor for practical performance when deployed outside controlled laboratory environments. Poor selectivity can lead to false-positive or false-negative results, thereby undermining the reliability and accuracy of biosensing platforms. A major contributor to this problem is nonspecific binding, the unintended interaction between biosensor and nontarget species. However, the origins, mechanism, and implications of nonspecific binding remain insufficiently understood and are still actively debated within the scientific community. In this review, we trace the conceptual development of nonspecific binding and critically examine its physicochemical origins in substrates and biorecognition elements. We then assess recent progress in recognition elements, such as antibodies, aptamers, and enzymes, emphasizing not only their strengths but also their limitations and vulnerability to off-target interactions. To mitigate nonspecific binding, we summarize a range of emerging strategies, including optimizing the conjugation and orientation and increasing binding site accessibility and density through structural design, removing interfering species, and implementing signal-level strategies. Finally, we outline persisting challenges and future directions for enhancing biosensor selectivity. Collectively, these insights offer a roadmap for designing next-generation biosensors with high accuracy, robust selectivity, and real-world applicability.

Spectroscopic techniques provide rapid, non-destructive means for fruit and vegetable quality assessment, yet the biological complexity of fruits and vegetables introduces high spectral variability and limits conventional model robustness. This review systematically examines the transition from traditional spectroscopic modeling to intelligent analysis strategies for enhanced robustness in fruit and vegetable quality detection. The fundamental optical properties of fruit and vegetable tissues are first analyzed, highlighting how cellular structures and chemical compositions contribute to spectral complexity. Subsequently, the limitations of conventional modeling approaches are critically evaluated when applied to complex biological systems, particularly their susceptibility to overfitting and poor cross-scenario performance. The core contribution focuses on intelligent modeling strategies that address these limitations through advanced machine learning techniques including deep learning architectures, multi-modal fusion approaches, transfer learning and ensemble methods. Additionally, applications across various fruit and vegetable categories demonstrate the practical effectiveness and performance improvements achieved through these intelligent strategies, with future research directions focusing on model interpretability and sustainable deployment strategies for real-world applications. This review provides essential guidance for developing dependable spectroscopic quality assessment frameworks within intelligent food processing systems.

Chitosan-based eco-friendly packaging materials present a promising route to alleviate mitigating greenhouse gas emissions and global climate change by replacing traditional petroleum-based packaging materials used for food preservation. Nevertheless, the practical application of chitosan-based films is hindered by insufficient antimicrobial activity and susceptible to breakage. In this study, a covalent organic framework (TpPa-SO3H) was synthesized at room temperature and exhibited inhibitory efficacies of 90.92% and 92.53% against Escherichia coli and Staphylococcus aureus. Chitosan/TpPa-SO3H film was formed through a molecular weaving strategy, and its elongation at break and tensile strength were remarkably enhanced to 148% and 403%, respectively, influenced by the concentration of TpPa-SO3H and the molecular weight of chitosan. The composite film achieves exceptional oxygen and carbon dioxide barrier properties. The water vapor control ability of the film depends on the hydrophilicity of TpPa-SO3H, while the UV blocking ability is mainly attributed to UV light harvesting and photocatalysis activity of TpPa-SO3H. The shelf life of grapes, strawberries, and blueberries has doubled thanks to the preservation of the Chitosan/TpPa-SO3H film, while maintaining the postharvest quality and antioxidant activity of the fruits. Furthermore, the Chitosan/TpPa-SO3H film for 45 days underwent considerable degradation. All the contributors make chitosan film an eco-friendly packaging material for fruit preservation.

Bee bread is a rich source of bioactive compounds and, in particular, the polyphenols. This study aims to determine the physicochemical properties of Vicia faba L. bee bread for the first time, specifically its total protein content (TPC), flavonoid (TFC), sugars, and lipids. The TPC, TFC, total sugar, and total lipid contents were 20.52 ± 0.94%, 57.39 ± 2.8 mg/100g, 38.32 ± 1.18%, and 2.47 ± 0.64%, respectively. Using both mass spectrometry (LC-MS/MS) and headspace solid-phase micro-extraction gas chromatography-mass spectrometry, the non-volatile and volatile compounds were identified. The antioxidant property of bee bread was evaluated using the DPPH assay. 38 Compounds were identified using LC-MS/MS analysis. Alcohols, alkanes, fatty acids, acetates, ketones, aromatic compounds, nitrile compounds, and dipeptides were among the 33 volatile compounds that were found. The antioxidant efficacy of beebread, as determined by DPPH assay, exhibited an IC50 value of 79.95 ± 5.43 µg/ml. This antioxidant activity is attributed to the presence of bioactive constituents, phenolic and flavonoids.

Background Rapid screening of pesticide residues directly on fruit surfaces is essential for food safety monitoring. However, conventional analytical methods usually require complicated sample pretreatment. This limitation restricts their practical use in in-situ residue analysis. Moreover, the non-planar morphology of fruit surfaces requires sensing platforms with good conformability. Results In this study, a low-cost flexible surface-enhanced Raman scattering (SERS) sensor was developed for thiabendazole (TBZ) residue detection on apple. Bimetallic Au@Ag core-shell nanocubes (Au@AgNCs) were immobilized onto inexpensive cellulose filter paper (CFP) through a simple polyelectrolyte-mediated layer-by-layer self-assembly strategy. The sharp corners of Au@AgNCs provided abundant plasmonic hotspots and strong Raman enhancement. The polyelectrolyte layers improved nanoparticle loading uniformity and substrate stability. The optimized sensor achieved an enhancement factor of 5.72 × 105 and showed good signal reproducibility. For in-situ detection of TBZ residues on apple, the sensor exhibited a linear response over 0.5–50 mg/L, with a correlation coefficient of 0.993 and a detection limit of 0.30 mg/L. The quantitative results were further validated by high-performance liquid chromatography (HPLC), confirming the reliability of the proposed sensor for real-sample analysis. Significance and Novelty Overall, the Au@AgNCs/CFP/PSS flexible SERS sensor provides a low-cost and sensitive platform for in-situ detection of pesticide residues on irregular fruit samples.

Staphylococcus aureus (S. aureus) is a major foodborne pathogen that poses a severe threat to public health, necessitating the development of rapid and sensitive detection methods. Herein, we developed a flexible surface-enhanced Raman scattering (SERS) aptasensor for the sensitive and specific detection of S. aureus. The sensor employs a single-aptamer-mediated sandwich-like recognition configuration: a polyimide/single-walled carbon nanotube-Au nanoparticle (PI/SWCNT-AuNP) film serves as the capture substrate, while 4-mercaptobenzoic acid (4-MBA)-labeled Au double-gap nanoparticles@Ag (AuDGNMBA@Ag) function as the signal probes. This configuration is proposed to enable sequential aptamer-mediated association of the target bacteria with the capture substrate and signal probes, thereby allowing highly sensitive SERS quantitative analysis. Under optimal conditions, the aptasensor exhibited a wide linear range from 10–105 CFU/mL with an R2 of 0.995 and a low limit of detection (LOD) of 2.52 CFU/mL, with good signal uniformity and high specificity. Furthermore, the practical applicability of the sensor was validated using spiked pork samples, achieving recovery rates of 90.2%–104% with results consistent with standard microbial plate counting (p > 0.05). This flexible SERS platform provides a robust and portable solution for on-site food safety monitoring.

Strawberries are highly appreciated by consumers for their sensory attributes. However, their delicate tissue structure makes them prone to mechanical damages (e.g., bruising) during postharvest handling. These inconspicuous damages subsequently accelerate microbial infection and decay throughout transportation and storage. Therefore, non-destructive detection of bruising at an early stage is critical for the quality assurance of commercial strawberries. In this study, the feasibility of using hyperspectral imaging (HSI) for the rapid detection of early bruise for strawberry was investigated. The full wavelength-based (460-1030 nm) partial least squareslinear discriminant analysis (PLS-LDA) model was first established for the discrimination of strawberry bruise, obtaining an accuracy of 99.21 %. To improve the detection efficiency, three characteristic wavelengths were selected by each of RGB, subwindow permutation analysis (SPA) and random frog (RF). Comparable performance to the full wavelength-based model was obtained on the SPA and RF-based model. Subsequently, the automatic recognition of bruise loci was achieved by YOLOv5 model based on the synthetic images at the characteristic wavelengths, with F1 score over 93.03 %. The results demonstrated that HSI combined with characteristic wavelengths is a useful tool for the rapid detection of early bruise and YOLOv5 model enables the automatic identification of bruise loci for strawberry.