Shrimp quality deteriorates during storage, making the assessment of freshness vital in the food industry. In this study, a spoilage gas-responsive chip was fabricated to integrate hyperspectral imaging and Raman spectroscopy, enabling the construction of a multimodal nondestructive detection model based on a multi-level data fusion strategy. Three different levels of fusion strategies were analyzed and compared, namely raw-data spectrum fusion, refined-feature spectrum fusion, and cross-model decision fusion. The results demonstrated that the cross-model decision fusion strategy employing adaptive Bayesian weighting for decision-making achieved excellent performance, with Rp2, RMSEP, and RPD values of 0.9911, 0.9056, and 10.3730, respectively. In subsequent external validation, the Rv2 and RMSEV were 0.9352 and 2.1479, respectively, demonstrating significant improvement over the single-modal model and the spectrum fusion models. This study offers a promising approach for rapid, nondestructive freshness detection during shrimp storage.

The contamination of plasticizers in food has attracted more and more attention. In this study, microwave detection technology is used to provide a new method for rapid non-destructive detection of phthalate esters (PAEs) plasticizers in edible oil. Microwave data of rapeseed oil samples containing different concentrations of dibutyl phthalate (DBP) were collected. Two feature optimization methods, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were used to select the features of the pre-processed data. Partial least square regression (PLSR) of linear regression model and support vector regression (SVR) and Extreme Gradient Boosting (XGBoost) of nonlinear regression model were established according to the selected best feature subset. Meanwhile, particle swarm optimization (PSO) was used to optimize the parameters of the two nonlinear models. By comparing the prediction performance of the three models, the CARS-PSO-SVR model established after feature selection and parameter optimization achieved the highest prediction accuracy among the evaluated models under the present experimental conditions, and its coefficient of determination (R2) and root mean square error (RMSE) are 0.994 and 0.467 mg/kg, respectively. The results show that, using the combination of microwave detection technology and machine learning modeling, high precision and rapid non-destructive detection of DBP content in rapeseed oil can be realized.

Gas adsorption technology serves as a critical tool in rapid food detection for capturing volatile organic compounds. The research in this domain has predominantly emphasized sensing reaction, often overlooking the significant role of adsorption capacity in enhancing detection sensitivity. Current investigations reveal that, though studies on gas adsorption within the food field remain relatively limited, valuable insights can be drawn from adsorption technologies developed in other fields. The common adsorbents are constrained by inadequate selectivity, limited capacity, and insufficient stability. This review systematically summarizes recent advances and application potential of novel gas adsorption materials for food quality detection, including metal-organic frameworks (MOFs), covalent organic frameworks (COFs), porous organic polymers (POPs), porous carbon materials (PCMs), and mixed matrix materials (MMMs)-with a focus on their synthesis strategies, adsorption performance, and respective advantages and limitations. Special emphasis is placed on their selective adsorption behavior toward different gases. The practical deployment of these materials continues to face challenges in the food quality detection aspects, including adsorption efficiency, high synthesis costs, limited stability, and complex fabrication processes. Future advances rely on the rational design of materials, the development of low-cost synthesis routes, and the enhancement of their adsorption and sensing performance in food detection. Such improvements are expected to broaden the applications of gas adsorption materials in rapid food detection, improving detection sensitivity and accuracy.

Pesticide residues in tea pose significant health risks to consumers. This research focuses on developing a surfaceenhanced Raman scattering (SERS) sensor using a solid-phase substrate made of Au@Ag@PVP nanoparticles (NPs), combined with machine learning chemometric techniques, for the rapid detection of trichlorfon, carbendazim, and clothianidin in tea. Au@Ag@PVP NPs were immobilized onto anodic aluminum oxide (AAO) filter membranes via vacuum filtration, yielding densely packed NPs that generate strong electromagnetic hot spots, thereby increasing SERS sensitivity. Raman spectra of tea infusions containing different concentrations of pesticides were acquired and analyzed using three machine-learning algorithms. Among these models, competitive adaptive reweighted sampling-partial least squares (CARS-PLS) demonstrated superior predictive performance, with Rc = 0.9950, Rp = 0.9942, and RPD = 9.2893 for trichlorfon; Rc = 0.9955, Rp = 0.9942, and RPD = 9.1010 for carbendazim; and Rc = 0.9981, Rp = 0.9957, and RPD = 9.7523 for clothianidin. This approach enables the rapid, quantitative detection of pesticide residues with minimal sample preparation and high sensitivity, representing a practical method for monitoring tea safety.

The economic value of summer-autumn tea can be enhanced through fermentation. However, the real-time monitoring quality of fermentation production faces significant challenges. Therefore, an on-line visible-near infrared spectroscopy (VIS-NIR) taste compounds monitoring system was developed to assist the fermentation production. Linear and non-linear VIS-NIR calibration methods were evaluated, and a parallel weighted hybrid modeling (PWHM) strategy was first proposed to elucidate the spectral-composition mapping. The results demonstrated that model optimization significantly improved performance, with non-linear models generally outperforming linear ones. Specifically, the PWHM models achieved excellent predictive accuracy for total sugars, total acids, and L-Theanine, showing high ratio of performance to deviation (RPD) and low mean absolute error (MAE). The fundings showed that the proposed method can meet the detection requirements for the fermentation process of summer-autumn tea. This study provides a technical reference for digital transformation and efficient production in the food fermentation industry.

Food safety is a critical global concern with direct consequences for human health, particularly due to the serious risks posed by biotoxin contamination in food. Consequently, the rapid and reliable detection of emerging biotoxins has become an essential priority in ensuring food safety. Surface-enhanced Raman spectroscopy (SERS) has gained growing consideration for the detection of various biotoxins due to its selectivity, rapid multiplex identification, enhanced sensitivity, and straightforward mechanism. This paper reviews the recent five years of progress of SERS-enabled biosensors for biotoxin detection, including aquatic, fungal, bacterial, and plant-based biotoxins in food. In addition, several biotoxin detection strategies in different food matrices using SERS sensors were systematically analyzed, including SERS label-free and label-based substrates, as well as the utilization of these substrates for various applications. The prospects and challenges of different substrates for detecting food contamination are also briefly discussed. The SERS-based methods for biotoxin detection are widely used nowadays. The nanomaterial can be designed in several ways, making it particularly helpful in developing SERS-based biosensors. Technological advancements in SERS biosensors have significantly enhanced SERS detection capabilities, opening up new avenues for biotoxin detection applications. The advancement of SERS-based methods will enhance food safety detection by addressing current challenges and paving the way for future opportunities.

Quality assessment of crude palm oil remains a critical challenge globally, particularly in resource-poor areas where traditional methods are time-consuming and destructive. This study explores machine learning-assisted Raman spectroscopy for non-destructive assessment of peroxide value (PV) and iodine value (IV) in palm oil. Raman spectra were collected from 200 samples from five Ghanaian markets, with second derivative preprocessing significantly enhancing feature resolution. Twelve predictive models were developed by combining three variable selection algorithms (CARS, GA, UVE) with three regression methods (PLS, SVM, RF). The genetic algorithm-random forest (GA-RF) model demonstrated exceptional prediction accuracy for both PV (Rp = 0.9831, RPD = 7.7397) and IV (Rp = 0.9752, RPD = 6.3927). Key spectral regions associated with unsaturation (1287-1657 cm⁻¹) and oxidation (1748-1840 cm⁻¹) were identified as crucial predictors. This approach enables rapid, non-destructive quality assessment with potential applications throughout the palm oil value chain.

Isothermal DNA amplifiers have emerged as powerful toolkits for the precise determination of contaminants in complex food matrices. Nevertheless, current DNA circuits suffer from the limitation of low amplification capacity. Here a cyclic feedback amplification (CFA) system with exponential signal gain was proposed for aflatoxin B1 (AFB1) analysis through synergistic cross-activation of catalytic hairpin assembly (CHA) and exonuclease III (Exo III)-powered amplification. The initiator activates CHA to generate numerous fuel strands. These fuels then initiate Exo III-powered amplification to generate numerous initiators that feed back into CHA, undergoing a mutually reinforcing cyclic amplification process. The CFA system realizes successive regeneration of the initiator and fuel strands, achieving exponential signal amplification for highly sensitive analyte detection. The CFA system, as a versatile and robust amplification tool, enables reliable detection of AFB1 in buffer, wheat, and maize samples by introducing an auxiliary recognition element, offering great potential for trace food contaminant analysis.

Capsaicinoids are the primary contributors to the pungency of chili powder, with capsaicin and dihydrocapsaicin together accounting for approximately 90% of the total content. The accurate quantification of capsaicinoids in chili powder is essential for quality evaluation due to growing consumer demand. Therefore, this study employed a data fusion strategy by combining visible-near infrared and Raman spectroscopy for the nondestructive quantification of capsaicinoids. To enhance spectral resolution and minimize noise, Savitzky-Golay preprocessing was flexibly optimized for each level of data fusion. The results illustrated that the intermediate-level data fusion model, after variable selection using variable combination population analysis, and iteratively retaining informative variables, achieved the best performance (Rc2 = 0.975, Rp2 = 0.971, RMSEC = 0.051, RMSEP = 0.061, RPD = 4.898). Thus, the integrated application of complementary visible-near infrared and Raman spectral fusion, combined with variable combination population analysis that iteratively retains informative variables, offers an efficient and feasible approach for detecting pungent compounds and evaluating the quality of chili powder.

Rapid advances in artificial intelligence, sensor technologies, and image recognition have accelerated the transition toward digital and intelligent systems for evaluating tea quality. Conventional sensory assessment remains the foundation of tea grading and pricing, but it is subjective, labor-intensive, time-consuming, and difficult to standardize for large-scale industrial applications. This review systematically summarizes recent advances in digital sensing technologies for comprehensive tea quality evaluation by organizing current research around three complementary tea quality evaluation objects, namely dry tea, tea infusion, and infused leaves. For dry tea, computer vision, near-infrared spectroscopy (NIRS), and hyperspectral imaging are reviewed for evaluating appearance, morphology, and internal chemical composition. For tea infusion, electronic nose, electronic tongue, spectroscopy, and imaging techniques are discussed for characterizing liquor color, aroma, taste, and physicochemical properties. For infused leaves, computer vision and hyperspectral imaging are summarized for assessing morphology, color, structural characteristics, and their potential value in processing quality verification. The review further highlights multimodal data fusion as a key strategy for integrating complementary information from different sensing modalities and tea quality evaluation objects, thereby improving the accuracy, robustness, and interpretability of digital tea quality assessment. Current challenges, including data heterogeneity, limited model generalization, insufficient research on infused leaves, the lack of standardized protocols and databases, sensor drift, calibration transfer, and equipment cost, are also discussed. Finally, future perspectives are presented for the development of portable, intelligent, and standardized digital sensing systems. Overall, this review provides a comprehensive overview of digital sensing technologies and offers future perspectives for developing objective, traceable, and intelligent tea quality evaluation throughout the tea industry.

As food safety issues gain increasing attention, the need for rapid, accurate, and low-cost detection techniques becomes essential. Colorimetric sensors, known for their rapid response, high sensitivity, low cost, and readily observable results, are playing an increasingly important role in food quality inspection. This review article provides an in-depth discussion on the development and application of colorimetric sensing technology in the field of food detection and safety. The review outlines the fundamental principles of colorimetric sensors and provides a critical assessment of research on array dyes, substrates, and system optimization. In recent years, the optimization of functionalized precious metal nanoparticles and nanoenzymes has pushed detection limits down to the parts per billion level. Porous polymer substrates modified with synergistic flexible materials combine high adsorption, strong anti-interference, and excellent mechanical properties, providing support for integrated applications in wearable sensors and smart packaging. The integration of data acquisition and analysis techniques based on color changes is also discussed. Through an overview of the application of sensors in food safety testing, food quality control, and food production and processing monitoring, this review article highlights the significant potential of colorimetric sensor arrays (CSAs) in enhancing food safety and quality. The review also identifies current challenges, with major obstacles including environmental interference, matrix effects, and limited industrial validation. The review concludes that combining advanced sensor materials, portable and stable optical devices with online analytical platforms for real-time data extraction and simple analysis will enable CSAs to develop into a powerful on-site tool for ensuring food quality and safety.

Plasmonic metal-support composites have been explored for heterogeneous catalysis with poor robustness under in situ conditions. The substrate performance index for surface-enhanced Raman scattering (SERS) is also limited by spatial heterogeneity and signal instability. Most reported hybrid devices still face a clear trade-off between catalytic and SERS performance, and durable multifunctionality in one integrated system remains largely unexplored. This study reports an integrated platform that combines photoelectrode, photocatalytic, and SERS functions, delivering stable photocurrent, high catalytic efficiency, and reproducible Raman activity, respectively. Herein, a systematic sputtering/electrodeposition combined with a single-step, temperature-controlled annealing route yields hybrid titanium dioxide (TiO2)/gold (Au) nanodendrites. The interconnected Au-TiO2-H2O interfaces promote efficient separation and interfacial transport of photogenerated charge carriers, while interdendritic junctions and sharp tips localize target molecules with high-|E|4 SERS hot spots. The composite enabled on-site detection of malachite green (MG+) in aquaculture water followed by in situ photoelectrocatalytic degradation, administering complete removal (Ct/C0→0) for feed concentrations up to × 10-4 mol/L in a 60 min interval, with synchronous real-time, online monitoring of MG+ across the entire degradation period. The composite also generated a stable and high photocurrent response at -0.73 V to serve for photoelectrochemical applications as a photoanode. Finally, the storage stability, reproducibility across synthesis batches and uniformity (RSD of 10.998% at n = 100), reusability for at least six cycles, self-cleaning, and repeatable detection performance demonstrate that this integrated sensing-degradation platform offers potential for deployment in operational aquatic matrices.

The current research has been focused on the quantification of total phenolic content (TPC) and total flavonoid content (TFC) based on spectral data collected from mid-infrared (MIR) and near-infrared (NIR) spectroscopy. Low-level fusion (NIR-MIRLL) and mid-level fusion (NIR-MIRML) were employed to enhance the statistical performance parameters with model partial least squares (PLS). In measuring the performance of the PLS regression models for TPC and TFC, the measures used were the coefficient of prediction (Rpred), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The best results were noted for TPC concerning the NIR-MIRML fusion model, which yielded Rpred = 0.8998, RMSEP = 8.82 mg GAAC/100 g, and RPD = 2.20. Best results for TFC were noted with the NIR-MIRLL fusion model at Rpred = 0.9086, RMSEP = 2.28 mg/100g, and RPD = 2.35. The NIR-MIRLL and NIR-MIRML fusion models surpassed the single sensor models' results regarding TPC and TFC content in wheat flour. The outcome indicates that the proposed fusion strategy has been successful with enhanced prediction accuracy.

ABSTRACT Volatile compounds generated during food processing are closely associated with product quality, and their real‐time monitoring provides an effective approach for dynamically tracking quality changes and ensuring product consistency. This review aims to summarize the formation mechanisms of volatile compounds during food processing and to examine current principles, methods, and application progress in real‐time monitoring, with particular emphasis on biological olfaction and biomimetic sensing technologies. The available studies indicate that food flavor is shaped by complex processes such as water migration, browning, oxidation, and sugar metabolism, and that the resulting volatile compounds play a decisive role in the sensory properties of final products. Because different volatile compounds exhibit distinct chemical characteristics, effective monitoring depends on sensor systems with appropriate sensitivity and selectivity, as well as on the integration of real‐time detection with automation for process regulation and quality control. Although challenges remain in sensor performance, selectivity, and system integration, real‐time volatile monitoring shows considerable promise for food processing applications. Overall, advances in ultra‐fast and highly selective sensors, customizable hardware architectures, and data‐driven intelligent platforms with integrated multi‐sensor systems are expected to further promote the development and practical implementation of e‐nose technologies.

Atmospheric cold plasma (ACP) is a promising non-thermal processing technology capable of modifying protein structures and enhancing functional performance in food systems. In this study, the effect of atmospheric cold plasma (ACP) treatment on the gel quality of minced beef was investigated, with emphasis on the underlying gel quality change mechanisms involving myofibrillar protein (MP) aggregation and conformational modifications. Firstly, minced beef was exposed to ACP for 30-180 s, then divided into two portions: one was subjected to stepwise heating (40 degrees C/30 min + 80 degrees C/30 min) for gel quality assessment, while the other was used to extract MPs for structural characterization. The results showed that 60-90 s treatment resulted in the most significant improvement in gel characteristics, with gel strength more than doubling (5800 g center dot mm vs. 2200 g center dot mm) and textural attributes such as springiness and cohesiveness significantly improving. To uncover the mechanism of gel quality improvement, the study analyzed MP solubility, sulfhydryl modifications, surface hydrophobicity, secondary structure, and aggregation behavior. The findings showed that ACP induced controlled protein unfolding and beta-sheet enrichment, which facilitated efficient cross-linking during heating and enhanced gel network formation. Furthermore, rheological evaluation confirmed higher storage modulus (G ') and favorable tan delta, consistent with strong yet elastic gel networks. Notably, WHC remained relatively stable (66.2-67.5 %) across treatments, indicating that gel strengthening occurred through improved protein organization without compromising water retention. These insights provide a mechanistic understanding of how ACP influences gel quality, thus supporting ACP as a promising clean-label strategy to improve the texture and functionality of meat products.

Marine bioactive materials are obtained from diverse organisms, which include algae, sponges, corals, and mollusks. These materials possess unique chemical structures and evolutionary traits that provide key benefits, such as high biocompatibility, controllable biodegradability, and a minimal immune response. This article highlights their applications in food science for functional foods and packaging and in biomedicine for targeted drug delivery using fucoidan nanoparticles and tissue engineering. A primary emphasis is placed on how integrating nanotechnology can innovatively enhance their functionality. We also identify critical research gaps that include an unclear connection between structure and performance at the nanoscale and challenges in sourcing raw materials. These insights are intended to guide the creation of next-generation health solutions that originate from marine resources.

Background Food Safety is particularly threatened by okadaic acid (OA), a marine biotoxin produced by dinoflagellates that accumulates in aquatic products. Rapid global warming and the overexploitation of marine ecosystems are intensifying water pollution, heightening risks to environmental sustainability and human health. As the leading cause of diarrhetic shellfish poisoning, OA poses a danger to public health and the seafood industry. Scope and approach This paper provides a comprehensive review of the advancements over the past five years in biosensor technologies for the sensitive and specific detection of OA in aquatic food commodities. It emphasizes the incorporation of highly selective biorecognition elements, such as antibodies and aptamers, into advanced optical sensing strategies and highlights validated systems that deliver reliable performance in complex food matrices and in real-time, field-deployable monitoring applications. Key findings and conclusion Sensitive and selective detection of OA at very low concentrations is achievable using fluorescence, colorimetry, and other emerging methods. The techniques exhibited limits of detection for OA ranging from ultra-trace levels with fluorescence and colorimetry to moderate levels with surface-enhanced Raman spectroscopy, electrochemiluminescence, near-infrared spectroscopy, and liquid-crystal detection. These techniques use specific biorecognition elements that produce measurable signals and perform well in complex samples. Overall, these methods offer reliable, cost-effective real-time platforms for food safety assessment and early detection of OA.

Food safety has become a major global concern in recent years because chemical and biological contaminants can enter the food supply chain and threaten public health. Ultra-trace monitoring of harmful biotoxins has attracted increasing attention because these compounds may accumulate at very low concentrations while still causing significant toxic effects. Surface-enhanced Raman spectroscopy (SERS) is a promising analytical technique for food safety applications owing to its high sensitivity and molecular specificity. In this work, gold nanorods (AuNRs) were used to develop a rapid, label-free, and reliable SERS assay for the detection of okadaic acid (OA) in shellfish. The method achieved an enhancement factor of 1.18 & times; 106 with straightforward sample preparation. Partial least squares (PLS)-based chemometric models were used to predict OA concentrations over the range of 1.0 & times; 10-4 to 1.0 & times; 102 mu g/mL. Among them, the bootstrapped soft shrinkage-partial least squares (BOSS-PLS) model showed the best performance. It achieved a calibration correlation coefficient (Rc) of 0.9956, a root-meansquare error of cross-validation (RMSECV) of 0.1895, and a residual predictive deviation (RPD) of 9.21. The recoveries ranged from 97.8% to 102.2%, and the relative standard deviation (RSD) values ranged from 3.32% to 7.34%, indicating acceptable accuracy and precision. Unlike many previously reported OA-SERS approaches that rely on aptamers, labels, or multistep amplification strategies, the present method uses a label-free AuNRs substrate combined with DFT-assisted spectral interpretation and chemometric quantification. The results suggest that the developed approach has strong potential for OA determination in shellfish for food quality and safety monitoring.