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.

Staphylococcus aureus (S. aureus) has been identified as a indicator of food contamination. In this study, a sensitive and accurate biosensor strategy for S. aureus through rolling circle amplification-assisted surface-enhanced Raman scattering (RCA-assisted-SERS), has been established. The work relies on the interaction between the aptamer and its partial complementary DNA strands fabricated on the surface of gold and silver-assisted magnetic microspheres and the subsequent detachment to trigger the activation of the RCA process. In RCA, template DNA, T4 DNA ligase, and Phi29 DNA polymerase were assembled to form long single-stranded DNA containing repetitive sequences. The gold core encapsulated with a layer of 4-nitrothiophenol and further covered with a silica shell was employed as the SERS nanoprobe (Au@NTP@SiO2). Subsequently, the output and amplification of SERS signal were performed by hybridizing ssDNA functionalized Au@NTP@SiO2 to realize the quantitative detection of S. aureus. Under the optimal conditions, S. aureus sensing was monitored (36.0-3.6 x 10(8) cfu/mL) with a limit of detection of 2.0 cfu/mL. This strategy was further validated for S. aureus recognition in spiked real samples with favorable recoveries (94.0-103.4 %) at p > 0.05. The suggested RCA-assisted SERS approach exhibits potential for multiple foodborne pathogens in both food safety and biomedical investigations.

This study developed a rapid, label-free analytical strategy for quantifying zearalenone (ZEN) in corn oil. A highly sensitive Au octahedrons (Ohs) monolayer film was synthesized as the surface-enhanced Raman spectroscopy (SERS) substrate. A hybrid metaheuristic algorithm that combines the particle swarm optimization (PSO) algorithm and the grey wolf optimizer (GWO) algorithms, was used to optimize an extreme learning machine (ELM) model (i.e., the PSOGWO-ELM model). The PSOGWO-ELM model analyzed the collected SERS spectra to determine ZEN contents in corn oil. The results demonstrated that the analytical strategy possessed excellent performance: the root mean squared error of the prediction set (RMSEP) = 0.2297 μg/mL, the coefficient of determination of the prediction set (RP2) = 0.9907, and the ratio of performance to deviation of the prediction set (RPDP) = 10.3695. The proposed analytical approach shows considerable promise for the rapid, label-free, and accurate detection of trace levels of ZEN in corn oil.

Tetrodotoxin (TTX) is a potent neurotoxic marine biotoxin that poses severe health risks. To address this challenge, a dual-mode detection strategy was developed based on TTX-induced conformational changes in hairpin probes (HP), regulation of Au NPs aggregation to modulate localized surface plasmon resonance, and hotspot formation for colorimetric and surface-enhanced Raman spectroscopy (SERS) signal detection. This method demonstrates higher specificity than ion-induced aggregation. TTX-induced conformational changes in HP were validated by molecular docking simulations and circular dichroism spectroscopy. A finite-difference time-domain was utilized as a theoretical guide to verify that the signal amplification effect caused by the introduction of Au@Ag NPs. Under optimal conditions, the limits of detection were 0.0947 ng/mL (SERS) and 1.077 ng/mL (colorimetry). This strategy successfully detected TTX in seawater and fish samples, showing no significant difference from liquid chromatography-mass spectrometry results (P > 0.05). This approach is promising for monitoring food safety and marine toxins.

In this study, we propose a novel surface-enhanced Raman scattering (SERS) method for quantifying aflatoxin B1 (AFB1). This method relies on the target-triggered release of a SERS reporter from aptamer-sealed aminated mesoporous silica nanoparticles (MSNs). These MSNs were synthesized to accommodate 4-mercaptophenylboronic acid (4-MPBA) within their well-defined micropores, which were subsequently sealed with AFB1 aptamers. Upon specific binding of AFB1 to its aptamer, the conformational change in the aptamer is regulated by the presence of the target. Consequently, a positive linear relationship between the AFB1 concentration and the 4-MPBA SERS signal was observed. Under optimal conditions, the method exhibited a good linear relationship over the range of 0.1 to 5 ng/mL AFB1, with a limit of detection (LOD) of 0.03 ng/mL. This strategy was validated using wheat samples, yielding results comparable to high performance liquid chromatography-fluorescence detector (P > 0.05), confirming its reliability for detecting AFB1 in complex food matrices.

Tetrodotoxin (TTX), a potent neurotoxin found in marine environments, poses a severe threat to public health due to its high mortality rate. Herein, we proposed a surface-enhanced Raman scattering (SERS) biosensor that utilizes a three-way junction catalytic hairpin assembly (3WJ-CHA) method for the ratiometric detection of TTX. The core-satellite FAPANPs nanostructures consisted of Fe3O4 magnetic microspheres (FeMMs) as the core, surrounded by gold-Prussian blue (PB)-Au nanoparticles as satellites. During the detection process, the target TTX triggered the cleavage of hybrid double-stranded DNA (dsDNA) and released the initiator strand (Is), which subsequently initiated the 3WJ-CHA process on the FAPANPs. This biosensor, built upon 3WJ-CHA, simplifies separation steps, operates without enzymes, and offers significant convenience. It also boasted high amplification efficiency, detecting TTX at a low limit of 0.0027ng/mL with a linear range spanning from 0.01 to 500ng/mL. The use of PB as an internal standard effectively mitigated various interferences, and significantly enhanced the reproducibility of TTX detection, resulting in a relative standard deviation of merely 3.66%. This work offered a new perspective on designing nucleic acid-based detecting TTX analysis.

Okadaic acid (OA) is the primary toxin that causes diarrhetic shellfish poisoning (DSP), posing a serious threat to human health. Although existing methods can achieve accurate detection, challenges remain in terms of sensitivity and efficiency. In this study, an aptamer adsorption-optimized aptasensor based on surface-enhanced Raman spectroscopy (SERS) was developed to detect OA with high sensitivity and specificity. To provide more binding sites for the aptamer and enhance the detection efficiency, we optimized the attachment of the complementary chains of the aptamer to the nanoparticles (NPs) by adding different salt solutions (NaCl, KCl, MgCl2, KI, KBr, Tris-HCl). The result showed that NaCl was the best option and reduced the connection time by 6 h. The use of gold-silver (Au@Ag) core-shell NPs provided stable SERS enhancement to the sensor, and an optimal silver shell thickness of 4.5 nm was obtained to maximize this enhancement. Quantitative detection of OA was achieved within 35 min through the specific binding of the aptamer to the target. The results showed good linearity in the range 0.1-1000 ng/g (R-2 = 0.9884). The limit of detection (LOD) was 0.037 ng/g which has strong competitiveness among other methods. The spiked recovery rate was measured to be 96.49-108.47 % using mussels as real samples. Comparing the result of this method with the standard ELISA method through paired t-test (P = 0.343 > 0.05), no significant difference was found confirming the ability of proposed method for OA detection in complex food matrices. This method offers the advantages of simplicity and sensitivity, providing a potential means for OA detection.

This study proposes a fast, cost-effective, and non-destructive strategy for determining acidity and peroxide index of extra virgin olive oil (EVOO) using portable near-infrared (NIR) spectroscopy coupled with a novel chemometric approach. A portable NIR spectroscopy system was used to collect NIR spectra from EVOO samples. A novel chemometric approach integrating the snake optimizer (SO) algorithm with the combined moving window (CMW) strategy was then proposed and named the snake optimizer combined moving window (SOCMW) algorithm. Five models, including partial least squares regression (PLSR), variable combination population analysis (VCPA), iteratively retaining informative variables (IRIV), iteratively variable subset optimization (IVSO), and SOCMW, were employed to analyze the NIR spectra for determining acidity and peroxide index. The results demonstrated that the SOCMW model, combined with the portable NIR spectroscopy system, achieved superior predictive performance for acidity and peroxide index, as indicated by a higher coefficient of determination of the prediction set (R2P), a higher ratio of performance to deviation (RPD), and a lower root mean squared error of the prediction set (RMSEP). These results demonstrate that the proposed analytical strategy can serve as a useful first screening step for the assessment of EVOO quality.

The distinctive flavor of oolong tea is derived from its complex processing techniques. In this study, comprehensive untargeted metabolomics were applied to investigate the effects of spring oolong tea processing on its aroma, taste, and color. Gas Chromatography-Mass Spectrometry (GC-MS) and Ultra-High-Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry (UHPLC-Q-Tof-MS) identified 118 volatile and 79 non-volatile metabolites. Multivariate statistical analysis filtered 30 key metabolites. Indole, E-nerolidol, linalool, and geraniol were identified as crucial volatile compounds responsible for the characteristic floral and fruity aroma of spring oolong tea, with indole contributing significantly to its jasmine-like fragrance. The tea infusion color deepened as processing progressed but brightened after Killing Green. Changes in theaflavins and catechins were the main factors influencing oolong tea's color and taste. By analyzing the dynamic changes in key metabolites, this study reveals the formation of spring oolong tea's quality attributes.

The precise changes in the flavor quality of green tea before and after the optimal consumption period remain elusive. This study used HS-SPME-GC-MS to carefully examine the volatile compounds of green tea samples from multiple storage periods and simultaneously detected changes in taste and color during tea infusion. Notably, 16 compounds with VIP > 1.5 were identified, highlighting their significance in the overall analysis profile. Among these components, nonanoic acid, octanoic acid and hexanal were identified as potential contributors to offflavors and rancid oil flavors in tea after storage. The color of the tea broth becomes darker. Bitter flavor is associated with increased amino acid content. The PSO-SVM model showed excellent accuracy for predicting the storage duration of green tea. These findings provide critical theoretical understandings of green tea manufacturing and quality control.

BACKGROUNDTea-garden pest control is crucial to ensure tea quality. In this context, the time-series prediction of insect pests in tea gardens is very important. Deep-learning-based time-series prediction techniques are advancing rapidly but research into their use in tea-garden pest prediction is limited. The current study investigates the time-series prediction of whitefly populations in the Tea Expo Garden, Jurong City, Jiangsu Province, China, employing three deep-learning algorithms, namely Informer, the Long Short-Term Memory (LSTM) network, and LSTM-Attention.RESULTSThe comparative analysis of the three deep-learning algorithms revealed optimal results for LSTM-Attention, with an average root mean square error (RMSE) of 2.84 and average mean absolute error (MAE) of 2.52 for 7 days' prediction length, respectively. For a prediction length of 3 days, LSTM achieved the best performance, with an average RMSE of 2.60 and an average MAE of 2.24.CONCLUSIONThese findings suggest that different prediction lengths influence model performance in tea garden pest time series prediction. Deep learning could be applied satisfactorily to predict time series of insect pests in tea gardens based on LSTM-Attention. Thus, this study provides a theoretical basis for the research on the time series of pest and disease infestations in tea plants. (c) 2024 Society of Chemical Industry.

The public is seriously at risk for major health problems due to environment contamination from lead ions (Pb2+) and heavy metal bioaccumulation in agricultural goods. In this study, core-shell silver-coated gold nanorods (Au@Ag NRs) were created and treated with glutathione (GSH) and 4-aminobenzoic acid (4-MBA) to monitor Pb2+ using the surface-enhanced Raman scattering (SERS) technique. Its free carboxyl groups can chelate Pb2+ added in solution and then present self-aggregated, bringing about more "hot spots" to acquire the significant enhancement signal of 4-MBA. The SERS intensity had a good linear correlation (y = 2420.81x+11333.62, R2 = 0.9914) in the dynamic range of Pb2+ concentration of 0.5-1000 mu g/L under the current optimization conditions, and LOD was 0.021 mu g/L. In addition, recoveries of 94.17-101.12% and 81.31-97.86% for Pb2+ in tea powder and glutinous rice flour confirmed the monitoring accuracy of Pb2+, highlighting its huge potential in the monitoring food quality and safety.

Aflatoxin B1 (AFB1) is a potent carcinogen whose presence in food threatens consumers. This study aimed to develop a rapid method for the quantitative detection of AFB1 in crude palm oil (CPO) using novel gold nanoparticles (AuNPs) and the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) technique, combined with chemometrics. The bootstrapping soft shrinkage-partial least squares (BOSS-PLS) algorithm yielded the most favorable results, with an Rc of 0.9929, RMSECV of 0.204 ng/g, and an RPD of 3.93. The results demonstrated that the developed method is highly accurate, sensitive, and specific for detecting AFB1 in CPO, with a low limit of detection (LOD) of 0.00092 ng/g. The results underscore the potential of employing a combination of AuNPs, QuEChERS extraction, and chemometrics for detecting AFB1 in CPO. The developed method can be employed to monitor and ensure the quality of CPO, thereby preventing the spread of AFB1 contamination in the food industry.

Tetrodotoxin (TTX), a lethal neurotoxin, poses a grave threat to human health. The available spectroscopic methods suffer from limitations such as complex procedures and inadequate on-site capabilities. In this study, we proposed a method using Fe3O4@Cu as a catalytic biosensor combined with SERS, colorimetry and image processing for TTX detection. Integrating the aptamer amplifies the specificity of the system and masks the catalytic activity of Fe3O4@Cu. The catalytic efficiency of Fe3O4@Cu in the H2O2-TMB reaction can quantify the concentration of TTX in the system. Consequently, oxidation of TMB (oxTMB) led to the generation and change of signals for SERS, colorimetry and image processing, enabling a three-channel quantitative detection of TTX. Under the optimal conditions, the detection limit of established SERS, colorimetry and image processing were 0.055, 2.127 and 0.243 ng/mL, respectively. This three-channel biosensor was applied to real samples, providing an accurate, stable and adaptable alternative for on-site TTX detection.

Acetamiprid (ACE) is a neuroactive insecticide similar to nicotine. ACE can cause neurotoxicity, immunotoxicity, and hepatotoxicity. This study explored the feasibility of using Surface-enhanced Raman spectroscopy (SERS) sensor and random frog (RF) algorithm to rapidly detect ACE in crude palm oil (CPO) within 400 – 1800 cm− 1 Raman peak. ACE levels varied from 5 to 100 ng/g. Successive projections algorithm – PLS (SPA- PLS), random frog-partial least squares – PLS (RF-PLS), and uninformative variable elimination-partial least squares (UVE-PLS) were used to develop quantitative models for ACE prediction after the data was pretreated with standard normal variate (SNV). The RF-PLS model provided superior results with Rc, Rp, RMSECV and RMSEP values of 0.990, 0.989, 5.17 and 6.95, respectively, with recovery rates of 93.89 – 108.32%. The findings demonstrate the enormous potential of the proposed SERS sensor in combination with RF-PLS for the rapid detection of ACE residues in CPO.

Tea waste (TW) includes pruned tea tree branches, discarded summer and fall teas, buds and wastes from the tea making process, as well as residues remaining after tea preparation. Effective utilization and proper management of TW is essential to increase the economic value of the tea industry. Through effective utilization of tea waste, products such as activated carbon, biochar, composite membranes, and metal nanoparticle composites can be produced and successfully applied in the fields of fuel production, composting, preservation, and heavy metal adsorption. Comprehensive utilization of tea waste is an effective and sustainable strategy to improve the economic efficiency of the tea industry and can be applied in various fields such as energy production, energy storage and pharmaceuticals. This study reviews recent advances in the strategic utilization of TW, including its processing, conversion technologies and high value products obtained, provides insights into the potential applications of tea waste in the plant, animal and environmental sectors, summarizes the effective applications of tea waste for energy and environmental sustainability, and discusses the effectiveness, variability, advantages and disadvantages of different processing and thermochemical conversion technologies. In addition, the advantages and disadvantages of producing new products from tea wastes and their derivatives are analyzed, and recommendations for future development of high-value products to improve the efficiency and economic value of tea by-products are presented.

The alarming increase in drug-resistant bacteria in fish resulting from the misuse of antibiotics poses a significant threat to ecosystems and human health. Therefore, the development of a reliable approach for detecting anti-biotic residues in fish is crucial. In this study, a rapid and simple method for detecting chloramphenicol (CAP) residue in tilapia was developed using surface-enhanced Raman scattering (SERS) combined with chemometric algorithms. Silver and gold core-shell nanoparticles (Ag@Au CSNPs) were used as SERS nanosensors to achieve strong signal amplification with an enhancement factor of 2.67 x 106. The results demonstrated that the variable combination population analysis-partial least square (VCPA-PLS) model combined with the standard normal variable transformation pretreatment method exhibited the best predictive performance with a detection limit of 1 x 10- 5 mu g/mL. Thus, an SERS technique was established based on Ag@Au CSNPs combined with VCPA-PLS to rapidly detect CAP in tilapia.

Chloramphenicol (CAP) is a widely used antibiotic in aquaculture. However, its improper use and the resulting residues in fish have become a significant concern for human health. Conventional methods for quantifying CAP are complex and expensive, leading to the need for more efficient and sensitive techniques. In this study, both CAP aptamer and sulfhydryl (SH) complementary DNA strand (SH-cDNA) were bound to gold and silver cor-e-shell nanoparticles (Ag@Au CSNPs) and core-shell magnetic nanoparticles (Fe3O4@Au MNPs) via Au-S co-valent bonding and were used as SERS signal probes and capture probes, respectively. The principle of aptamer competitive recognition was applied to achieve the quantitative detection of CAP in fish. The capture probe and the signal probe generated strong signals by complementary hybridization in the absence of CAP in the detection system. In the presence of CAP, the CAP aptamer bonded to the CAP, and the signal probe was separated from the capture probe, resulting in a linear decrease in SERS signal intensity. Consequently, there was a strong inverse relationship between SERS intensity and the logarithm of CAP concentration, ranging from 10(-5) to 10(-1) mu g center dot mL(-1) (R-2 = 0.9974). The limit of detection (LOD) was as low as 16 pg center dot mL(-1). The established method exhibited strong selectivity, anti-interference, reproducibility, and stability.

Crude palm oil (CPO) is an important edible vegetable oil used globally, recently subjected to Sudan dye adulteration. This study explored the feasibility of a novel SERS-based Au@Ag substrate to detect four Sudan dyes (I - IV) in CPO. When mixed with spiked CPO, the SERS substrate produced strong signals that increased with increasing concentrations from 0.001 to 4.0 ppm. The genetic algorithm partial least square (GA-PLS) model outperformed the partial least square (PLS) and the ant colony optimization - PLS (ACO-PLS) models with Rc values of 0.9844, 0.9865, 0.9884, and 0.9888 0.9846. The calculated LOD were 0.00088 ppm, 0.00092 ppm, 0.00095 ppm, and 0.00097 and real sample recovery rates of 88.0-113.0%, 92.0-103.0%, 91.2-98.5% and 97.0-109.5% for Sudan I, II, III, and IV, respectively. The findings affirmed the SERS sensor's considerable potential for rapid and selective detection of Sudan dyes in CPO when combined with chemometrics.

In this work, a highly structured SERS tags assisted bacteria inhibiting salt-induced aggregation platform was proposed for screening foodborne pathogens. Initially, gold and silver core-shell nanoparticles (Au@Ag NPs) were synthesized by forming an Ag shell on an Au core in situ and employed as SERS substrate. The Au@Ag@MPBA tags were prepared by chemically joining Au@Ag NPs and 4-mercaptophenylboronic acid (4MPBA) via sulfhydryl groups. 4-MPBA functioned as a bacterial recognition element as well as a Raman reporter molecule. Through the reversible recognition relationship between a boronic acid group of 4-MPBA and bacterial peptidoglycan, multiple types of bacteria were collected by Au@Ag@MPBA tags. Plotting the SERS intensity of the tags against the logarithmic concentration of pathogens (56 - 56 x 10(5 )cfu/mL) revealed an ultra-low detection sensitivity of 16 cfu/mL. Bacteria were determined in real fish samples with satisfactory recoveries (94.26 %-104.37 %) and validation results (p > 0.05). Thus, the comprehensive bacteria-inhibiting salt-induced aggregation SERS-system offered speedy and accurate pathogenic bacteria screening in actual settings.