Quantification of deltamethrin residues in wheat by Ag@ZnO NFs-based surface-enhanced Raman spectroscopy coupling chemometric models

Advancements in magnetic nanomaterial-assisted sensitive detection of foodborne bacteria: Dual-recognition strategies, functionalities, and multiplexing applications

Foodborne bacterial diseases are a major cause of human death. Sensitively quantifying those bacteria in foodstuffs is crucial for effective prevention. Yet, the matrix effect from abundant food interferents challenges this goal. Magnetic nanomaterials have been extensively utilized as effective sample pretreatment agents to facilitate the sensitive detection of bacterial pathogens, benefiting from their contribution to mitigating interference in food matrices. The advancement of magnetic scaffold-based biosensors in monitoring foodborne bacteria is reviewed in this work. This review highlights the dual-recognition strategies, which contribute to superior affordability and applicability in bacteria monitoring. The functionalities of magnetic nanoscaffolds in constructing pathogen-targeted biosensors are cataloged into three sections: magnetic separation mediators, signal generation probes, and agents for inactivating bacterial pathogens. Additionally, magnetic nanocomposite-driven multiplexing determination is critically discussed, with different detection approaches are highlighted. Further perspectives regarding superior multifunctional magnetic probes, tunable selection of bioreceptor, portable detection devices, smart identification, and differentiation of bacteria mixtures are introduced.

Sensing technology empowering food safety: research progress of SERS-assisted multimodal biosensing toward food hazard factors

Food is the main source of human energy and nutrition, but once it is contaminated with hazardous factors, such as biotoxins, pesticide residues, etc., it will seriously damage health. This paper reviews the research progress of biosensors based on surface-enhanced Raman scattering (SERS) in the detection of food hazard factors. First, the basic principle, substrate and assay mode of SERS technology, as well as related design and sensing strategy mechanisms, are introduced. Then, the design idea of multimodal biosensors combining SERS with microfluidic, fluorescence, colorimetric, electrochemical (EC), molecular imprinting and other technologies is expounded to improve the analysis accuracy and specificity. Then the application results of multimodal biosensors based on SERS sensing toward food hazard factors are discussed, and the necessity of its development is illustrated. Finally, the future development direction of this field is prospected, which provides a reference for promoting the research and application of multimodal biosensors based on SERS.

Recent Advances in Food Safety: Nanostructure-Sensitized Surface-Enhanced Raman Sensing

Food safety is directly related to human health and has attracted intense attention all over the world. Surface-enhanced Raman scattering (SERS), as a rapid and selective technique, has been widely applied in monitoring food safety. SERS substrates, as an essential factor for sensing design, greatly influence the analytical performance. Currently, nanostructure-based SERS substrates have garnered significant interest due to their excellent merits in improving the sensitivity, specificity, and stability, holding great potential for the rapid and accurate sensing of food contaminants in complex matrices. This review summarizes the fundamentals of Raman spectroscopy and the used nanostructures for designing the SERS platform, including precious metal nanoparticles, metal-organic frameworks, polymers, and semiconductors. Moreover, it introduces the mechanisms and applications of nanostructures for enhancing SERS signals for monitoring hazardous substances, such as foodborne bacteria, pesticide and veterinary drug residues, food additives, illegal adulterants, and packaging material contamination. Finally, with the continuous progress of nanostructure technology and the continuous improvement of SERS technology, its application prospect in food safety testing will be broader.

A review of emerging techniques for pyrethroid residue detection in agricultural commodities

Pyrethroid pesticides are essential for modern agriculture, helping to control pests and protect crops. However, due to growing concerns about their potential impact on human health and the environment, reliable detection methods are essential to ensure food safety. In this literature review, we explore the techniques used over the past decade to detect pyrethroid residues in agricultural products. Until now, various methods have been developed for detecting pyrethroid pesticides, ranging from conventional analytical approaches to innovative approaches. The conventional analytical approaches include gas, liquid, and supercritical fluid chromatography, micellar electrokinetic capillary chromatography, and enzyme-linked immunosorbent assay. Whereas innovative approaches refer to various optical-based and electrochemical-based sensors. For each method, we evaluate its strengths, limitations, and practical applications. Recent innovations are highlighted, focusing on sensitivity, selectivity, and practical applicability. By summarizing the current state of research, this review serves as a valuable resource for researchers and practitioners, providing insights into the evolving technology and strategy for detecting pyrethroid residue.

Recent advancements in the extraction and analysis of phthalate acid esters in food samples

Phthalate acid esters (PAEs) are ubiquitous environmental pollutants present in food samples, necessitating accurate detection for risk assessment and remediation efforts. This review provides an updated overview of the recent progress on the PAEs analysis regarding sample pretreatment techniques and analytical methodologies over the latest decade. Advances in sample preparation include solid-based extraction techniques replacing conventional liquid-liquid extraction, with solid sorbents emerging as promising alternatives due to their minimal solvent consumption and enhanced selectivity. Although techniques like the microextraction methods offer versatility and reduced solvent reliance, there is a need for more efficient and environmentally friendly techniques enabling on-site portable detection. High-resolution mass spectrometry is increasingly utilized for its enhanced sensitivity and reduced contamination risks. However, challenges persist in developing in situ analytical techniques for trace PAEs in complex food samples. Future research should prioritize novel analytical techniques with superior sensitivity and selectivity, addressing current limitations to meet the demand for precise PAEs detection in diverse food matrices.

Recyclable Au@R-Fe3O4/g-C3N4 substrates for rapid SERS detection and degradation of multiple pollutants

Developing a Surface-enhanced Raman spectroscopy (SERS) method with excellent detecting ability, good recyclability and analyzing multiple pollutants rapidly are critical for evaluation of water quality in emergency pollution affairs. While constructing a multifunctional substrate with these characteristics to realize the application of SERS in water quality monitoring remains a challenge. In this work, a reusable Au@R-Fe3O4/g-C3N4 SERS substrate is prepared by loading Au nanoparticles (Au NPs) on Fe3O4 nanorings (R-Fe3O4) and the formed Au@R-Fe3O4 is further combined with g-C3N4 nanosheets through a simple electrostatic assembly method. The Au@R-Fe3O4/g-C3N4 nanocomposite presents multifunction of magnetic enrichment, SERS signal enhancement, multiple pollutants analyzing, and photocatalytic activity, which achieves quantitative detection of rhodamine B (RhB), tetracycline hydrochloride (TC), and 4-chlorophenol (4-CP), with detection limits of 5.30 × 10-9, 7.50 × 10-8, 7.69 × 10-8 mol/L, respectively. Furthermore, the recyclable detection capability of Au@R-Fe3O4/g-C3N4 for multi components is demonstrated by the strong SERS signal after 9 cycles of "detection-degradation" processes. Combined with good uniformity and stability, this SERS method based on Au@R-Fe3O4/g-C3N4 substrate provides a new strategy for the multi-pollutants detection and degradation in water environment.

Nanotechnology-based optical sensors for Baijiu quality and safety control

Baijiu quality and safety have received considerable attention owing to the gradual increase in its consumption. However, owing to the unique and complex process of Baijiu production, issues leading to quality and safety concerns may occur during the manufacturing process. Therefore, establishing appropriate analytical methods is necessary for Baijiu quality assurance and process control. Nanomaterial (NM)-based optical sensing techniques have garnered widespread interest because of their unique advantages. However, comprehensive studies on nano-optical sensing technology for quality and safety control of Baijiu are lacking. In this review, we systematically summarize NM-based optical sensor applications for the accurate detection and quantification of analytes closely related to Baijiu quality and safety. Furthermore, we evaluate the sensing mechanisms for each application. Finally, we discuss the challenges nanotechnology poses for Baijiu analysis and future trends. Overall, nanotechnological approaches provide a potentially useful alternative for simplifying Baijiu analysis and improving final product quality and safety.

Pesticides in the environment: Degradation routes, pesticide transformation products and ecotoxicological considerations

Among rising environmental concerns, emerging contaminants constitute a variety of different chemicals and biological agents. The composition, residence time in environmental media, chemical interactions, and toxicity of emerging contaminants are not fully known, and hence, their regulation becomes problematic. Some of the important groups of emerging contaminants are pesticides and pesticide transformation products (PTPs), which present a considerable obstacle to maintaining and preserving ecosystem health. This review article aims to thoroughly comprehend the occurrence, fate, and ecotoxicological importance of pesticide transformation products (PTPs). The paper provides an overview of pesticides and PTPs as contaminants of emerging concern and discusses the modes of degradation of pesticides, their properties and associated risks. The degradation of pesticides, however, does not lead to complete destruction but can instead lead to the generation of PTPs. The review discusses the properties and toxicity of PTPs and presents the methods available for their detection. Moreover, the present study examines the existing regulatory framework and suggests the need for the development of new technologies for easy, routine detection of PTPs to regulate them effectively in the environment.

Apt-Conjugated PDMS-ZnO/Ag-Based Multifunctional Integrated Superhydrophobic Biosensor with High SERS Activity and Photocatalytic Sterilization Performance

Sensitive detection and efficient inactivation of pathogenic bacteria are crucial for halting the spread and reproduction of foodborne pathogenic bacteria. Herein, a novel Apt-modified PDMS-ZnO/Ag multifunctional biosensor has been developed for high-sensitivity surface-enhanced Raman scattering (SERS) detection along with photocatalytic sterilization towards Salmonella typhimurium (S. typhimurium). The distribution of the electric field in PDMS-ZnO/Ag with different Ag sputtering times was analyzed using a finite-difference time-domain (FDTD) algorithm. Due to the combined effect of electromagnetic enhancement and chemical enhancement, PDMS-ZnO/Ag exhibited outstanding SERS sensitivity. The limit of detection (LOD) for 4-MBA on the optimal SERS substrate (PZA-40) could be as little as 10-9 M. After PZA-40 was modified with the aptamer, the LOD of the PZA-40-Apt biosensor for detecting S. typhimurium was only 10 cfu/mL. Additionally, the PZA-40-Apt biosensor could effectively inactivate S. typhimurium under visible light irradiation within 10 min, with a bacterial lethality rate (Lb) of up to 97%. In particular, the PZA-40-Apt biosensor could identify S. typhimurium in food samples in addition to having minimal cytotoxicity and powerful biocompatibility. This work provides a multifunctional nanoplatform with broad prospects for selective SERS detection and photocatalytic sterilization of pathogenic bacteria.

Facet junction engineering for enhanced SERS activity of Ag/Cu2O composite substrates

Featuring ultra-high sensitivity and molecule-specific detection ability, surface-enhanced Raman scattering (SERS) is suitable for the rapid sensing of trace-level chemicals in biological, environmental, and agricultural samples. Although crystal facet junction engineering is a powerful tool to manipulate the optoelectronic properties of semiconducting materials, its correlation with the SERS sensing activity of noble metal/semiconductor composites has still not been clarified. In this work, Ag was deposited on Cu2O nanocrystals enclosed by different facets, including {100} (cube), {111} (octahedron), and {100}/{111} (truncated octahedron), and a detailed study of their SERS performance was carried out. It was found that Ag/truncated-octahedral Cu2O (Ag/Cu2O(J3)) exhibited superior SERS activity to Ag/cubic Cu2O (Ag/Cu2O(C)) and Ag/octahedral Cu2O (Ag/Cu2O(O)). The {100}/{111} facet junction in Cu2O can promote the separation and transfer of photogenerated charge carriers, which is beneficial for enhancing SERS sensing performance. Moreover, Ag/Cu2O(J3) has a higher content of oxygen vacancies, providing extra interfacial charge-transfer pathways to the analyte, which also contribute to improving the SERS activity. The low detection limit of Ag/Cu2O(J3) was 1 × 10-11 M for 4-nitrobenzenethiol, two orders of magnitude lower than that of Ag/Cu2O(C) and Ag/Cu2O(O). In addition, Ag/Cu2O(J3) could detect CV and R6G at concentrations down to 1 × 10-10 M and 1 × 10-8 M, respectively. The findings of this work can provide insightful information for designing metal/semiconductor substrates toward SERS sensing application by regulating the crystal facet junction.

SERS mapping combined with chemometrics, for accurate quantification of methotrexate from patient samples

Despite the technological development in Raman instrumentation that has democratized access to 2D sample scanning capabilities, most quantitative surface-enhanced Raman scattering (SERS) analyses are still performed by only acquiring a single or a few spectra per sample and performing univariate data analysis on those. This strategy can however reach its limit when analytes need to be detected and quantified in complex matrices. In that case, surface fouling and competition between the target analyte and interfering compounds can impair univariate SERS data analysis, underlining the need for a more in-depth data analysis strategy based on exploiting of full-spectrum information. In this paper, a multivariate data analysis strategy was developed, for analyzing SERS maps of methotrexate (MTX) from patient samples, including all steps from baseline correction, selection of wavelength, and the relevant pixels in the map (image threshold segmentation), as well as quantitative model construction based on partial-least squares regression. Among the different baseline correction methods evaluated, standard normal variable transformation and Savitzky-Golay smoothing proved to be more suitable, while the genetic algorithm wavelength screening method was able to screen out MTX-related SERS spectral regions more efficiently. Importantly, with the here-developed process, it was sufficient to use MTX-spiked commercial serum when building quantitative models, removing the need to work with MTX-spiked patient samples, and consequently enabling time- and resource-saving quantitative analyses. Besides, the developed multivariate data analysis approach showed superior performances compared with univariate analysis, with 30 % improved sensitivity (detection limit of 5.7 µM), 25 % higher reproducibility (average relative standard variation of 15.6 %), and 110 % better accuracy (average prediction error of -10.5 %).

Research Status in the Use of Surface-Enhanced Raman Scattering (SERS) to Detect Pesticide Residues in Foods and Plant-Derived Chinese Herbal Medicines

Surface-enhanced Raman scattering (SERS) technology has unique advantages in the rapid detection of pesticides in plant-derived foods, leading to reduced detection limits and increased accuracy. Plant-derived Chinese herbal medicines have similar sources to plant-derived foods; however, due to the rough surfaces and complex compositions of herbal medicines, the detection of pesticide residues in this context continues to rely heavily on traditional methods, which are time consuming and laborious and are unable to meet market demands for portability. The application of flexible nanomaterials and SERS technology in this realm would allow rapid and accurate detection in a portable format. Therefore, in this review, we summarize the underlying principles and characteristics of SERS technology, with particular focus on applications of SERS for the analysis of pesticide residues in agricultural products. This paper summarizes recent research progress in the field from three main directions: sample pretreatment, SERS substrates, and data processing. The prospects and limitations of SERS technology are also discussed, in order to provide theoretical support for rapid detection of pesticide residues in Chinese herbal medicines.

Research advances of SERS analysis method based on silent region molecules for food safety detection

Food safety is a critical issue that is closely related to people's health and safety. As a simple, rapid, and sensitive detection technique, surface-enhanced Raman scattering (SERS) technology has significant potential for food safety detection. Recently, researchers have shown a growing interest in utilizing silent region molecules for SERS analysis. These molecules exhibit significant Raman scattering peaks in the cellular Raman silent region between 1800 and 2800 cm-1 avoiding overlapping with the SERS spectrum of biological matrices in the range 600-1800 cm-1, which could effectively circumvent matrix effects and improve the SERS accuracy. In this review, the application of silent region molecules-based SERS analytical technique for food safety detection is introduced, detection strategies including label-free detection and labeled detection are discussed, and recent applications of SERS analysis technology based on molecules containing alkyne and nitrile groups, as well as Prussian blue (PB) in the detection of pesticides, mycotoxins, metal ions, and foodborne pathogens are highlighted. This review aims to draw the attention to the silent region molecules-based SERS analytical technique and to provide theoretical support for its further applications in food safety detection.

Optimization of near-infrared reflectance models in determining flavonoid composition of okra (Abelmoschus esculentus L.) pods

Okra pods have been utilized as a functional food due to their rich active ingredient composition, especially the high content of flavonoid compounds. This study conducted near-infrared spectroscopy (NIRS) modeling optimization and external validation based on the flavonoid components of 219 pod samples. Spectral correlation analyses identified two types of spectral response patterns classified as quercetin-3-O-xylose (1-2) glucoside (QOXG) and total flavonoid content (TFC), consisting of six different spectral regions. Different modeling effects were observed for QOXG and TFC with various spectral region combination analyses, where the lower wave-number region contributed more to both flavonoids calibration models. The combination of standard normal variate / "1, 9, 3" / partial least squares was found to be the most effective for developing calibration models for both flavonoids. The resulting models had small root mean square errors of prediction for external validation and high determination coefficients, indicating their usefulness for rapid prediction of flavonoid composition in okra pods.

Advances in surface-enhanced Raman spectroscopy technology for detection of foodborne pathogens

Rapid control and prevention of diseases caused by foodborne pathogens is one of the existing food safety regulatory issues faced by various countries and has received wide attention from all sectors of society. The development of rapid and reliable detection methods for foodborne pathogens remains a hot research area for food safety and public health because of the limitations of complex steps, time-consuming, low sensitivity, or poor selectivity of commonly used methods. Surface-enhanced Raman spectroscopy (SERS), as a novel spectroscopic technique, has the advantages of high sensitivity, selectivity, rapid and nondestructive detection and has exhibited broad application prospects in the determination of pathogenic bacteria. In this study, the enhancement mechanisms of SERS are briefly introduced, then the characteristics and properties of liquid-phase, rigid solid-phase, and flexible solid-phase are categorized. Furthermore, a comprehensive review of the advances in label-free or label-based SERS strategies and SERS-compatible techniques for the detection of foodborne pathogens is provided, and the advantages and disadvantages of these methods are reviewed. Finally, the current challenges of SERS technology applied in practical applications are listed, and the possible development trends of SERS in the field of foodborne pathogens detection in the future are discussed.

Simultaneous and Sensitive Detection of Three Pesticides Using a Functional Poly(Sulfobetaine Methacrylate)-Coated Paper-Based Colorimetric Sensor

Chlorpyrifos (CHL), profenofos (PRO) and cypermethrin (CYP) are widely used in combination to increase crop yields. However, these three pesticides can cause serious harm to human health and do not easily degrade. In this study, a novel visible paper sensor has been prepared successfully and different colorimetric reactions were utilized to detect the three pesticides simultaneously. The sensor was constructed by grafting a zwitterionic polymer onto a cellulose filter (CF) and placing it on a glass surface modified with PDMS. The branch shape was designed to form multiple detection areas, which were modified with specific pesticides and corresponding chromogenic reagents. The as-prepared colorimetric platform exhibited high sensitivity, a short detection time, a good linear response and a low detection limit (LOD) for the three pesticides (chlorpyrifos: y = 46.801 - 1.939x, R² = 0.983, LOD = 0.235 mg/L; profenofos: y = 40.068 + 42.5x, R² = 0.988, LOD = 4.891 mg/L; cypermethrin: y = 51.993 + 1.474x, R² = 0.993, LOD = 4.053 mg/L). The comparison of the results obtained by the proposed paper sensor and those obtained by spectrophotometry further revealed the stability and reliability of the paper sensor. In particular, the color intensity of the interaction between the pesticides and coloring agents could be directly observed by the human eye. The consistency of the colorimetric/optical assay was proven in real target pesticide samples. Thus, this sensing strategy provides a portable, cost-effective, accurate and visualized paper platform, which could be suitable for application in the fruit and vegetable industry for monitoring CHL, PRO and CYP in parallel.

Exploring Surface-Enhanced Raman Spectroscopy (SERS) Characteristic Peaks Screening Methods for the Rapid Determination of Chlorpyrifos Residues in Rice

Surface-enhanced Raman spectroscopy (SERS), coupled with characteristic peak screening methods, was developed for analyzing chlorpyrifos (CM) pesticide residues in rice. Au nanoparticles (AuNPs) were prepared as Raman signal enhancement. Magnesium sulfate (MgSO₄), primary secondary amine (PSA), and C₁₈ were used to purify the rice extraction. A successive projections algorithm (SPA) was performed to identify the optimal characteristic peaks of CM in rice from full Raman spectroscopy. Support vector machine (SVM) and partial least squares (PLS) were implemented to investigate the quantitative analysis models. The results demonstrated that six Raman peaks such as 671, 834, 1016, 1114, 1436, and 1444 cm^-1 were selected by the SPA and SVM models and had better performance using six peaks (only 0.92% of the full spectra variables) with R²_p = 0.97, RMSEP = 2.89 and RPD = 4.26, and the experiment time for a sample was accomplished within 10 min. Recovery for five unknown concentration samples was 97.45-103.96%, and T-test results also displayed no obvious differences between the measured value and the predicted value. The study stated that SERS, combined with characteristic peak screening methods, can be applied to rapidly monitor the chlorpyrifos residue in rice.

Dandelion-like covalent organic frameworks with high-efficiency fluorescence for ratiometric sensing and visual tracking-by-detection of Fe3

Iron ions, one of the most common heavy metal pollutants in industrial waste materials, are continuously actively or passively delivered to the environment. Meanwhile, the importance of Fe³⁺ in biological processes in vivo can not be neglected due to its crucial role in maintaining normal physiological function. Therefore, a ratiometric fluorescence covalent organic framework (TD-COF) was constructed for tracking-by-detection of Fe³⁺. Alkynes-extended 1,3,6,8-tetrakis(4-ethynyl benzaldehyde)-pyrene (TEBPY) with complete planar structure and 2,5-dihydroxyterephthalohydrazide (DHTH) with functional group -OH were selected as the building blocks. The ratiometric fluorescence TD-COF with a dandelion-like structure exhibited its dual emission peaked at 510 nm and 630 nm. It displayed an obvious fluorescence color variation of yellow-red-black in the presence of Fe³⁺. Benefiting from the high luminescent efficiency (QY of 36.4%) and multiple identical binding sites, TD-COF exhibited a wide linear range to Fe³⁺ (0.005-50 μM) with a detection limit of 10.9 nM. Additionally, a smartphone visual sensing platform integrated with TD-COF was developed based on the color transformation and successfully applied to visual smart real-time monitoring Fe³⁺. More surprisingly, the maximum adsorption capacity of TD-COF towards Fe³⁺ was 833.3 mg/g due to the coordination interaction and cationic π-effect. The practicability of the smartphone-integrated ratiometric sensing platform for visual tracking-by-detection of Fe³⁺ was verified by choosing tap water as the actual sample, and the recoveries were calculated to be 98.71-100.88%. This work thus developed COF-based ratiometric sensing of Fe³⁺, which is an attractive candidate for further application in fluorescent sensing and visual monitoring.

Fabrication and evaluation of chitosan modified filter paper for chlorpyrifos detection in wheat by surface-enhanced Raman spectroscopy

BACKGROUND

Chlorpyrifos is a commonly used organophosphorus pesticide in agriculture. However, its neurotoxicity poses a huge threat to human health. In the present study, a chitosan-modified filter paper-based surface enhanced Raman scattering active substrate (Ch/AgNPs/paper) was fabricated and used to detect trace amounts of chlorpyrifos in 120 treated wheat samples.

RESULTS

Results showed that the Ch/AgNPs/paper substrate could be used to enhance the chlorpyrifos spectral fingerprint only up to a concentration of 0.000558 mg L^-1 . Following Raman spectra acquisition, three pre-processing methods, including Savitzky-Golay (Savitsky-Golay filter with a second order polynomial) smoothing with first derivative and second derivative and normalization, were used to reduce baseline variation and increase resolutions of spectral peak features of the original spectra dataset. Then, prediction models based on partial least squares were established for detecting chlorpyrifos pesticide residue in wheat. The partial least squares model with normalization yielded optimal result, with a correlation coefficient of 0.9764, root mean square error of prediction of 1.22 mg L^-1 in the prediction, and relative analysis deviation of 4.12. Five unknown samples were prepared to verify the accuracy of the prediction model. The predicted recoveries were calculated to be between 97.25% and 119.38% with an absolute t value of 0.598. The value of a t-test shows that the prediction model is accurate and reliable.

CONCLUSION

The present study demonstrates that the proposed method can achieve rapid detection of chlorpyrifos in wheat. © 2022 Society of Chemical Industry.

Recent Progress in Nanotechnology-Based Approaches for Food Monitoring

Throughout the food supply chain, including production, storage, and distribution, food can be contaminated by harmful chemicals and microorganisms, resulting in a severe threat to human health. In recent years, the rapid advancement and development of nanotechnology proposed revolutionary solutions to solve several problems in scientific and industrial areas, including food monitoring. Nanotechnology can be incorporated into chemical and biological sensors to improve analytical performance, such as response time, sensitivity, selectivity, reliability, and accuracy. Based on the characteristics of the contaminants and the detection methods, nanotechnology can be applied in different ways in order to improve conventional techniques. Nanomaterials such as nanoparticles, nanorods, nanosheets, nanocomposites, nanotubes, and nanowires provide various functions for the immobilization and labeling of contaminants in electrochemical and optical detection. This review summarizes the recent advances in nanotechnology for detecting chemical and biological contaminations in the food supply chain.

Experimental and theoretical evaluation of crystal facet exposure on the charge transfer and SERS activity of ZnO films

Semiconductors exhibit great potential as a surface enhanced Raman scattering (SERS) substrate due to their low cost, good stability and biocompatibility. However, the extensive application of semiconductors has been restricted by their intrinsically low SERS sensitivity. It is urgently required to design uniform metal oxide substrates with enhanced charge transfer and SERS activity. Herein, three facet-defined ({101̄0}, {0001} and {101̄1}) ZnO films were synthesized via an electrodeposition procedure with the assistance of KCl or ethylenediamine. According to the results, the ZnO films with {0001} and {101̄1} exposed facets exhibit appreciable SERS enhancement factors (EFs) of 1.6 × 10⁴ and 2.8 × 10⁴ for 4-nitrobenzenethiol (4-NBT), as well as a relatively low limit of detection (LOD) down to 1 × 10^-6 M and 5 × 10^-7 M, respectively. Simultaneously, the electrodeposited ZnO films deliver good repeatability and SERS stability, with relative standard deviation (RSD) less than 6% and 85.2% of their original activity retained after 40 days. Theoretical calculations verified that the {0001} and {101̄1} facets can transfer more electrons from ZnO to the molecules on account of their low facet-related electronic work functions, thus generating the noticeable improvement of SERS activity. The current study provides theoretical and technical support for the crystal facet engineering and property improvement of semiconductors.

Quantitative Analysis of Acetone in Transformer Oil Based on ZnO NPs@Ag NWs SERS Substrates Combined with a Stoichiometric Model

Acetone is an essential indicator for determining the aging of transformer insulation. Rapid, sensitive, and accurate quantification of acetone in transformer oil is highly significant in assessing the aging of oil-paper insulation systems. In this study, silver nanowires modified with small zinc oxide nanoparticles (ZnO NPs@Ag NWs) were excellent surface-enhanced Raman scattering (SERS) substrates and efficiently and sensitively detected acetone in transformer oil. Stoichiometric models such as multiple linear regression (MLR) models and partial least square regressions (PLS) were investigated to quantify acetone in transformer oil and compared with commonly used univariate linear regressions (ULR). PLS combined with a preprocessing algorithm provided the best prediction model, with a correlation coefficient of 0.998251 for the calibration set, 0.997678 for the predictive set, a root mean square error in the calibration set (RMSECV = 0.12596 mg/g), and a prediction set (RMSEP = 0.11408 mg/g). For an acetone solution of 0.003 mg/g, the mean absolute percentage error (MAPE) was the lowest among the three quantitative models. For a concentration of 7.29 mg/g, the MAPE was 1.60%. This method achieved limits of quantification and detections of 0.003 mg/g and 1 μg/g, respectively. In general, these results suggested that ZnO NPs@Ag NWs as SERS substrates coupled with PLS simply and accurately quantified trace acetone concentrations in transformer oil.

Dispersive micro solid phase extraction based ionic liquid functionalized ZnO nanoflowers couple with chromatographic methods for rapid determination of aflatoxins in wheat and peanut samples

Aflatoxins (AFs) contaminate agricultural products in a wide range of ways during their harvesting, storage and transport. Therefore, the detection of AFs has certain practical significance. Herein, a dispersive micro solid phase extraction (D-µSPE) technology was constructed based on 1-hexyl-3-methylimidazolium hexafluorophosphate ([HMIM][PF₆]) fabricated ZnO nanoflowers for AFs extraction from food matrix before HPLC procedure. The key parameters affecting the extraction efficiency were studied. Under optimal experimental conditions, the method showed excellent linearity with high correlation coefficients (≥0.994). LOD and LOQ were 0.034 and 0.114 μg/kg for AFB1, 0.024 and 0.082 μg/kg for AFB2, 0.067 and 0.226 μg/kg for AFG1 and 0.025 and 0.084 μg/kg for AFG2. The recovery of actual samples spiked with analytes (at 5, 15 and 20 μg/kg) were from 93.8 to 105.1%. Overall, an accurate AFs analysis method was developed and could be applied to the determination of AFs in various food and agricultural products.

A plasmonic AgNP decorated heterostructure substrate for synergetic surface-enhanced Raman scattering identification and quantification of pesticide residues in real samples

Rapid and on-site Raman spectroscopic identification and quantification of pesticide residues have been restricted to the low instrumental sensitivity of a portable Raman instrument, and no ideal platforms have been reported for analyzing pesticides on real sample surfaces. An efficient method to improve the detection sensitivity is to fabricate a highly sensitive surface-enhanced Raman scattering (SERS) substrate. Here, we present a MOF-derived ZnO@TiO₂ heterostructure combined with plasmonic AgNPs as a SERS sensor to achieve synergetic EM and CM enhancement, exhibiting high sensitivity, excellent signal reproducibility (RSD < 5.9%) and superior stability for analysis of model molecules. The SERS sensor achieved a low detection concentration of 10^-8 M for both CV and R6G molecular solutions on a portable Raman device. As a proof of concept, we modelled a pesticide residue on real samples of dendrobium leaves. Thiram, triazophos and fonofos solutions were selected as analytes for mimicking the function of on-site analysis. The SERS analytical platform showed not only high sensitivity for single- and multi-component identification, but also quantitative detection of pesticide residues on dendrobium leaves. These preliminary investigations indicate that this SERS analytical platform will allow the development and potential applications in rapid and on-site pesticide analysis.

Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy

A novel swarm intelligence algorithm, discretized grey wolf optimizer (GWO), was introduced as a variable selection tool in edible blend oil analysis for the first time. In the approach, positions of wolves were updated and then discretized by logical function. The performance of a wolf pack, the iteration number and the number of wolves were investigated. The partial least squares (PLS) method was used to establish and predict single oil contents in samples. To validate the method, 102 edible blend oil samples containing soybean oil, sunflower oil, peanut oil and sesame oil were measured by an ultraviolet-visible (UV-Vis) spectrophotometer. The results demonstrated that GWO-PLS models can provide best prediction accuracy with least variables compared with full-spectrum PLS, Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). The determination coefficients (R²) of GWO-PLS were all above 0.95. Therefore, the research indicates the feasibility of using discretized GWO for variable selection in rapid determination of quaternary edible blend oil.

Recent Advances in Aptasensors For Rapid and Sensitive Detection of Staphylococcus Aureus

The infection of Staphylococcus aureus (S.aureus) and the spread of drug-resistant bacteria pose a serious threat to global public health. Therefore, timely, rapid and accurate detection of S. aureus is of great significance for food safety, environmental monitoring, clinical diagnosis and treatment, and prevention of drug-resistant bacteria dissemination. Traditional S. aureus detection methods such as culture identification, ELISA, PCR, MALDI-TOF-MS and sequencing, etc., have good sensitivity and specificity, but they are complex to operate, requiring professionals and expensive and complex machines. Therefore, it is still challenging to develop a fast, simple, low-cost, specific and sensitive S. aureus detection method. Recent studies have demonstrated that fast, specific, low-cost, low sample volume, automated, and portable aptasensors have been widely used for S. aureus detection and have been proposed as the most attractive alternatives to their traditional detection methods. In this review, recent advances of aptasensors based on different transducer (optical and electrochemical) for S. aureus detection have been discussed in details. Furthermore, the applications of aptasensors in point-of-care testing (POCT) have also been discussed. More and more aptasensors are combined with nanomaterials as efficient transducers and amplifiers, which appears to be the development trend in aptasensors. Finally, some significant challenges for the development and application of aptasensors are outlined.

SERS-based Au@Ag NPs Solid-phase substrate combined with chemometrics for rapid discrimination of multiple foodborne pathogens

In this study, a surface enhanced Raman scattering (SERS) sensor based on Au@Ag NPs solid-phase substrate combined with chemometrics was constructed for the discrimination of three pathogenic bacteria (Staphylococcus aureus, Escherichia coli and Listeria monocytogenes). The Au@Ag NPs were synthesized and self-assembled on filter paper using the dip-coating method. The good absorbency of the filter paper immobilized the bacteria on the substrate, increased the interaction between the bacteria and the substrate, and enhanced the SERS signal of the bacteria. The main peaks of the bacterial spectra were close to each other, but the relative intensities of the vibrational peaks were significantly different, and each strain exhibited unique Raman peaks. The combination of partial least squared discriminant analysis (PLS-DA) method with bacterial SERS allowed the effective identification of the three bacteria. Moreover, the method was applied for the quantitative detection of Staphylococcus aureus with a minimum detection concentration of 10⁴ cfu/mL.

Simultaneous quantification of deoxymyoglobin and oxymyoglobin in pork by Raman spectroscopy coupled with multivariate calibration

Because of the nutritional advantages and customer acceptance, it is vital to ensure pork meat quality. This study examined the quantification of myoglobin proportions (deoxymyoglobin and oxymyoglobin) by coupling Raman spectroscopy with efficient variables selection chemometrics. Prior to acquiring Raman spectroscopic data, the fractions of myoglobin were determined. Afterward, multivariate calibration methods like partial least square (PLS), competitive adaptive reweighted sampling (CARS-PLS), genetic algorithm-PLS (GA-PLS), and random frog-PLS (RF-PLS) were applied and evaluated. The models' performance was assessed using correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The RF-PLS model achieved optimal results for both deoxymyoglobin and oxymyoglobin, with Rp = 0.8936; RMSEP = 2.91 and RPD = 1.97 for the former and Rp = 0.9762; RMSEP = 1.23 and RPD = 4.47 for the latter, respectively. Therefore, this work demonstrated that Raman spectroscopy paired with RF-PLS could be employed for nondestructive, fast, and easy detection of deoxymyoglobin and oxymyoglobin.

Application of NIR spectroscopy for rapid quantification of acid and peroxide in crude peanut oil coupled multivariate analysis

Two key parameters (acidity and peroxide content) for evaluation of the oxidation level in crude peanut oil have been studied. The titrimetric analysis was carried out for reference data collection. Then, near-infrared spectroscopy in combination with chemometric algorithms such as partial least square (PLS); bootstrapping soft shrinkage-PLS (BOSS-PLS); uninformative variable elimination-PLS (UVE-PLS), and competitive-adaptive reweighted sampling-PLS (CARS-PLS) were attempted and assessed. The correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to individually evaluate the performance of the models. Optimum results were noticed with CARS-PLS, 0.9517 ≤ Rc ≤ 0.9670, 0.9503 ≤ Rp ≤ 0.9637, 0.0874 ≤ RMSEP ≤ 0.5650, and 3.14 ≤ RPD ≤ 3.64. Therefore, this affirmed that the near-infrared spectroscopy coupled with CARS-PLS could be used as a simple, fast, and non-invasive technique for quantifying acid value and peroxide value in crude peanut oil.

Label-free Au NRs-based SERS coupled with chemometrics for rapid quantitative detection of thiabendazole residues in citrus

Citrus is a highly consumed fruit worldwide. However, the excessive use of thiabendazole (TBZ) pesticides during citrus cultivation poses a health risk to people. Hence, a rapid and quantitative method has been established for TBZ determination in citrus by coupling gold nanorods (Au NRs) based surface enhanced Raman scattering (SERS) coupling chemometrics. The results show that support vector machine (SVM) can distinguish TBZ residues of different orders of magnitude with 99.1667% accuracy and that genetic algorithm-partial least squares (GA-PLS) had the best performance in the quantitative prediction of TBZ residues (Rp² = 0.9737, RMSEP = 0.1179 and RPD = 5.85) in citrus. The limit of detection (LOD) was 0.33 μg mL^-1. Furthermore, the proposed method was validated by a standard HPLC method using t-test with no significant difference. Therefore, the proposed Au NRs-based SERS technique can be used for the rapid quantitative analysis of TBZ residues in citrus.

Rapid detection of chlorpyrifos residue in rice using surface-enhanced Raman scattering coupled with chemometric algorithm

Due to the continuous development and progress of society and more and more attention to the quality and safety of food, rapid testing of pesticides in food is of great significance. In this paper, surface-enhanced Raman spectroscopy (SERS) and chemometric algorithms were employed collectively to quantify chlorpyrifos (CP) residues in rice samples. The SERS spectra from different concentrations (0.01-1000 μg/mL) of CP were collected using AgNPs-deposited-ZnO nanoflower (NFs)-like nanoparticles (Ag@ZnO NFs) SERS sensor. Four quantitative chemometric models for CP were comparatively studied, and the competitive adaptive reweighted sampling-partial least squares model achieved the best prediction and practical applicability for predicting CP levels with a limit of detection of 0.01 µg/mL. The results of the student's t-test showed no significant difference between this method and high-performance liquid chromatography (HPLC), and good relative standard deviation (RSD) indicated that this method could be used for the detection of CP in rice.

Rapid field trace detection of pesticide residue in food based on surface-enhanced Raman spectroscopy

Surface-enhanced Raman spectroscopy is an alternative detection tool for monitoring food security. However, there is still a lack of a conclusion of SERS detection with respect to pesticides and real sample analysis, and the summary of intelligent algorithms in SERS is also a blank. In this review, a comprehensive report of pesticides detection using SERS technology is given. The SERS detection characteristics of different types of pesticides and the influence of substrate on inspection are discussed and compared by the typical ways of classification. The key points, including the progress in real sample analysis and Raman data processing methods with intelligent algorithm, are highlighted. Lastly, major challenges and future research trends of SERS analysis of pesticide residue are also addressed. SERS has been proven to be a powerful technique for rapid test of residue pesticides in complex food matrices, but there still is a tremendous development space for future research.

Recent advances of nanomaterial-based optical sensor for the detection of benzimidazole fungicides in food: a review

The abuse of pesticides in agricultural land during pre- and post-harvest causes an increase of residue in agricultural products and pollution in the environment, which ultimately affects human health. Hence, it is crucially important to develop an effective detection method to quantify the trace amount of residue in food and water. However, with the rapid development of nanotechnology and considering the exclusive properties of nanomaterials, optical, and their integrated system have gained exclusive interest for accurately sensing of pesticides in food and agricultural samples to ensure food safety thanks to their unique benefit of high sensitivity, low detection limit, good selectivity and so on and making them a trending hotspot. This review focuses on recent progress in the past five years on nanomaterial-based optical, such as colorimetric, fluorescence, surface-enhanced Raman scattering (SERS), and their integrated system for the monitoring of benzimidazole fungicide (including, carbendazim, thiabendazole, and thiophanate-methyl) residue in food and water samples. This review firstly provides a brief introduction to mentioned techniques, detection mechanism, applied nanomaterials, label-free detection, target-specific detection, etc. then their specific application. Finally, challenges and perspectives in the respective field are discussed.

Inhibition of the Nrf2/p38MAPK pathway involved in deltamethrin-induced apoptosis and fibrosis in quail kidney

Deltamethrin (DLM) is a broad-spectrum and effective pyrethroid insecticide. However, DLM has good residual activity on most surfaces and many insects, so it poses a threat to the environment and health of animals and human. Exposure to DLM can cause kidney injury, but the mechanism is not well understood. Therefore, we investigated the possible mechanism of quail kidney injury induced by chronic exposure to different doses of DLM for 12 weeks. The results showed that chronic exposure to DLM induced apoptosis and fibrosis of quail kidney through the promotion of oxidative stress by down-regulating nuclear factor erythroid 2 related factor 2 (Nrf2), up-regulating the phosphorylation of p38 mitogen-activated protein kinases (p38MAPK). Furthermore, DLM-induced kidney apoptosis in quails as evidenced by increased expression of B-cell lymphoma gene 2-associated X while decreased expression of B-cell lymphoma-extra large. Simultaneously, DLM-induced kidney fibrosis in quails as evidenced by increased expression of fibrosis maker proteins. Overall, the results demonstrate that chronic DLM exposure induces kidney apoptosis and fibrosis via inhibition of the Nrf2/p38MAPK pathway. This study provides a new understanding for the mechanism of DLM-induced quail kidney injury and also provides a theoretical basis for treatment of the DLM poisoning.

A Sensitive SERS Method for Determination of Pymetrozine in Apple and Cabbage Based on an Easily Prepared Substrate

Residual pesticides are one of the major food safety concerns around the world. There is a demand for simple and reliable methods to monitor pesticide residues in foods. In this study, a sensitive method for determination of pymetrozine in apple and cabbage samples using surface-enhanced Raman spectroscopy (SERS) based on decanethiol functionalized silver nanoparticles was established. The proposed method performed satisfactorily with the linear detection range of 0.01-1.00 mg/L and limit of detection (LOD) of 0.01 mg/L in methanol. In addition, it was successfully used to detect pymetrozine in apple and cabbage samples, the LOD was 0.02 and 0.03 mg/L, respectively, and the recoveries of spiked cabbage and apple ranged 70.40-104.00%, with relative standard deviations below 12.18% and 10.33% for intra-day and inter-day tests. Moreover, the results of the correlation test with real cabbage samples of liquid chromatography-tandem mass spectrometry showed that they were highly correlated (slope = 0.9895, R2 = 0.9953). This study provides a sensitive approach for detection of pymetrozine in apple and cabbage, which has great potential for determination of pymetrozine residues in food products.

SERS Sensors Based on Aptamer-Gated Mesoporous Silica Nanoparticles for Quantitative Detection of Staphylococcus aureus with Signal Molecular Release

This work describes a simple and novel biosensor for the quantitative determination of Staphylococcus aureus (S. aureus) based on target-induced release of signal molecules from aptamer-gated aminated mesoporous silica nanoparticles (MSNs) coupled with surface-enhanced Raman scattering (SERS) technology. MSNs were synthesized and then modified with amino groups by (3-aminopropyl) triethoxysilane to make them positively charged. Next, signal molecules (4-aminothiophenol, 4-ATP) were loaded into the pores of MSNs. Then, negatively charged aptamers of S. aureus were assembled on the surface of MSNs through electrostatic interactions. Upon the addition of S. aureus, the assembled aptamers were specifically bound to the bacteria. Consequently, the "gates" were opened, resulting in the release of 4-ATP from the pores of MSNs. The released molecules were measured by a Raman spectrometer, and the intensity of 4-ATP at 1071 cm^-1 was linearly related to the S. aureus concentration. A silver nanoflower silica core-shell structure (Ag NFs@SiO₂) was prepared and it served as a SERS substrate. Under optimized experimental conditions, a good linear relationship (y = 2107.93 + 1536.30x, R² = 0.9956) in the range from 4.7 × 10 to 4.7 × 10⁸ cfu/mL was observed with a limit of detection of 17 cfu/mL. The method was successfully applied for the analysis of S. aureus in fish samples and the recovery rate was 91.3-109%.

Infochemistry and the Future of Chemical Information Processing

Nowadays, information processing is based on semiconductor (e.g., silicon) devices. Unfortunately, the performance of such devices has natural limitations owing to the physics of semiconductors. Therefore, the problem of finding new strategies for storing and processing an ever-increasing amount of diverse data is very urgent. To solve this problem, scientists have found inspiration in nature, because living organisms have developed uniquely productive and efficient mechanisms for processing and storing information. We address several biological aspects of information and artificial models mimicking corresponding bioprocesses. For instance, we review the formation of synchronization patterns and the emergence of order out of chaos in model chemical systems. We also consider molecular logic and ion fluxes as information carriers. Finally, we consider recent progress in infochemistry, a new direction at the interface of chemistry, biology, and computer science, considering unconventional methods of information processing.

Application of benchtop NIR spectroscopy coupled with multivariate analysis for rapid prediction of antioxidant properties of walnut (Juglans regia)

Benchtop near-infrared (NIR) spectroscopy coupled with multivariate analysis was used for the classification and prediction of antioxidant properties of walnut. Total phenolic content (TPC), total flavonoid content (TFC), ABTS assay and FRAP assay were performed spectrophotometrically. The synergy interval partial least square coupled competitive adaptive reweighted sampling (Si-CARS-PLS) was used for the prediction. A decent discrimination using principal component analysis (PCA) was observed by mean of spectroscopic and antioxidant properties data with total cumulative variance of 99.26% (PC1 = 95.07%, PC2 = 2.98%, PC3 = 1.21%) and 96.60% (PC1 = 64.28%, PC2 = 32.32%) respectively. The Si-CARS-PLS yielded optimal performance, R_P = 0.9616, RPD = 3.807 for TPC, R_P = 0.9657, RPD = 3.367 for TFC, R_P = 0.9683, RPD = 2.728 for ABTS assay, and R_P = 0.914, RPD = 2.669 for FRAP assay. These findings revealed that NIR integrated with Si-CARS-PLS could be used for the prediction of antioxidant properties of walnut.

Rapid detection of mercury in food via rhodamine 6G signal using surface-enhanced Raman scattering coupled multivariate calibration

Considering food safety and limitations of biorecognition elements, this study focused on the development of a novel method for predicting mercury (Hg²⁺) in fish and water samples using surface-enhanced Raman scattering (SERS) coupled wavenumber selection chemometric method. Herein, core-shell Au@Ag nanoparticles (Au@Ag NPs) were synthesized as SERS substrate, and rhodamine 6G (R6G) was used as signaling probe for Hg²⁺. In the presence of Hg²⁺, citrate ion of Au@Ag NPs induced complexation and become amalgam causes desorption of R6G occurred, resulted in decreased SERS signal intensity. Compared to surface Plasmon resonance method, SERS coupled genetic algorithm-partial least squares realized good correlation coefficient (0.9745 and 0.9773) in their prediction over the concentration ranges 1.0 × 10² to 1.0 × 10^-3 µg/g. The recovery (88.45 - 94.73%) and precision (coefficient of variations, 3.28 - 5.76%) exhibiting satisfactory results suggested that the proposed method could be employed to predict Hg²⁺ in fish and water samples towards quality and safety monitoring.

Cellulose paper-based SERS sensor for sensitive detection of 2,4-D residue levels in tea coupled uninformative variable elimination-partial least squares

Food safety is a growing concern in recent years. This work presents the design of a simple and sensitive method for predicting 2,4-D (2,4-dichlorophenoxyacetic acid) residue levels in green tea extract employing surface-enhanced Raman spectroscopy (SERS) coupled uninformative variable elimination-partial least squares (UVE-PLS). Herein, SERS active citrate functionalized silver nanoparticles (AgNPs) with enhancement factor 1.51 × 10⁸ was used to prepare cellulose paper (common office) templated SERS sensor for acquiring SERS spectra of 2,4-D. The principle of the work was based on the interaction between 2,4-D and citrate group of AgNPs via chlorine atoms in the concentration range 1.0 × 10^-4 to 1.0 × 10³ µg/g. Three different wavenumber selection chemometric algorithms were studied comparatively to build an optimum calibration model, among them UVE-PLS showed enhanced performance as evident from the RPD value of 6.01 and Rp = 0.9864. Under optimized experimental condition proposed paper-based SERS sensor exhibited detection limit and RSD of 1.0 × 10^-4 µg/g and <5%, respectively. In addition, the validation results by HPLC method were satisfactory (p > 0.05).

Recent progress in mycotoxins detection based on surface-enhanced Raman spectroscopy

Mycotoxins are toxic compounds naturally produced by certain types of fungi. The contamination of mycotoxins can occur on numerous foodstuffs, including cereals, nuts, fruits, and spices, and pose a major threat to humans and animals by causing acute and chronic toxic effects. In this regard, reliable techniques for accurate and sensitive detection of mycotoxins in agricultural products and food samples are urgently needed. As an advanced analytical tool, surface-enhanced Raman spectroscopy (SERS), presents several major advantages, such as ultrahigh sensitivity, rapid detection, fingerprint-type information, and miniaturized equipment. Benefiting from these merits, rapid growth has been observed under the topic of SERS-based mycotoxin detection. This review provides a comprehensive overview of the recent achievements in this area. The progress of SERS-based label-free detection, aptasensor, and immunosensor, as well as SERS combined with other techniques, has been summarized, and in-depth discussion of the remaining challenges has been provided, in order to inspire future development of translating the techniques invented in scientific laboratories into easy-to-operate analytic platforms for rapid detection of mycotoxins.

A SERS aptasensor based on AuNPs functionalized PDMS film for selective and sensitive detection of Staphylococcus aureus

In this study, a sensitive biosensor was developed based on aptamer functionalized polydimethylsiloxane (PDMS) film for the detection of Staphylococcus aureus (S. aureus) using surface-enhanced Raman scattering (SERS) technology. Initially, the surface of PDMS film was chemically modified by piranha solution and 3-Aminopropyltriethoxysilane (APTES), and then AuNPs-PDMS film was prepared by coating gold nanoparticles (AuNPs) through electrostatic interaction. Next, the aptamers were immobilized on the AuNPs-PDMS membrane via gold-sulfur bond to form the capture substrate. Meanwhile, gold-silver core-shell nanoflowers (Au@Ag NFs) modified with mercaptobenzoic acid (4-MBA) and aptamers were applied as a signal probe. In the presence of the target, the signal molecular probe and the capturing substrate specifically combined with the target and resulted in a sandwich structure "capture substrate-target-signal molecular probe". Under the optimized experimental condition, the signal of 4-MBA at 1085 cm^-1 was linearly related to the S. aureus concentration in the range of 4.3 × 10 cfu mL^-1-4.3 × 10⁷ cfu mL^-1 (y = 326.91x-117.62, R² = 0.9932) with a detection limit of 13 cfu mL^-1. The method was successfully applied to spiked actual samples and a 92.5-110% recovery rate was achieved.