Au@Ag nanoflowers based SERS coupled chemometric algorithms for determination of organochlorine pesticides in milk

Towards solving the reproducibility crisis in surface-enhanced Raman spectroscopy-based pesticide detection

Growing concerns about pesticide residues in agriculture are pushing the scientific community to develop innovative and efficient methods for detecting these substances at low concentrations down to the molecular level. In this context, surface-enhanced Raman spectroscopy (SERS) is a powerful analytical method that has so far already undergone some validation for its effectiveness in pesticide detection. However, despite its great potential, SERS faces significant difficulties obtaining reproducible and accurate pesticide spectra, particularly for some of the most widely used pesticides, such as malathion, chlorpyrifos, and imidacloprid. Those inconsistencies can be attributed to several factors, such as interactions between pesticides and SERS substrates and the variety of substrates and solvents used. In addition, differences in the equipment used to obtain SERS spectra and the lack of standards for control experiments further complicate the reproducibility and reliability of SERS data. This review systematically discusses the problems mentioned above, including a comprehensive analysis of the challenges in precisely evaluating SERS spectra for pesticide detection. We not only point out the existing limitations of the method, which can be traced in previous review works, but also offer practical recommendations to improve the quality and comparability of SERS spectra, thereby expanding the potential applications of the method in such an essential field as pesticide detection.

Ag@Au core-shell nanoparticle-based surface-enhanced Raman scattering coupled with chemometrics for rapid determination of chloramphenicol residue in fish

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 antibiotic 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 × 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 × 10-5 µg/mL. Thus, an SERS technique was established based on Ag@Au CSNPs combined with VCPA-PLS to rapidly detect CAP in tilapia.

Surface-Enhanced Raman Spectroscopy Using a Silver Nanostar Substrate for Neonicotinoid Pesticides Detection

Surface-enhanced Raman spectroscopy (SERS) has been introduced to detect pesticides at low concentrations and in complex matrices to help developing countries monitor pesticides to keep their concentrations at safe levels in food and the environment. SERS is a surface-sensitive technique that enhances the Raman signal of molecules absorbed on metal nanostructure surfaces and provides vibrational information for sample identification and quantitation. In this work, we report the use of silver nanostars (AgNs) as SERS-active elements to detect four neonicotinoid pesticides (thiacloprid, imidacloprid, thiamethoxam and nitenpyram). The SERS substrates were prepared with multiple depositions of the nanostars using a self-assembly approach to give a dense coverage of the AgNs on a glass surface, which ultimately increased the availability of the spikes needed for SERS activity. The SERS substrates developed in this work show very high sensitivity and excellent reproducibility. Our research opens an avenue for the development of portable, field-based pesticide sensors, which will be critical for the effective monitoring of these important but potentially dangerous chemicals.

Flexible SERS sensor using AuNTs-assembled PDMS film coupled chemometric algorithms for rapid detection of chloramphenicol in food

The misuse of chloramphenicol (CAP) has led to the development of drug-resistant strains that pose significant threats to public health. Here, we propose a universal flexible surface-enhanced Raman spectroscopy (SERS) sensor utilizing gold nanotriangles (AuNTs) and polydimethylsiloxane (PDMS) film for rapid detection of CAP in food samples. Initially, AuNTs@PDMS with unique optical and plasmonic properties were used to collect spectra of CAP. Afterward, four chemometric algorithms were executed and compared. Accordingly, random frog-partial least squares (RF-PLS) exhibited optimum results with correlation coefficient of prediction (Rp = 0.9802) and the lowest root-mean-square error of prediction (RMSEP = 0.348 µg/mL). Furthermore, the sensor's efficacy to detect CAP in milk samples was confirmed, and the findings were compatible with the conventional HPLC approach (P > 0.05). Therefore, the proposed flexible SERS sensor could effectively be used to monitor milk quality and safety.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Qualitative and quantitative analysis of four benzimidazole residues in food by surface-enhanced Raman spectroscopy combined with chemometrics

In this study, surface-enhanced Raman spectroscopy (SERS) combined with chemometric methods were developed for qualitative and quantitative analysis of four benzimidazole (BMZs) residues in corn. Sulfhydryl functionalized Fe₃O₄@SiO₂@Ag-SH magnetic SERS substrates were prepared to obtain the SERS spectra of four BMZs for chemometric analysis. The partial least squares regression discrimination analysis (PLS-DA) model performed best, with a recall rate upwards 99.17%, and could successfully distinguish four BMZs. Under the support vector machine regression (SVR) model, the detection limits of carbendazim, benomyl, thiophanate-methyl and thiabendazole were 0.055 mg/L, 0.056 mg/L, 0.067 mg/L and 0.093 mg/L, respectively; the average recovery was in the range of 85.6%-107.5%. Furthermore, the method verified by HPLC, and the results showed that there was no significant difference between two methods (p > 0.05). Therefore, the strategy based on SERS coupling chemometrics can be served as a promising tool for rapid determination of BMZs residues in food.

Silver nanostar films for surface-enhanced Raman spectroscopy (SERS) of the pesticide imidacloprid

Strategies for synthetic control of anisotropic metal nanostructures have grown in recent years in part due to their great potential for application as surface-enhanced Raman scattering (SERS) sensing substrates. It has been shown that SERS using silver substrates is a powerful tool for identification and qualification of trace chemical analysis on the basis of their unique molecular vibrations. In this work, we synthesized star-shaped silver nanostructures and fabricated SERS substrates to use the SERS enhancement of the Raman signal to detect neonicotinoid pesticides. These silver nanostar substrates were prepared by assembling the nanostar particles on a glass substrate surface using a self-assembly technique with various layers of silver nanostars film. The silver nanostar distribution on the solid substrate surface was found to have good reproducibility, reusability and were a stable SERS substrate giving SERS enhancements for pesticide detection at concentrations as low as 10⁻⁶ mg/ml. The distribution of these silver nanostars on the surface allowed excellent reproducibility of the detection with a low relative standard derivation (RSD) of SERS intensity of 8%. This work potentially builds a platform for an ultrasensitive detector where samples can be probed with little to no pre-processing and a range of pollutants can be detected at very low levels.

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.

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.

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.

Recent advances in the application of Raman spectroscopy for fish quality and safety analysis

Fish is one of the highly demanded aquatic products, and its quality and safety play a pivotal role in daily diet. However, the possible hazardous substance in perishable fish both in pre- and postharvest periods may decrease their values and pose a threat to public health. Laborious and expensive traditional methods drive the need of developing effective tools for detecting fish quality and safety properties in a rapid, nondestructive, and effective manner. Recent advances in Raman spectroscopy (RS) and surface-enhanced Raman scattering (SERS) have shown enormous potential in various aspects, which largely boost their applications in fish quality and safety evaluation. They have incomparable merits such as providing molecule fingerprint information and allowing for rapid, sensitive, and noninvasive detection with simple sample preparation. This review provides a comprehensive overview focusing on the applications of RS and SERS for fish quality assessment and safety inspection, highlighting the hazardous substance and illegal behavior both in preharvest (veterinary drug residues and environmental pollutants) and postharvest (freshness and illegal behavior) particularly. Moreover, challenges and prospects are also proposed to facilitate the vigorous development of RS and SERS. This review is aimed to emphasize potential opportunities for applying RS and SERS as promising techniques for routine food quality and safety detection. PRACTICAL APPLICATION: With these applications, it can be clearly indicated that RS and SERS are promising and powerful in fish quality and safety surveillance, thereby reducing the occurrence of commercial fraud and food safety issues. More efforts still should be concentrated on exploiting the high-performance Raman instruments, establishing a universal Raman database, developing reproducible SERS substrates and combing RS with other versatile spectral techniques to promote these technologies from laboratory to practice. It is hoped that this review should arouse more research interests in RS and SERS technologies for fish quality and safety surveillance, as well as provide more insights to make a breakthrough.

Applications of surface-enhanced Raman spectroscopy in environmental detection

As the human population grows, the anthropogenic impacts from various agricultural and industrial processes produce unwanted contaminants in the environment. The accurate, sensitive and rapid detection of such contaminants is vital for human health and safety. Surface-enhanced Raman spectroscopy (SERS) is a valuable analytical tool with wide applications in environmental contaminant monitoring. The aim of this review is to summarize recent advancements within SERS research as it applies to environmental detection, with a focus on research published or accessible from January 2021 through December 2021 including early-access publications. Our goal is to provide a wide breadth of information that can be used to provide background knowledge of the field, as well as inform and encourage further development of SERS techniques in protecting environmental quality and safety. Specifically, we highlight the characteristics of effective SERS nanosubstrates, and explore methods for the SERS detection of inorganic, organic, and biological contaminants including heavy metals, pharmaceuticals, plastic particles, synthetic dyes, pesticides, viruses, bacteria and mycotoxins. We also discuss the current limitations of SERS technologies in environmental detection and propose several avenues for future investigation. We encourage researchers to fill in the identified gaps so that SERS can be implemented in a real-world environment more effectively and efficiently, ultimately providing reliable and timely data to help and make science-based strategies and policies to protect environmental safety and public health.

A photo-responsive p-Si/TiO2/Ag heterostructure with charge transfer for recyclable surface-enhanced Raman scattering substrates

A Si/TiO2/Ag heterostructure is prepared as a recyclable SERS substrate with EF of 1.23 × 1012 and excellent repeatability, which can boost performance effectively by the synergistic contribution of the EM and CT enhancement effects.