Detection of systemic pesticide residues in tea products at trace level based on SERS and verified by GC-MS

A novel method for quantitative determination of multiple substances using Raman spectroscopy combined with CWT

The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R2 is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.

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.

On-site rapid detection of multiple pesticide residues in tea leaves by lateral flow immunoassay

The application of pesticides (mostly insecticides and fungicides) during the tea-planting process will undoubtedly increase the dietary risk associated with drinking tea. Thus, it is necessary to ascertain whether pesticide residues in tea products exceed the maximum residue limits. However, the complex matrices present in tea samples comprise a major challenge in the analytical detection of pesticide residues. In this study, nine types of lateral flow immunochromatographic strips (LFICSs) were developed to detect the pesticides of interest (fenpropathrin, chlorpyrifos, imidacloprid, thiamethoxam, acetamiprid, carbendazim, chlorothalonil, pyraclostrobin, and iprodione). To reduce the interference of tea substrates on the assay sensitivity, the pretreatment conditions for tea samples, including the extraction solvent, extraction time, and purification agent, were optimized for the simultaneous detection of these pesticides. The entire testing procedure (including pretreatment and detection) could be completed within 30 min. The detected results of authentic tea samples were confirmed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), which suggest that the LFICS coupled with sample rapid pretreatment can be used for on-site rapid screening of the target pesticide in tea products prior to their market release.

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

A facile heat-treatment solid phase microextraction method for SERS detection of isocarbophos in tea using a hand-held Raman spectrometer

A facile sensor system based on heat-treatment solid phase microextraction and Surface-Enhanced Raman Scattering (HT-SPME-SERS) was established for in-situ detection of isocarbophos in complex tea matrix. Starting from the action optimization of temperature control unit and air flow control unit, pesticide molecules volatilizing from solution are efficiently captured by substrate and generate real-time SERS signals by a hand-held Raman spectrometer, and the sensor system based on HT-SPME-SERS was finally established. A novel SERS substrate of Cu@rGO@Ag was developed as HT-SPME-SERS material, where reduced graphene oxide (rGO) enriched pesticide molecules by π-π stacking. A superior detection sensitivity brought by the ultra-high enhancement effect of Cu@rGO@Ag substrate was obtained. A good linear relationship between Raman intensity and isocarbophos concentration was obtained and the limit of detection (LOD) was as low as 0.00451 ppm. The detection results obtained from the sensor system have been verified by gas chromatography-mass spectrometer (GC-MS), showing its great application potential for the safety of agricultural products.

Au nanogap SERS substrate for the carbaryl pesticide determination in juice and milk using chemomterics

Conventional spectroscopic methods like IR, and Raman are not very effective at detecting low levels of pesticides or harmful chemicals in food matrices. A quick, highly accurate approach that can identify pesticides present in different food products at lower levels must be developed in order to address this problem and ensure food safety. In this study, a highly sensitive and uniform wafer-scale Au nanogap surface-enhanced Raman spectroscopy (SERS) substrate was used for the quantitative analysis of carbaryl pesticide levels in standard solution, mango juice, and milk samples using chemometrics. Carbaryl was detected up to 3 ppb concentration levels for all three group of samples. However, due to the higher sensitivity, uniformity, and enhancement factors of the SERS substrate used in this investigation, the limit of detection (LOD) values for the standard solution, mango juice, and milk were 0.37 ppb, 0.57 ppb, and 0.15 ppb at 1380 cm^-1, 1380 cm^-1, and 1364 cm^-1 wavenumber ranges. In order to predict different carbaryl concentrations (1, 2, 3, 4, and 5 ppb), the variable importance in projection (VIP) method combined with partial least squares regression (PLSR) and attained the coefficient of determination (R²) values of 0.994, 0.989, and 0.978 along with minimum root mean square error (RMSE) values of 0.112, 0.190, and 0.278 ppb for the prediction datasets. Furthermore, PLS-DA was able to distinguish between pure and adulterated samples with the highest classification accuracy of 100 % for a standard solution, and mango juice and 94.4 % for milk samples. Considering this, we can conclude that the SERS Au Nanogap substrate can rapidly and effectively detect carbaryl pesticides quantitatively and qualitatively in mango juice and milk.

Recent advances in electrochemical and optical sensing of the organophosphate chlorpyrifos: a review

Chlorpyrifos (CP) is one of the most popular organophosphorus pesticides that is commonly used in agricultural and nonagricultural environments to combat pests. However, several concerns regarding contamination due to the unmitigated use of chlorpyrifos have come up over recent years. This has popularized research on various techniques for chlorpyrifos detection. Since conventional methods do not enable smooth detection, the recent trends of chlorpyrifos detection have shifted toward electrochemical and optical sensing techniques that offer higher sensitivity and selectivity. The objective of this review is to provide a brief overview of some of the important and innovative contributions in the field of electrochemical and optical sensing of chlorpyrifos with a primary focus on the comparative advantages and shortcomings of these techniques. This review paper will help to offer better perspectives for research in organophosphorus pesticide detection in the future.

Rapid Determination of Mixed Pesticide Residues on Apple Surfaces by Surface-Enhanced Raman Spectroscopy

Chlorpyrifos (CPF) and 2,4-dichlorophenoxyacetic acid (2,4-D) are insecticides and herbicides which has been widely used on farms. However, CPF and 2,4-D residues on corps can bring high risks to human health. Accurate detection of pesticide residues is important for controlling health risks caused by CPF and 2,4-D. Therefore, we developed a fast, sensitive, economical, and lossless surface-enhanced Raman spectroscopy (SERS)-based method for pesticide detection. It can rapidly and simultaneously determine the CPF and 2,4-D mixed pesticide residues on an apple surface at a minimum of 0.001 mg L⁻¹ concentration, which is far below the pesticide residue standard in China and the EU. The limits of detection reach down to 1.28 × 10⁻⁹ mol L⁻¹ for CPF and 2.47 × 10⁻¹⁰ mol L⁻¹ for 2,4-D. The limits of quantification are 4.27 × 10⁻⁹ mol L⁻¹ and 8.23 × 10⁻¹⁰ mol L⁻¹ for CPF and 2,4-D. This method has a great potential for the accurate detection of pesticide residues, and may be applied to other fields of agricultural products and food industry.

Study on the Absorption and Conduction Properties of Vanisulfane in Tobacco

The purposes of this study were to explore the systemic properties of vanisulfane in tobacco plant and to provide a reference for the rational use of vanisulfane in the field. After the tobacco plants were treated by hydroponics and foliar spraying, the contents of vanisulfane in root and stem leaf were detected by high-performance liquid chromatography tandem high-resolution mass spectrometry (UPLC-HRMS), and the position of vanisulfane in root and stem leaf was real-time observed through fluorescence two-photon confocal microscope. UPLC-HRMS results showed that the contents of vanisulfane in root and stem leaf gradually increased with the extension of processing time, and after 12 h treatment, the contents of vanisulfane in root and stem leaf reached the maximum levels of 31.95 and 0.215 mg/kg, respectively. In addition, fluorescence two-photon confocal microscope results showed that vanisulfane could observe in the root and stem leaf. These results showed that vanisulfane had excellent upward and downward of systemic in tobacco plants, which is helpful to guide a reference for the rational use of vanisulfane in the field.

Optimum synthesis of cactus-inspired SERS substrate with high roughness for paraquat detection

Paraquat is a highly effective herbicide and widely used in agricultural production. However, paraquat residue is harmful for human health and can cause irreversible hazard. Thus, it is crucial for monitoring of paraquat residues. In this paper, an efficient SERS platform based on cactus-inspired nanoparticles is proposed for sensitive detection of paraquat. The cactus-liked nanoparticles obtained from one-pot stepwise reduction method possess multiple spiny structures and can produce abundant hot spots, resulting in remarkable SERS performance. SEM, TEM, UV-vis and Raman tests were conducted to characterize and optimize the morphology of cactus-liked nanoparticles under different preparation conditions. The synthesis mechanism and corresponding parameters influence mechanism of cactus-liked nanoparticles were explored in detail. Optimized substrate exhibited a high sensitivity with the detectable concentration of crystal violet (CV) down to 10^-9 M and an excellent reproducibility proved by SERS mapping. Furthermore, it behaved good linear relationship with a correlation coefficient (R²) of 96.89% between Raman intensities and concentrations of paraquat, which indicates the SERS substrate prepared with cactus-liked nanoparticles could offer a great potential for identification of paraquat.

Raman spectral classification algorithm of cephalosporin based on VGGNeXt

The classification algorithm VGGNeXt of cephalosporin Raman spectrum is proposed. The low resolution spectrum of portable Raman spectrometer is predicted by using the high resolution spectrum training of benchtop Raman spectrometer.

Multi-residue analytical method development and risk assessment of 56 pesticides and their metabolites in tea by chromatography tandem mass spectroscopy

A modified QuEChERS method coupled to chromatography tandem mass spectroscopy was established and used to identify 56 pesticides and 21 metabolites residues in tea samples. The average recoveries for the target compounds ranged from 71% to 109% with RSDs of 1-17%. Pesticides and metabolites residues in 248 tea samples from China were investigated by the developed method, and the dietary intake risk for consumers was estimated. The results showed that 36 pesticides and 14 metabolites were detected with concentrations of 0.0050-7.7 mg/kg. There are unlikely to present a public health concern for Chinese consumers. The acute risk of pesticides would be exaggerated when calculated by the residue level in tea instead of tea infusion. The unauthorized and banned pesticide contributed to the chronic and acute hazard index by 68% and 36%, respectively. The cumulative risk of residual pesticides and their metabolites on human health deserve further attention.

Gold-based immunochromatographic assay strip for the detection of quinclorac in foods

In this study, a highly specific and sensitive monoclonal antibody (mAb) against quinclorac (Qui) was prepared. Based on the selected mAb, 2G3, an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) and an immunochromatographic strip assay were established for the detection of Qui in actual samples. The 50%-inhibitory concentration of mAb 2G3 against Qui was 48.763 ng mL^-1. No cross-reaction with other quinolines indicated that mAb 2G3 had high specificity. The recovery of the established ic-ELISA method was in the range of 85.6% to 98.9%. The cut-off value of Qui in cucumber and tomato by immunochromatographic strip was 200 ng g^-1. The analysis results of ic-ELISA and immunochromatographic strip assay were consistent with the results of LC-MS/MS, which further proved that the established ic-ELISA and immunochromatographic strip assay could provide valuable tools for the rapid detection of Qui residues in cucumber and tomato samples.

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.

Controllable Self-Assembly of SERS Hotspots in Liquid Environment

Controllable synthesis of novel metal nanoparticles and effective capture of hotspots are of great significance for SERS (surface-enhanced Raman spectroscopy) detection. Therefore, in this paper, a green controllable synthesis method of gold nanoparticle was achieved via epigallocatechin gallate reduction. Different morphologies of gold nanoparticles were synthesized just by changing the solution pH values, and the growth kinetics of AuNPs (gold nanoparticles) were systematically studied. The synthetic AuNPs were put in a droplet to study dynamic variations of self-assembly SERS hotspots from the liquid sol state to the solid dry state. The addition of halogen ions in the droplet can controllably regulate the self-assembly three-dimensional hotspot model of gold nanoparticles in the evaporation process of a droplet, during which the most enhancement effect can be easily captured. The dynamically changing images of nanoparticles in the process were graphically described based on the internal interaction forces of a droplet. Two stronger areas in the changes of SERS intensity were achieved with a high concentration of halogen ions, while only one maximum intensity area was obtained with a low concentration of halogen ions added. This method can effectively avoid complex and unpredictable microenvironments of SERS substrates in the liquid drop, further improving the reproducibility of SERS detection as well as broadening it to biological applications.

Zero-Background Surface-Enhanced Raman Scattering Detection of Cymoxanil Based on the Change of the Cyano Group after Ultraviolet Irradiation

A zero-background method based on surface-enhanced Raman scattering (SERS) was developed for the rapid determination of cymoxanil residue in food. Because of the influence of complex matrices, conventional Raman spectroscopy has multiple peaks that overlap with those of target molecules, which makes qualitative and quantitative detection difficult. However, the cyano group (C≡N) of cymoxanil after ultraviolet irradiation has a special characteristic peak in the Raman-silent region (1800-2800 cm^-1), which eliminates the possible background interference. The intensity of the characteristic peak at 2130 cm^-1 exhibited a good linear relationship (R² = 0.9907) with the concentration of cymoxanil in the range of 1.0-50.0 mg/L, whose limit of detection was 0.5 mg/L. The novel method was also applied to the detection of cymoxanil residue in real samples such as cucumber and grape, and the results were in good agreement with those from high-performance liquid chromatography analysis. This revealed that the SERS method has great potential in the detection of cymoxanil in fruits and vegetables. Moreover, ultraperformance liquid chromatography-quadrupole-time-of-flight-mass spectrometry (UPLC-QTOF/MS) was adopted to identify the photoproducts of cymoxanil. The photolysis mechanism was explored by SERS and the UPLC-QTOF/MS technique, which provided basic information on photodegradation of cymoxanil.