Rapid detection of chlorpyrifos pesticide residue in tea using surface-enhanced Raman spectroscopy combined with chemometrics

A tailored dual core-shell magnetic SERS substrate with precise shell-thickness control for trace organophosphorus pesticides residues detection

For addressing the challenges of strong affinity SERS substrate to organophosphorus pesticides (OPs), herein, a rapid water-assisted layer-by-layer heteronuclear growth method was investigated to grow uniform UiO-66 shell with controllable thickness outside the magnetic core and provide abundant defect sites for OPs adsorption. By further assembling the tailored Au@Ag, a highly sensitive SERS substrate Fe3O4-COOH@UiO-66/Au@Ag (FCUAA) was synthesized with a SERS enhancement factor of 2.11 × 107. The substrate's suitability for the actual vegetable samples (cowpeas and peppers) was confirmed under both destructive and non-destructive detection conditions, showing a strong SERS response to fenthion and triazophos, with limits of detection of 1.21 × 10-5 and 2.96 × 10-3 mg/kg in the vegetables under destructive conditions, and 0.13 and 1.39 ng/cm2 for non-destructive detection, respectively. The FCUAA substrate had high SERS performance, effective adsorption capability for OPs, and demonstrated good applicability, thus exhibiting great potential for rapid detection of trace OPs residues in the food industry.

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.

SERS combined with QuEChERS using NBC and Fe3O4 MNPs as cleanup agents to rapidly and reliably detect chlorpyrifos pesticide in citrus

The surface-enhanced Raman spectroscopy (SERS) technique is being increasingly used for the detection of pesticide residues in agricultural products. However, there are large amounts of fluorescence-producing substances in agricultural products, which seriously affect the Raman signal of the analyte. In this paper, the QuEChERS method was used to remove interfering fluorescent substances in the analyte, and the purification effects of different doses of nano bamboo charcoal (NBC) and Fe3O4 magnetic nanoparticle (Fe3O4 MNP) adsorbents were studied. Meanwhile, the Raman spectral acquisition conditions (AuNPs, test solution, and NaCl) were optimized based on the orthogonal test method. The results showed that 300 µL AuNPs, 40 µL test solution, and 100 µL 1.5% NaCl gave the best SERS response effect. 12.5 mg NBC combined with 10 mg Fe3O4 MNPs could effectively remove the interfering substances from citrus. The Raman spectra of chlorpyrifos molecules were theoretically modeled using density-functional theory (DFT). By comparing the DFT results with the actual tests, five feature peaks, at 338, 522, 558, 672, and 1600 cm-1, were obtained for the detection of chlorpyrifos pesticide residues in citrus. Based on the Raman feature peak intensity at 672 cm-1, the concentration of chlorpyrifos in citrus showed a good linear relationship (R2 = 0.9979) in the concentration range of 3-20 mg kg-1. The recovery rate was 92.12% to 98.38%, and the relative standard deviation (RSD) was 1.77% to 5.29%. The lowest detection concentration was about 3 mg kg-1, and the detection time of a single sample could be completed within 15 min. This study showed that the combination of SERS and QuEChERS preprocessing methods could achieve rapid detection of chlorpyrifos pesticide residues in citrus.

Rapid detection of residual chlorpyrifos and pyrimethanil on fruit surface by surface-enhanced Raman spectroscopy integrated with deep learning approach

Chlorpyrifos and pyrimethanil are widely used insecticides/fungicides in agriculture. The residual pesticides/fungicides remaining in fruits and vegetables may do harm to human health if they are taken without notice by the customers. Therefore, it is important to develop methods and tools for the rapid detection of pesticides/fungicides in fruits and vegetables, which are highly demanded in the current markets. Surface-enhanced Raman spectroscopy (SERS) can achieve trace chemical detection, while it is still a challenge to apply SERS for the detection and identification of mixed pesticides/fungicides. In this work, we tried to combine SERS technique and deep learning spectral analysis for the determination of mixed chlorpyrifos and pyrimethanil on the surface of fruits including apples and strawberries. Especially, the multi-channel convolutional neural networks-gate recurrent unit (MC-CNN-GRU) classification model was used to extract sequence and spatial information in the spectra, so that the accuracy of the optimized classification model could reach 99% even when the mixture ratio of pesticide/fungicide varied considerably. This work therefore demonstrates an effective application of using SERS combined deep learning approach in the rapid detection and identification of different mixed pesticides in agricultural products.

Non-destructive detection and recognition of pesticide residue levels on cauliflowers using visible/near-infrared spectroscopy combined with chemometrics

In this study, two prediction models were developed using visible/near-infrared (Vis/NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) for the detection of pesticide residues of avermectin, dichlorvos, and chlorothalonil at different concentration levels on the surface of cauliflowers. Five samples of each of the three different types of pesticide were prepared at different concentrations and sprayed in groups on the surface of the corresponding cauliflower samples. Utilizing the spectral data collected in the Vis/NIR as input values, comparison and analysis of preprocessed spectral data, and regression coefficient (RC), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) were used in turn to downscale the data to select the main feature wavelengths, and PLS-DA and LS-SVM models were built for comparison. The results showed that the RC-LS-SVM was the best discriminant model for detecting avermectin residues concentration on the surface of cauliflowers, with a prediction set discriminant accuracy of 98.33%. For detecting different concentrations of dichlorvos, the SPA-LS-SVM had the best predictive accuracy of 95%. The accuracy of the model based on CARS-PLS-DA to identify chlorothalonil at different concentration levels on cauliflower surfaces reached 93.33%. The results demonstrated that the Vis/NIR spectroscopy combined with chemometrics could quickly and effectively identify pesticide residues on cauliflower surfaces, affording a certain reference for the rapid recognition of different pesticide residue concentrations on cauliflower surfaces. PRACTICAL APPLICATION: Vis/NIR spectroscopy can detect the concentration levels of pesticide residues on the surface of cauliflowers and help food regulators quickly and non-destructively detect traces of pesticides in food, providing a guarantee for food safety. The technique also provides a basis for determining pesticide residue concentrations on the surface of other vegetables.

The bitter side of teas: Pesticide residues and their impact on human health

Tea (Camellia sinensis) is one of the most widely consumed non-alcoholic beverages globally, known for its rich composition of bioactive compounds that offer various health benefits to humans. However, the cultivation of tea plants often faces challenges due to their high vulnerability to pests and diseases, resulting in the heavy use of pesticides. Consequently, pesticide residues can be transferred to tea leaves, compromising their quality and safety and potentially posing risks to human health, including hormonal and reproductive disorders and cancer development. In light of these concerns, this review aims to: (I) present the maximum limits of pesticide residues established by different international regulatory agencies; (II) explore the characteristics of pesticides commonly employed in tea cultivation, encompassing aspects such as digestion, bioaccessibility, and the behavior of pesticide transfer; and (III) discuss the effectiveness of detection and removal methods for pesticides, the impacts of pesticides on both tea plants and human health and investigate emerging alternatives to replace these substances. By addressing these critical aspects, this review provides valuable insights into the management of pesticide residues in tea production, with the goal of ensuring the production of safe, high-quality tea while minimizing adverse effects on human health.

Detection methods, migration patterns, and health effects of pesticide residues in tea

Due to its rich health benefits and unique cultural charm, tea drinking is increasingly popular with the public in modern society. The safety of tea is the top priority that affects the development of tea industry and the health of consumers. During the process of tea growth, pesticides are used to prevent the invasion of pests and diseases with maintaining high quality and stable yield. Because hot water brewing is the traditional way of tea consumption, water is the main carrier for pesticide residues in tea into human body accompanied by potential risks. In this review, pesticides used in tea gardens are divided into two categories according to their solubility, among which water-soluble pesticides pose a greater risk. We summarized the methods of the sample pretreatment and detection of pesticide residues and expounded the migration patterns and influencing factors of tea throughout the process of growth, processing, storage, and consumption. Moreover, the toxicity and safety of pesticide residues and diseases caused by human intake were analyzed. The risk assessment and traceability of pesticide residues in tea were carried out, and potential eco-friendly improvement strategies were proposed. The review is expected to provide a valuable reference for reducing risks of pesticide residues in tea and ensuring the safety of tea consumption.

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.

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.

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms

Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.

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.

Biosensing Chlorpyrifos in Environmental Water Samples by a Newly Developed Carbon Nanoparticle-Based Indirect Lateral Flow Assay

Pesticides are used in agriculture to prevent pests. Chlorpyrifos (CHLP) is an insecticide with potentially detrimental effects on humans, bees, and the aquatic environment. Its effects have led to a total ban by the European Union (EU), but outside the EU, CHLP is still produced and used. An indirect lateral flow immunoassay (LFIA) for the detection of CHLP was developed and integrated into a cassette to create a lateral flow device (LFD). Species-specific reporter antibodies were coupled to carbon nanoparticles to create a detector conjugate. Water samples were mixed with a specific CHLP monoclonal antibody and detector conjugate and applied to the LFD. Dose-response curves elicited the detection of low concentrations of CHLP (<1 µg/L). This sensitivity was recorded through a rapid handheld digital imaging device but also visually by naked eye. The CHLP LFD was applied to a range of European surface water samples, fortified with CHLP, revealing a sensitivity in these matrices of 2 µg/L, both by digital and visual analysis. To improve the simplicity of the CHLP LFIA, the assay reagents were dried in tubes, enabling to carry out the test by simply adding water samples and inserting the LFIA strips. This CHLP LFIA is thus suited for the on-site screening of surface waters.

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.

Robotic written silver ink on photographic paper for detection of thiram residues in fruits

With the widespread application of pesticide in agriculture, pesticide residues in food have posed serious health risks to human. There is an urgent requirement to detect pesticide residues in food. In this work, a sensitive and effective method was employed to measure thiram residues in fruit using surface-enhanced Raman scattering (SERS) technique. Silver ink was written on photographic paper (AgNPs-photographic paper) directly by robotic writing technique. The AgNPs-photographic paper substrates possessed good SERS activities and high stability among four months. A good linear response between the peaks intensities and the logarithmic concentrations of thiram was obtained with the limit of detection (LOD) of 0.024 ppb. The substrates also exhibited excellent reproducibility with relative standard deviation (RSD) value less than 10% from ten different substrates. SERS mapping was tested to characterize the uniformity of AgNPs-photographic paper, and the RSD value was calculated to be 14.34% at 1377 cm^-1 measured by 120 points. The LOD values of apple and peach juice adulterated with thiram were 0.0024 and 0.024 ppm, respectively. The LOD values of thiram residues on apple and peach peels were both 0.25 ng/cm². It was demonstrated that the substrates prepared by robotic writing technique had great potential for practical application in food safety inspection.

Rapid detection of sulfonamide antibiotics residues in swine urine by surface-enhanced Raman spectroscopy

Surface enhanced Raman spectroscopy (SERS) combined with rapid pretreatment technique was used to determine sulfonamide antibiotics (sulfadiazine and sulfathiazole) residue in swine urine. Au nanoparticles (AuNPs) were synthesized as Raman enhance substrate and the extraction of swine urine was purified with primary secondary amine (PSA), octadecyl silane (C₁₈) and graphitized carbon (GCB) to eliminate the interference of the matrix and different dosages of adsorbents (PSA, C₁₈, GCB) were investigated. The results showed that the treatment with C₁₈ of 150 mg, GCB of 200 mg and PSA of 200 mg were an excellent approach for rapidly detecting sulfonamide antibiotics residue in swine urine. Combined with density functional theory calculation (DFT), Raman characteristic peaks of 819, 1102, 1173, 1588 cm^-1 and 825, 1127 cm^-1 were selected for qualitative and quantitative assessment of sulfadiazine and sulfathiazole in swine urine, respectively. Based on raman characteristic peak of 819 cm^-1, a good linear relationship between sulfadiazine concentration and Raman intensity was developed with R² = 0.9912, and based on raman characteristic peak of 825 cm^-1, a good linear relationship between sulfathiazole concentration and Raman intensity was developed with R² = 0.9941. And recoveries for five unknown concentration samples predicted were 98.47 ∼ 105.18% with relative standard deviation (RSD) of 1.53% ∼ 5.18%. This study demonstrated that SERS coupled with a quick, easy, cheap, effective, rugged, and safe (QuEChERS) method could be employed to rapidly examine the sulfonamide antibiotics residue in swine urine towards its quality and safety monitoring.

Nontarget and high-throughput screening of pesticides and metabolites residues in tea using ultra-high-performance liquid chromatography and quadrupole-orbitrap high-resolution mass spectrometry

A Sin-QuEChERS, coupled to UHPLC Q-Exactive Orbitrap MS, was used for nontargeted high-throughput rapid screening and quantitative analysis of residual pesticides and metabolites in green teas. The sample was extracted with 0.1% formic acid in acetonitrile with shaking, salted out and centrifuged, and purified with Sin-QuEChERS Nano solid phase extraction column; with Full MS/ddMS2 as the data collection mode, the database containing 384 pesticides combined with Trace Finder 3.0 software, In the absence of standard products, rapid screening and confirmation of potential pesticide residues in tea samples with accurate mass, isotope abundance ratio, secondary fragment ions, etc. 20 pesticides were used as quality controls to verify the screening method, and the linearity of these pesticides was between 1 and 200 μg/L, and the correlation coefficients were all greater than 0.9922. Moreover, the LOQ was between 0.002 and 0.01 mg/kg. The average recoveries of spiked tea samples were 74%-111%. Efficiency and reliability of this method were investigated by the analysis of 38 Chinese green tea samples. 18 potential residual pesticides were detected by non-targeted screening. The researchers then conducted a quantitative analysis of the 18 potential residual pesticides.