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    Detection of Pirimiphos-Methyl in Wheat Using Surface-Enhanced Raman Spectroscopy and Chemometric Methods. Weng Shizhuang,Yu Shuan,Dong Ronglu,Zhao Jinling,Liang Dong Molecules (Basel, Switzerland) Pesticide residue detection is a hot issue in the quality and safety of agricultural grains. A novel method for accurate detection of pirimiphos-methyl residues in wheat was developed using surface-enhanced Raman spectroscopy (SERS) and chemometric methods. A simple pretreatment method was conducted to extract pirimiphos-methyl residue from wheat samples, and highly effective gold nanorods were prepared for SERS measurement. Raman peaks assignment was calculated using density functional theory. The Raman signal of pirimiphos-methyl can be detected when the concentrations of residue in wheat extraction solution and contaminated wheat is as low as 0.2 mg/L and 0.25 mg/L, respectively. Quantification of pirimiphos-methyl was performed by applying regression models developed by partial least squares regression, support vector machine regression and random forest with principal component analysis using different preprocessed methods. As for the contaminated wheat samples, the relative deviation between gas chromatography-mass spectrometry value and predicted value is in the range of 0.10%-6.63%, and predicted recovery is 94.12%-106.63%, ranging from 23.93 mg/L to 0.25 mg/L. Results demonstrated that the proposed SERS method is an effective and efficient analytical tool for detecting pirimiphos-methyl in wheat with high accuracy and excellent sensitivity. 10.3390/molecules24091691
    Development of a novel wavelength selection method for the trace determination of chlorpyrifos on Au@Ag NPs substrate coupled surface-enhanced Raman spectroscopy. Zhu Jiaji,Ahmad Waqas,Xu Yi,Liu Shuangshuang,Chen Quansheng,Hassan Md Mehedi,Ouyang Qin The Analyst A novel wavelength selection method, namely interval combination population analysis-minimal redundancy maximal relevance (ICPA-mRMR), was employed for the trace level detection of chlorpyrifos (CPS) coupled surface-enhanced Raman spectroscopy (SERS). Herein, a highly sensitive SERS enhancement substrate, Au@Ag nanoparticles (NPs), was synthesized possessing strong enhancement of Raman signals for CPS quantification (enhancement factor: 2.5 × 106). Compared with other established methods such as partial least squares (PLS), synergy interval partial least squares-genetic algorithm (siPLS-GA) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS), ICPA-mRMR yielded the best results with higher correlation coefficients (Rc = 0.9917, RP = 0.9895), ratios of performance to deviation (RPD = 6.8797), and lower root mean square errors (RMSEC = 0.1998, RMSEP = 0.2271). The proposed method was employed for the determination of trace level CPS in tea samples, and the recovery percentages were in the range 90%-108%. Meanwhile, this method was validated using a standard GC-MS method indicating no significant difference (P > 0.05). The proposed methodology offers a rapid, sensitive and powerful analytical platform for the detection of pesticide residues in food. 10.1039/c8an02086h
    [Detection of Chlorpyrifos on Spinach Based on Surface Enhanced Raman Spectroscopy with Silver Colloids]. Zhai Chen,Xu Tian-feng,Peng Yan-kun,Li Yong-yu Guang pu xue yu guang pu fen xi = Guang pu In this research, the surface enhanced Raman spectroscopy (SERS) technique is used to develop a nondestructive and fast detecting method for the detection of residual chlorpyrifos on spinach. Silver colloids used for SERS spectroscopy is prepared by the reduction of silver nitrate with hydroxylamine hydrochloride at alkaline pH. The prepared silver colloids are dropped onto spinach samples, then the SERS spectra are collected non-destructively with a self-developed Raman system. This method can be made without physical contact to samples, and rapidly completed without time-consuming sample pre-treatments, and suited to the development of real-time on-line detection methods for trace pesticide residues. SERS signals are collected from 20 points on each spinach sample with 450 mW laser power and 2.5 s exposure time. Chlorpyrifos concentrations in 24 samples are determined with gas chromatography after SERS spectra taken. Savitzky-Golay (SG) smoothing filter and effective peak linear fitting method are used to remove the random noise and the fluorescence background for improving the accuracy of SERS results. The SERS signals are collected from different parts of 50 spinach samples with the same concentration of chlorpyrifos but at different fresh degrees. The relative standard deviation (RSD) of chlorpyrifos’ characteristic peak intensities is 13.4%. Although the differences of samples lead to differences in the curves of Raman spectrum, they have little influence on the characteristic peak intensities, which indicates the stability of the proposed detecting method. After the fluorescent background removed, the 20 curves of each sample are averaged. Correlation analysis is done between chlorpyrifos concentration and signal intensity at every Raman shift. Results show that correlation coefficients are higher than 0.85 in the range of 615.5~626.4 cm-1. Signals in this range are used to establish multiple linear regression (MLR) model for the prediction of residual chlorpyrifos. MLR model was developed for chlorpyrifos concentration versus Raman signal intensity at 615.5~626.4 cm-1 for predicting residual chlorpyrifos content in samples, the correlation coefficients of calibration (RC) and validation (RP) are 0.961 and 0.954, which indicate a good linear relationships between them. The minimum detectable threshold for this method is 0.05 mg·kg-1 which is close to the value limited by the national standard of China (0.1 mg·kg-1 for chlorpyrifos in spinach). The proposed practical method is sample, fast, without sample preparation, thus it shows great potential in safety detection of fruits and vegetables.
    Quantitative detection of dithiocarbamate pesticides by surface-enhanced Raman spectroscopy combined with an exhaustive peak-seeking method. Wei Qiaoling,Zhang Liangdong,Song Chunfeng,Yuan Hongfu,Li Xiaoyu Analytical methods : advancing methods and applications Surface-enhanced Raman spectroscopy (SERS) based on nanosilver colloid substrates has great potential for rapid detection of pesticide residues because of its advantages of sensitivity, rapidity, simplicity, low cost, etc. However, its poor repeatability and narrow linear quantitative range limit its practical application. In this paper, a silver colloid SERS analysis method combined with an exhaustive peak-seeking method was introduced for quantitative determination of thiram and ziram. This method can establish a linear quantitative relationship in a wide range by use of an own characteristic peak of analysis as an internal standard (IS) which is found via judging the linear correlation between the intensity ratios of two SERS peaks of analytes and the concentrations. Combined with improving the preparation method of silver colloids, adding suitable activators and optimizing the detection process, a liquid detection system with good repeatability and a wide linear quantitation range was obtained. The relative standard deviation (RSD) of the strongest SERS peak is no more than 8.98%, which is better than the general case of the silver colloid SERS substrate. The ratio of I/I has a good linear relationship with the concentration of thiram solution, and the 1148 cm characteristic peak was utilized as the IS to establish the standard curve equation for the determination of thiram concentration. The equation is I/I = -1.7930 × lg[c (ppm)] + 6.0078 with a linear range of 10 to 10 ppm (4.16 × 10 to 4.16 × 10 mol L) and a limit of detection (LOD) of 10 ppm. The peak of IS for the determination of ziram concentration is at 938 cm, and the equation is I/I = 4.5531 × lg[c (ppm)] + 6.4792 with a linear range of 10 to 10 ppm (3.27 × 10 to 3.27 × 10 mol L) and a LOD of 10 ppm. Thiram or ziram in apple juice was successfully detected by using this liquid detection system. This analysis system effectively solves the problem of poor repeatability and a narrow linear quantification range in SERS analysis based on silver colloid substrates, and the linear quantification range meets the requirements of the national standard (GB-2763-2019). 10.1039/d0ay01953d
    Determination of tricyclazole content in paddy rice by surface enhanced Raman spectroscopy. Tang Huirong,Fang Dongmei,Li Qingqing,Cao Peng,Geng Jinpei,Sui Tao,Wang Xuan,Iqbal Jibran,Du Yiping Journal of food science An ultrasensitive method based on Surface enhanced Raman scattering (SERS) has been developed to determine content of a pesticide which is tricyclazole in paddy rice using sliver colloid as a substrate and pyridine as an internal standard. The peaks at 424 and 1035 cm(-1) in a SERS spectrum were selected as analytic and internal peaks, respectively, and their intensity ratio I(t)/I(p) was used to calculate the regression concentration of tricyclazole. The correlation between I(t)/I(p) and concentration showed significant linear relationship with a correlation coefficient of R(2)= 0.995 in a concentration range of 0.05 to 0.70 mg/L and the tricyclazole solution can be detected to be low as 0.002 mg/L by SERS. The method was applied to determine tricyclazole contents of 3 real rice samples with a standard addition method in order to eliminate interference of matrix. The errors of SERS measurements for the 3 samples were 0.0008 to 0.0246, 0.0013 to 0.0028, and 0.0129 to 0.0304 mg/kg, respectively, compared with the results obtained by high performance liquid chromatography method. This also showed a good reproducibility with low values of relative standard deviation (n= 3) for the 3 samples ranged from 3.63% to 4.64%. 10.1111/j.1750-3841.2012.02665.x
    Detection of thiabendazole applied on citrus fruits and bananas using surface enhanced Raman scattering. Müller Csilla,David Leontin,Chiş Vasile,Pînzaru Simona Cintă Food chemistry Thiabendazole (TBZ) is a chemical fungicide and parasiticide largely used in food industry against mold and blight in vegetables and fruits during transportation and long term deposit. We investigated the possibility to detect and monitor the TBZ from the chemically treated bananas and citrus fruits available on Romanian market, using surface enhanced Raman spectroscopy (SERS) with a compact, portable, mini-Raman spectrometer. To assess the potential of the technique for fast, cheap and sensitive detection, we report the first complete vibrational characterization of the TBZ in a large pH and concentration range in conjunction with the density functional theory (DFT) calculations. From the relative intensity of the specific SERS bands as a function of concentration, we estimated a total amount of TZB as 78 mg/kg in citrus fruits, 13 times higher than the maximum allowed by current regulations, whereas in banana fruit the value was in the allowed limit. 10.1016/j.foodchem.2013.08.136
    Evaluation of surface-enhanced Raman scattering detection using a handheld and a bench-top Raman spectrometer: a comparative study. Zheng Jinkai,Pang Shintaro,Labuza Theodore P,He Lili Talanta Surface enhanced Raman scattering (SERS) detection using a handheld Raman spectrometer and a bench-top Raman spectrometer was systemically evaluated and compared in this study. Silver dendrites were used as the SERS substrate, and two pesticides, maneb and pyrrolidine dithiocarbamate-ammonium salt (PDCA) were used as the analytes. Capacity and performance were evaluated based on spectral resolution, signal variation, quantitative capacity, sensitivity, flexibility and intelligence for SERS detection. The results showed that the handheld Raman spectrometer had better data consistency, more accurate quantification capacity, as well as the capacity of on-site and intelligence for qualitative and semi-quantitative analysis. On the other hand, the bench-top Raman spectrometer showed about 10 times higher sensitivity, as well as flexibility for optimization of the SERS measurements under different parameters such as laser power output, collective time, and objective magnification. The study on the optimization of SERS measurements on a bench-top spectrometer provides a useful guide for designing a handheld Raman spectrometer, specifically for SERS detection. This evaluation can advance the application of a handheld Raman spectrometer for the on-site measurement of trace amounts of pesticides or other chemicals. 10.1016/j.talanta.2014.05.015
    Optimum synthesis of cactus-inspired SERS substrate with high roughness for paraquat detection. Chen Wenwen,Li Chen,Yu Zhi,Song Ying,Zhang Xiubing,Ni Dejiang,Zhang De,Liang Pei Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy 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 M and an excellent reproducibility proved by SERS mapping. Furthermore, it behaved good linear relationship with a correlation coefficient (R) 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. 10.1016/j.saa.2021.120703
    [Study on the Rapid Detection of Triazophos Residues in Flesh of Navel Orange by Using Surface-Enhanced Raman Scattering]. Wang Xiao-bin,Wu Rui-mei,Ling Jing,Liu Mu-hua,Zhang Lu-ling,Lin Lei,Chen Jin-yin Guang pu xue yu guang pu fen xi = Guang pu Surface enhanced Raman spectroscopy (SERS) and quick pre-treatment technology were used to detect triazophos residues in flesh of navel orange. Quantitative analysis model was developed by partial least squares (PLS) algorithm. SERS of different concentration (0.5 to 20 mg x L(-1)) triazophos juice solution with flesh extract as the matrix were collected by laser Raman spectrometer. Three preprocessing methods such as normalization, MSC and SNV were used to optimize Raman signals and PLS models were set up. The results showed that minimum detection concentration for triazophos in navel orange below 0.5 mg L(-1). The model built with normalization pre-processing gave the best result; the values of correlation (R(p)) and Root mean square error of prediction set (RMSEP) were 1.38 and 0.976 6, respectively. The predict recoveries were 95.97%-103.18%, and the absolute values of relative errors were below 5%. T-test (t = -0.018) showed that there was no significant difference between the true values and prediction values. This study demonstrates that this method is accurate and reliable.
    Surface-enhanced Raman spectroscopic analysis of phorate and fenthion pesticide in apple skin using silver nanoparticles. Li Xiaozhou,Zhang Su,Yu Zhuang,Yang Tianyue Applied spectroscopy Traditional pesticide residue detection methods are usually complicated, time consuming, and expensive. Rapid, portable, online, and real-time detection kits are the developing direction of pesticide testing. In this paper, we used a surface-enhanced Raman spectroscopy (SERS) technique to detect the organophosphate pesticide residue of phorate and fenthion in apple skin, for the purpose of finding a fast, simple, and convenient detection method for pesticide detection. The results showed that the characteristic wavenumbers of the two organophosphorus pesticides are more easily identified using SERS. We selected the Raman peaks at 728 cm(-1) of phorate and 1215 cm(-1) of fenthion as the target peaks for quantitative analysis, and utilized internal standards to establish linear regression models for phorate and fenthion. The detection limit was 0.05 mg/L for phorate and 0.4 mg/L for fenthion. This method can be used as a quantitative analytical reference for the detection of phorate and fenthion. 10.1366/13-07080
    [Surface-enhanced Raman spectroscopy analysis of thiabendazole pesticide]. Lin Lei,Wu Rui-mei,Liu Mu-hua,Wang Xiao-bin,Yan Lin-yuan Guang pu xue yu guang pu fen xi = Guang pu Surface-enhanced Raman spectroscopy (SERS) technique was used to analyze the Raman peaks of thiabendazole pesticides in the present paper. Surface enhanced substrates of silver nanoparticle were made based on microwave technology. Raman signals of thiabendazole were collected by laser Micro-Raman spectrometer with 514. 5 and 785 nm excitation wavelengths, respectively. The Raman peaks at different excitation wavelengths were analyzed and compared. The Raman peaks 782 and 1 012 at 785 nm excitation wavelength were stronger, which were C--H out-of-plane vibrations. While 1284, 1450 and 1592 cm(-1) at 514.5 nm excitation wavelength were stronger, which were vng and C==N stretching. The study results showed that the intensity of Raman peak and Raman shift at different excitation wavelengths were different And strong Raman signals were observed at 782, 1012, 1284, 1450 and 1592 cm(-1) at 514.5 and 785 nm excitation wavelengths. These characteristic vibrational modes are characteristic Raman peaks of carbendazim pesticide. The results can provide basis for the rapid screening of pesticide residue in agricultural products and food based on Raman spectrum.
    Real-Time and in Situ Monitoring of Pesticide Penetration in Edible Leaves by Surface-Enhanced Raman Scattering Mapping. Yang Tianxi,Zhang Zhiyun,Zhao Bin,Hou Ruyan,Kinchla Amanda,Clark John M,He Lili Analytical chemistry Understanding of the penetration behaviors of pesticides in fresh produce is of great significance for effectively applying pesticides and minimizing pesticide residues in food. There is lack, however, of an effective method that can measure pesticide penetration. Herein, we developed a novel method for real-time and in situ monitoring of pesticide penetration behaviors in spinach leaves based on surface-enhanced Raman scattering (SERS) mapping. Taking advantage of penetrative gold nanoparticles (AuNPs) as probes to enhance the internalized pesticide signals in situ, we have successfully obtained the internal signals from thiabendazole, a systemic pesticide, following its penetration into spinach leaves after removing surface pesticide residues. Comparatively, ferbam, a nonsystemic pesticide, did not show internal signals after removing surface pesticide residues, demonstrating its nonsystemic behavior. In both cases, if the surface pesticides were not removed, copenetration of both AuNPs and pesticides was observed. These results demonstrate a successful application of SERS as an effective method for measuring pesticides penetration in fresh produce in situ. The information obtained could provide useful guidance for effective and safe applications of pesticides on plants. 10.1021/acs.analchem.6b00320
    Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy. Nie Pengcheng,Dong Tao,Xiao Shupei,Lin Lei,He Yong,Qu Fangfang Molecules (Basel, Switzerland) Thiabendazole (TBZ) is widely used in sclerotium blight, downy mildew as well as root rot disease prevention and treatment in plant. The indiscriminate use of TBZ causes the excess pesticide residues in soil, which leads to soil hardening and environmental pollution. Therefore, it is important to accurately monitor whether the TBZ residue in soil exceeds the standard. For this study, density functional theory (DFT) was used to theoretically analyze the molecular structure of TBZ, gold nanoparticles (AuNPs) were used to enhance the detection signal of surface-enhanced Raman spectroscopy (SERS) and the TBZ residue in red soil extracts was quantitatively determined by SERS. As a result, the theoretical Raman peaks of TBZ calculated by DFT were basically consistent with the measured results. Moreover, 784, 1008, 1270, 1328, 1406 and 1576 cm could be determined as the TBZ characteristic peaks in soil and the limits of detection (LOD) could reach 0.1 mg/L. Also, there was a good linear correlation between the intensity of Raman peaks and TBZ concentration in soil (784 cm: = 672.26 + 5748.4, ² = 0.9948; 1008 cm: = 1155.4 + 8740.2, ² = 0.9938) and the limit of quantification (LOQ) of these two linear models can reach 1 mg/L. The relative standard deviation () ranged from 1.36% to 8.02% and the recovery was ranging from 95.90% to 116.65%. In addition, the 300⁻1700 cm SERS of TBZ were analyzed by the partial least squares (PLS) and backward interval partial least squares (biPLS). Also, the prediction accuracy of TBZ in soil (² = 0.9769, = 0.556 mg/L, = 5.97) was the highest when the original spectra were pretreated by standard normal variation (SNV) and then modeled by PLS. In summary, the TBZ in red soil extracts could be quantitatively determined by SERS based on AuNPs, which was beneficial to provide a new, rapid and accurate scheme for the detection of pesticide residues in soil. 10.3390/molecules23081949
    Surface Enhanced Raman Spectroscopy for In-Field Detection of Pesticides: A Test on Dimethoate Residues in Water and on Olive Leaves. Tognaccini Lorenzo,Ricci Marilena,Gellini Cristina,Feis Alessandro,Smulevich Giulietta,Becucci Maurizio Molecules (Basel, Switzerland) Dimethoate (DMT) is an organophosphate insecticide commonly used to protect fruit trees and in particular olive trees. Since it is highly water-soluble, its use on olive trees is considered quite safe, because it flows away in the residual water during the oil extraction process. However, its use is strictly regulated, specially on organic cultures. The organic production chain certification is not trivial, since DMT rapidly degrades to omethoate (OMT) and both disappear in about two months. Therefore, simple, sensitive, cost-effective and accurate methods for the determination of dimethoate, possibly suitable for in-field application, can be of great interest. In this work, a quick screening method, possibly useful for organic cultures certification will be presented. DMT and OMT in water and on olive leaves have been detected by surface enhanced Raman spectroscopy (SERS) using portable instrumentations. On leaves, the SERS signals were measured with a reasonably good S/N ratio, allowing us to detect DMT at a concentration up to two orders of magnitude lower than the one usually recommended for in-field treatments. Moreover, detailed information on the DMT distribution on the leaves has been obtained by Raman line- (or area-) scanning experiments. 10.3390/molecules24020292
    Determination of the Limit of Detection of Multiple Pesticides Utilizing Gold Nanoparticles and Surface-Enhanced Raman Spectroscopy. Dowgiallo A M,Guenther D A Journal of agricultural and food chemistry Exposure to commonly used pesticides poses significant health risks to humans and wildlife. Hence, accurate and sensitive pesticide residue testing methods are imperative to minimize potential health hazards. In this study, we report a method to detect several pesticide residues at trace levels utilizing colloidal gold nanoparticles and surface-enhanced Raman spectroscopy (SERS). Gold nanoparticles suspended in water have been found to enhance Raman scattering from 21 pesticides, including fungicides and insecticides, such as neonicotinoids and organothiophosphates. Measured limits of detection ranged from 0.001 to 10 parts per million (ppm). Furthermore, simultaneous detection of two pesticides, phosmet and thiram, in both a mixture solution and on apple skin, was performed using the SERS method and principal component analysis. The results presented here indicate that SERS coupled with colloidal gold nanoparticles is a potential useful tool for identifying pesticides at trace levels for food safety applications. 10.1021/acs.jafc.9b01544
    Detection of pesticide residue distribution on fruit surfaces using surface-enhanced Raman spectroscopy imaging. RSC advances Surface-enhanced Raman spectroscopy (SERS) is an emerging technique for the detection of pesticide residues on food surfaces, permitting quantitative measurement of pesticide residues without pretreating the sample. However, previous studies have mainly involved the single Raman spectrum of samples, while have given little information on pesticide residue distribution. In this paper, gold nanoparticles were used as surface enhancers to obtain the Raman spectra of omethoate and chlorpyrifos, using the Raman shifts of 413 cm (omethoate) and 346 & 634 cm (chlorpyrifos) as the peaks of interest. Different concentrations of pesticide solution were quantitatively analyzed and the regression curve model was established, whereby the solutions of omethoate and chlorpyrifos were used to study the distribution of pesticide residues on an apple surface by SERS microscopy imaging. Our study shows that this method can achieve rapid and quantitative detection and obtain basic information about the distribution of pesticide residues during pesticide application, which has the potential to be applied to the studies of the diffusion and absorption processes of pesticides in agricultural products. 10.1039/c7ra11927e
    Rapid Multi-Residue Detection Methods for Pesticides and Veterinary Drugs. Jia Min,E Zhongbo,Zhai Fei,Bing Xin Molecules (Basel, Switzerland) The excessive use or abuse of pesticides and veterinary drugs leads to residues in food, which can threaten human health. Therefore, there is an extremely urgent need for multi-analyte analysis techniques for the detection of pesticide and veterinary drug residues, which can be applied as screening techniques for food safety monitoring and detection. Recent developments related to rapid multi-residue detection methods for pesticide and veterinary drug residues are reviewed herein. Methods based on different recognition elements or the inherent characteristics of pesticides and veterinary drugs are described in detail. The preparation and application of three broadly specific recognition elements-antibodies, aptamers, and molecular imprinted polymers-are summarized. Furthermore, enzymatic inhibition-based sensors, near-infrared spectroscopy, and SERS spectroscopy based on the inherent characteristics are also discussed. The aim of this review is to provide a useful reference for the further development of rapid multi-analyte analysis of pesticide and veterinary drug residues. 10.3390/molecules25163590
    Rapid detection of chlorpyrifos pesticide residue in tea using surface-enhanced Raman spectroscopy combined with chemometrics. Zhu Xiaoyu,Li Wenjin,Wu Ruimei,Liu Peng,Hu Xiao,Xu Lulu,Xiong Zhengwu,Wen Yangping,Ai Shirong Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Surface enhanced Raman spectroscopy based on rapid pretreatment combined with Chemometrics was used to determine chlorpyrifos residue in tea. Au nanoparticles were used to as enhance substrate. Different dosages of PSA and NBC were investigated to eliminate the tea substrate influence. Competitive adaptive reweighted sampling (CARS) was used to optimize the characteristic peaks, and compared to full spectra variables and the experiment selected variables. The results showed that PSA of 80 mg and NBC of 20 mg was an excellent approach for rapid detecting. CARS - PLS had better accuracy and stability using only 1.7% of full spectra variables. SVM model achieved better performance with R = 0.981, RMSEP = 1.42 and RPD = 6.78. Recoveries for five unknown concentration samples were 98.47 ~ 105.18% with RSD - 1.53% ~ 5.18%. T-test results showed that t value was 0.720, less than t0.05,4 = 2.776, demonstrating that no clear difference between the real value and predicted value. The detection time of a single sample is completed within 15 min. This study demonstrated that SERS coupled with Chemometrics and QuEChERS may be employed to rapidly examine the chlorpyrifos residue in tea towards its quality and safety monitoring. 10.1016/j.saa.2020.119366
    In situ and rapid determination of acetamiprid residue on cabbage leaf using surface-enhanced Raman scattering. Pan Ting-Tiao,Guo Wang,Lu Ping,Hu Deyu Journal of the science of food and agriculture BACKGROUND:Pesticide residues in agricultural products and foods pose a serious threat to human health, and therefore a simple, rapid and direct method is urgently needed for pesticide residue detection. In addition to realizing the detection of acetamiprid in cabbage extract solution, the main target of this study was to establish an in situ surface-enhanced Raman scattering (SERS) method, which could directly detect acetamiprid residue on cabbage leaf without the need for extraction. Acetamiprid was first used to contaminate the surface of fresh cabbage leaf, and then bimetallic silver-coated gold nanoparticles (Au@AgNPs) were added on the contaminated spots and dried for SERS measurement. RESULTS:Results suggested that acetamiprid can be detected in cabbage extract and on cabbage leaf surface in situ using the SERS method based on the Au@AgNPs substrate. The limit of detection was 0.08 μg mL in cabbage extract and 0.14 mg kg on cabbage leaf, the recovery ranged from 80.5% to 105.5% and the relative standard deviation was in the range 4.37-10.63%. CONCLUSIONS:The proposed SERS method provides an in situ, nondestructive and rapid way to detect acetamiprid residue on the surface of fruits and vegetables, which could serve as an auxiliary approach for early screening of contaminated produce in field or on site in the future. © 2020 Society of Chemical Industry. 10.1002/jsfa.10988
    [Detection of organophosphorus pesticide residue on the surface of apples using SERS]. Li Xiao-Zhou,Yu Zhuang,Yang Tian-Yue,Ding Jian-Hua Guang pu xue yu guang pu fen xi = Guang pu Traditional pesticide residue detection methods are usually complicated, time-consuming, and destructive. Rapid, nondestructive, online real-time is the development direction of the pesticide testing. In the present paper, we use surface enhanced Raman spectroscopy (SERS) technique to detect the organophosphorus pesticide residue of phorate and fenthion on apple to investigate a fast, nondestructive detection method for the pesticide of phorate and tiguron on apples. The results show that the characteristic frequencies of the two organophosphorus pesticides are easier to identify using surface-enhanced Raman spectroscopy. We select Raman signal at 728 cm(-1) for phorate and that at 1 512 cm(-1) for fenthion as target peak for quantitative analysis, and use an internal standard to establish phorate and fenthion linear regression model. This method can be used as a quantitative analysis reference of phorate and fenthion.
    Raman spectrum classification based on transfer learning by a convolutional neural network: Application to pesticide detection. Hu Jiaqi,Zou Yanqiu,Sun Biao,Yu Xinyao,Shang Ziyang,Huang Jie,Jin Shangzhong,Liang Pei Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Pesticide detection is of tremendous importance in agriculture, and Raman spectroscopy/Surface-Enhanced Raman Scattering (SERS) has proven extremely effective as a stand-alone method to detect pesticide residues. Machine learning may be able to automate such detection, but conventional algorithms require a complete database of Raman spectra, which is not feasible. To bypass this problem, the present study describes a transfer learning method that improves the algorithm's accuracy and speed to extract features and classify Raman spectra. The transfer learning model described here was developed through the following steps: (1) the classification model was pre-trained using an open-source Raman spectroscopy database; (2) the feature extraction layer was saved after training; and (3) the training model for the Raman spectroscopy database was re-established while using self-tested pesticides and keeping the feature extraction layer unchanged. Three models were evaluated with or without transfer learning: CNN-1D, Resnet-1D, and Inception-1D, and they have improved the accuracy of spectrum classification by 6%, 2%, and 3%, with reduced training time and increased curve smoothness. These results suggest that transfer learning can improve the feature extraction capability and therefore accuracy of Raman spectroscopy models, expanding the range of Raman-based applications where transfer learning model can be used to identify the spectra of different substances. 10.1016/j.saa.2021.120366
    Gold Nanoparticles with Different Particle Sizes for the Quantitative Determination of Chlorpyrifos Residues in Soil by SERS. He Yong,Xiao Shupei,Dong Tao,Nie Pengcheng International journal of molecular sciences Chlorpyrifos (CPF) is widely used in the prevention and control of crop pests and diseases in agriculture. However, the irrational utilization of pesticides not only causes environmental pollution but also threatens human health. Compared with the conventional techniques for the determination of pesticides in soil, surface-enhanced Raman spectroscopy (SERS) has shown great potential in ultrasensitive and chemical analysis. Therefore, this paper reported a simple method for synthesizing gold nanoparticles (AuNPs) with different sizes used as a SERS substrate for the determination of CPF residues in soil for the first time. The results showed that there was a good linear correlation between the SERS characteristic peak intensity of CPF and particle size of the AuNPs with an R of 0.9973. Moreover, the prepared AuNPs performed great ultrasensitivity, reproducibility and chemical stability, and the limit of detection (LOD) of the CPF was found to be as low as 10 μg/L. Furthermore, the concentrations ranging from 0.01 to 10 mg/L were easily observed by SERS with the prepared AuNPs and the SERS intensity showed a good linear relationship with an R of 0.985. The determination coefficient (Rp) reached 0.977 for CPF prediction using the partial least squares regression (PLSR) model and the LOD of CPF residues in soil was found to be as low as 0.025 mg/kg. The relative standard deviation (RSD) was less than 3.69% and the recovery ranged from 97.5 to 103.3%. In summary, this simple method for AuNPs fabrication with ultrasensitivity and reproducibility confirms that the SERS is highly promising for the determination of soil pesticide residues. 10.3390/ijms20112817
    Unsupported liquid-state platform for SERS-based determination of triazophos. Liu Wen,Huang Yuting,Liu Jing,Chao Shengmao,Wang Dongmei,Gong Zhengjun,Feng Zhe,Fan Meikun Mikrochimica acta A highly reproducible surface-enhanced Raman scattering (SERS) unsupported liquid-state platform (ULP) was developed for accurate quantitative determination of triazophos. Herein, citrate-reduced Ag NPs suspension was concentrated and placed in a stainless steel perforated template to form the SERS ULP. The relative standard deviation of the SERS measurements was less than 5% (n ≥ 10), and the R of the calibration curve was 0.994. The developed SERS ULP was applied for determination of triazophos in spiked agricultural products (rice, cabbage, and apple). Experiment results showed that the coefficient of variation ranged from 5.3 to 6.2% for intra-day and from 5.5 to 6.3% for inter-day (n = 3), which proved excellent SERS reproducibility. Moreover, the results were in good agreement with those from HPLC analysis. As a liquid-state SERS substrate, the highly reproducible ULP can perform precision quantitative analysis without surface modification of NPs, which is a significant improvement. This method provides a new perspective for quantitative SERS analysis of pesticide residues. Graphical abstract. 10.1007/s00604-020-04474-6
    Fast and Low-Cost Surface-Enhanced Raman Scattering (SERS) Method for On-Site Detection of Flumetsulam in Wheat. Han Mingming,Lu Hongmei,Zhang Zhimin Molecules (Basel, Switzerland) The pesticide residues in agri-foods are threatening people's health. This study aims to establish a fast and low-cost surface-enhanced Raman scattering (SERS) method for the on-site detection of flumetsulam in wheat. The two-step modified concentrated gold nanoparticles (AuNPs) acted as the SERS substrate with the aid of NaCl and MgSO. NaCl is served as the activator to modify AuNPs, while MgSO is served as the aggregating agent to form high-density hot spots. The activation and aggregation are two essential collaborative procedures to generate remarkable SERS enhancement and achieve the trace-level detection of flumetsulam. This method exhibits good enhancement effect with an enhancement factor of 10 and wide linear range (5-1000 μg/L). With simple pretreatment, the flumetsulam residue in real wheat samples can be successfully detected with the limit of detection (LOD) down to 0.01 μg/g, which is below the maximum residue limit of flumetsulam in wheat (0.05 μg/g) set in China. The recovery of flumetsulam residue in wheat ranges from 88.3% to 95.6%. These results demonstrate that the proposed SERS method is a powerful technique for the detection of flumetsulam in wheat, which implies the great application potential in the rapid detection of other pesticide residues in various agri-foods. 10.3390/molecules25204662
    Detection of multi-class pesticide residues with surface-enhanced Raman spectroscopy. Mikac L,Kovačević E,Ukić Š,Raić M,Jurkin T,Marić I,Gotić M,Ivanda M Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy The excessive use of pesticides disturbs the natural balance in the environment, creates resistance to pesticides and leads to water and food contamination. Therefore, the implementation of fast, robust and cost effective techniques for the monitoring of pesticides is required. In this work surface-enhanced Raman spectroscopy (SERS) was used for the detection of four common pesticides: atrazine, simazin, irgarol, and diuron. SERS is nowadays considered an effective technique for detection of various analytes in low concentration. Sensitivity of the SERS method depends on the type of substrate that can be either a colloidal solution of metal nanoparticles (NPs) or a metal surface with a suitable nanostructured topology. Here, we have investigated the application of silver nanospheres and silver nanoprisms as SERS substrates in pesticides detection. Colloids with spherical NPs were produced by chemical reduction while Ag nanoprisms were prepared by reducing silver nitrate with borohydride (with citrate as a stabilizing agent) and stirring under a UV lamp for 4 and 10 h. The SERS results have shown that, in the presence of synthesized NPs, it was possible to detect millimolar concentrations of aforementioned pesticides with the exception of diuron. 10.1016/j.saa.2021.119478
    Rapid Detection of Pesticide Residues in Paddy Water Using Surface-Enhanced Raman Spectroscopy. Weng Shizhuang,Zhu Wenxiu,Dong Ronglu,Zheng Ling,Wang Fang Sensors (Basel, Switzerland) Pesticide residue in paddy water is one of the main factors affecting the quality and safety of rice, however, the negative effect of this residue can be effectively prevented and reduced through early detection. This study developed a rapid detection method for fonofos, phosmet, and sulfoxaflor in paddy water through chemometric methods and surface-enhanced Raman spectroscopy (SERS). Residue from paddy water samples was directly used for SERS measurement. The obtained spectra from the SERS can detect 0.5 mg/L fonofos, 0.25 mg/L phosmet, and 1 mg/L sulfoxaflor through the appearance of major characteristic peaks. Then, we used chemometric methods to develop models for the intelligent analysis of pesticides, alongside the SERS spectra. The classification models developed by K-nearest neighbor identified all of the samples, with an accuracy of 100%. For the quantitative analysis, the partial least squares regression models obtained the best predicted performance for fonofos and sulfoxaflor, and the support vector machine model provided optimal results, with a root-mean-square error of validation of 0.207 and a coefficient of determination of validation of 0.99952, for phosmet. Experiments for actual contaminated samples also showed that the above models predicted the pesticide residue values with high accuracy. Overall, using SERS with chemometric methods provided a simple and convenient approach for the detection of pesticide residues in paddy water. 10.3390/s19030506
    Gold Nanoparticles for Qualitative Detection of Deltamethrin and Carbofuran Residues in Soil by Surface Enhanced Raman Scattering (SERS). He Yong,Xiao Shupei,Dong Tao,Nie Pengcheng International journal of molecular sciences The residues of deltamethrin (DM) and carbofuran (CBF) in soil is becoming an intractable problem causing soil hardening and environmental pollution. This paper reports a very simple method via improved reduction of chloroauric acid by the trisodium citrate method for fabricating gold nanoparticles (AuNPs), which were used as a surface enhanced Raman scattering (SERS) active colloids with the advantages of ultrasensitivity, reproducibility and chemical stability. The results demonstrated that the limits of detection (LODs) of the DM and CBF were found to be as low as 0.01 mg/L. The SERS intensity showed a good linear relationship with DM (² = 0.9908) and CBF (² = 0.9801) concentration from 0.01 to 10 mg/L. In a practical application, DM and CBF residues in soil were easily detected by SERS with the flexible AuNPs colloids, and the LODs of DM and CBF were found to be as low as 0.056 mg/kg and 0.053 mg/kg, respectively. Moreover, DM in soil could be qualitatively detected by the characteristic peaks at 560 and 1000 cm, and CBF in soil could be qualitatively detected by the characteristic peaks at 1000 and 1299 cm. The determination coefficient (R²) for DM and CBF reached 0.9176 and 0.8517 in partial least squares (PLS) model. Overall, it is believed that the prepared AuNPs can provide technical support for the accurate detection of pesticide residues in soil by SERS technique. 10.3390/ijms20071731
    Nanostructure-Based Surface-Enhanced Raman Spectroscopy Techniques for Pesticide and Veterinary Drug Residues Screening. Li Mingtao,Zhang Xiang Bulletin of environmental contamination and toxicology Pesticide and veterinary drug residues in food and environment pose a threat to human health, and a rapid, super-sensitive, accurate and cost-effective analysis technique is therefore highly required to overcome the disadvantages of conventional techniques based on mass spectrometry. Recently, the surface-enhanced Raman spectroscopy (SERS) technique emerges as a potential promising analytical tool for rapid, sensitive and selective detections of environmental pollutants, mostly owing to its possible simplified sample pretreatment, gigantic detectable signal amplification and quick target analyte identification via finger-printing SERS spectra. So theoretically the SERS detection technology has inherent advantages over other competitors especially in complex environmental matrices. The progress in nanostructure SERS substrates and portable Raman appliances will promote this novel detection technology to play an important role in future rapid on-site assay. This paper reviews the advances in nanostructure-based SERS substrates, sensors and relevant portable integrated systems for environmental analysis, highlights the potential applications in the detections of synthetic chemicals such as pesticide and veterinary drug residues, and also discusses the challenges of SERS detection technique for actual environmental monitoring in the future. 10.1007/s00128-020-02989-5
    Fabrication of silver-coated gold nanoparticles to simultaneously detect multi-class insecticide residues in peach with SERS technique. Yaseen Tehseen,Pu Hongbin,Sun Da-Wen Talanta Fast sampling and multicomponent detections are important in the analysis of pesticide residues detection. In this work, surface-enhanced Raman scattering (SERS) method based on silver-coated gold nanoparticles (Au@Ag NPs) was used to simultaneously detect multi-class pesticide residues such as thiacloprid (carbamate), profenofos (organophosphate) and oxamyl (neonicotinoid) in standard solution and peach fruit. The Au@Ag NPs with 26 nm Au core size and 6 nm Ag shell thickness exhibited significant Raman enhancement, especially by the creation of hot spots through NPs aggregation induced by the connection between Au@Ag NPs and target molecules. The findings demonstrated that the characteristic wavenumber of the pesticides (thiacloprid, profenofos, and oxamyl) could be precisely identified using the SERS method. Compared with earlier studies, the current approach was rapid, inexpensive and without lengthy sample pretreatment. Moreover, the results revealed that the limit of detection (LOD) was 0.1 mg/kg for thiacloprid obtained in the peach extract with determination coefficient (R) of 0.986. Additionally, LOD for both profenofos and oxamyl was 0.01 mg/kg with a determination coefficient (R) of 0.985 and 0.988, respectively. Good recovery percentage (78.6-162.0%) showed the high SERS activity with better accuracy for the detection of the thiacloprid, profenofos, and oxamyl in peach. The results of this study could offer a promising SERS platform for simultaneous detection of other contaminants such as thiacloprid, profenofos and oxamyl in multifaceted food matrices. 10.1016/j.talanta.2018.12.030
    Rapid Determination of Mixed Pesticide Residues on Apple Surfaces by Surface-Enhanced Raman Spectroscopy. Foods (Basel, Switzerland) 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 concentration, which is far below the pesticide residue standard in China and the EU. The limits of detection reach down to 1.28 × 10 mol L for CPF and 2.47 × 10 mol L for 2,4-D. The limits of quantification are 4.27 × 10 mol L and 8.23 × 10 mol L 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. 10.3390/foods11081089
    [Quantitative Analysis of Dimethoate Pesticide Residues in Honey by Surface-Enhanced Raman Spectroscopy]. Sun Xu-dong,Dong Xiao-ling Guang pu xue yu guang pu fen xi = Guang pu The feasibility of a combination method of surface-enhanced Raman spectroscopy (SERS) technology and linear regression algorithm was investigated for rapid quantitative analysis of pesticide residues in honey. The total of 30 samples was applied in the experiment with dimethoate pesticide residues range from 1 ppm to 10 ppm. The samples were divided into calibration set (20) and prediction set (10). The substrate of Klarite with an inverted pyramidal structure was adopted for improvement of the relative intensity of the majority of Raman shift peaks. The comparative analysis was carried out between SERS spectra of dimethoate pesticide residues in honey samples and conventional Raman spectra of dimethoate standard sample. And four characteristic Raman shift peaks at the wavenumbers of 867, 1 065, 1 317 and 1 453 cm(-1) were found, which were related with the vibrational information of dimethoate molecule. The relationship was developed by linear regression algorithm between the intensity of Raman shift and the concentration of dimethoate pesticide residues. The 10 new samples in the prediction set were applied to evaluate the performance of the models. By comparison, the optimal model was obtained with the characteristic Raman shift peak of 867 cm(-1). The higher correlation coefficient of prediction of 0.984 and lower root mean square error of prediction of 0.663 ppm were obtained. The detection limit of this method was 2 ppm, which was close to the maximum levels of pesticide residue detection limits. Experimental results showed that it was feasible to rapidly analyze quantitative of pesticide residues in honey with the combination method of SERS technology and linear regression algorithm. Compared with the conventional method coupled with the suitable pretreatment, the combination method of SERS technology and linear regression method could analyze the dimethoate pesticide residues in honey, and it also provided an optional method for rapid quantitative analysis pesticide residues in other agricultural products.
    Screening pesticide residues on fruit peels using portable Raman spectrometer combined with adhesive tape sampling. Gong Xinying,Tang Mi,Gong Zhengjun,Qiu Zhongping,Wang Dongmei,Fan Meikun Food chemistry In this work, we report a simple and rapid surface-enhanced Raman scattering (SERS) method for the screening of pesticide residues on fruit peels using a portable Raman spectrometer. Adhesive tapes were used as the sampling media; the effectiveness of different tape brands was examined. Collection efficiencies were found to be 60.2 ± 7.6%, 54.3 ± 5.0%, and 52.3 ± 9.0% on glass, aluminum foil, and fruit peels, respectively. SERS was achieved by applying silver nanoparticles (Ag NPs) to the surface of the tape after analyte collection. Preparation of the Ag NPs was optimized for pesticide detection. The limit of detection of triazophos on apple peels was 25 ng/cm with the portable Raman spectrometer. Considering the least favorable conditions, the calculated detection limit was 0.0225 mg/kg, which is an order of magnitude less than the maximum residue limit (MRL, 0.2 mg/kg) in China. The method is sufficiently sensitive for use in field analysis. 10.1016/j.foodchem.2019.05.127
    Detection of Pesticide Residues in Food Using Surface-Enhanced Raman Spectroscopy: A Review. Xu Meng-Lei,Gao Yu,Han Xiao Xia,Zhao Bing Journal of agricultural and food chemistry Pesticides directly pollute the environment and contaminate foods ultimately being absorbed by the human body. Their residues contain highly toxic substances that have been found to cause serious problems to human health even at very low concentrations. The gold standard method, gas/liquid chromatography combined with mass spectroscopy, has been widely used for the detection of pesticide residues. However, these methods have some drawbacks such as complicated pretreatment and cleanup steps. Recent technological advancements of surface-enhanced Raman spectroscopy (SERS) have promoted the creation of alternative detection techniques. SERS is a useful detection tool with ultrasensitivity and simpler protocols. Present SERS-based pesticide residue detection often uses standard solutions of target analytes in conjunction with theoretical Raman spectra calculated by density functional theory (DFT) and actual Raman spectra detected by SERS. SERS is quite a promising technique for the direct detection of pesticides at trace levels in liquid samples or on the surface of solid samples following simple extraction to increase the concentration of analytes. In this review, we highlight recent studies on SERS-based pesticide detection, including SERS for pesticide standard solution detection and for pesticides in/on food samples. Moreover, in-depth analysis of pesticide chemical structures, structural alteration during food processing, interaction with SERS substrates, and selection of SERS-active substrates is involved. 10.1021/acs.jafc.7b02504
    In Situ Recyclable Surface-Enhanced Raman Scattering-Based Detection of Multicomponent Pesticide Residues on Fruits and Vegetables by the Flower-like MoS@Ag Hybrid Substrate. Chen Ying,Liu Hongmei,Tian Yiran,Du Yuanyuan,Ma Yi,Zeng Shuwen,Gu Chenjie,Jiang Tao,Zhou Jun ACS applied materials & interfaces Pesticides, extensively used in agriculture production, have received enormous attention because of their potential threats to the environment and human health. Hence, in this study, a kind of highly sensitive and stable hybrid surface-enhanced Raman scattering (SERS)-active substrates constructed with flower-like two-dimensional molybdenum sulfide and Ag (MoS@Ag) has been developed, and then the above substrate was sequentially utilized in the recyclable detection of pesticide residues on several kinds of fruits and vegetables. In the first place, the excellent photocatalytic performance of the MoS@Ag hybrid substrate was demonstrated, which was attributed to the inhibition of electron-hole combination after the formation of Schottky barrier between the Ag NPs and MoS matrix. Thereafter, two calibration curves with ultra-low limits of detection (LOD) as 6.4 × 10 and 9.8 × 10 mg/mL were established for the standard solutions of thiram (tetramethylthiuram disulfide, TMTD) and methyl parathion (MP), and then the recyclable assay of their single and mixed residues on eggplant, Chinese cabbage, grape, and strawberry was successfully realized. It is interesting to note that the detection recoveries from 95.5 to 63.1% for TMTD and 92.3 to 62.6% for MP are greatly dependent on the size and surface roughness of these foods. In a word, the MoS@Ag composite matrix shows attractive SERS and photocatalysis performance, and it is expected to have the potential application on food safety monitoring. 10.1021/acsami.9b22725
    Detection of systemic pesticide residues in tea products at trace level based on SERS and verified by GC-MS. Zhang De,Liang Pei,Ye Jiaming,Xia Jing,Zhou Yongfeng,Huang Jie,Ni Dejiang,Tang Lisha,Jin Shangzhong,Yu Zhi Analytical and bioanalytical chemistry Surface-enhanced Raman spectroscopy (SERS) has the potential to detect pesticide residues in agricultural products. However, some systemic pesticides, such as chlorpyrifos, can enter the plant tissue, and not just stay on the surface. Consequently, many SERS studies halted at practical application because of its complexity. In this work, SERS technology was used to detect chlorpyrifos residues in tea products at the semiquantitative level. A simple pretreatment method effectively avoided interference of other fluorescent substances, and all major peaks could be distinguished on the basis of a novel substrate. A principal component analysis algorithm was applied to form a regression model, and a nanogram detection limit was obtained. Furthermore, chlorpyrifos residues in the same tea products were also measured by gas chromatography-mass spectrometry, and the results show a small range of errors. From the comparative study of the two detection methods, the results suggest the great promise of SERS technology for rapid inspection of agricultural products. 10.1007/s00216-019-02103-7
    Rapid on-site identification of pesticide residues in tea by one-dimensional convolutional neural network coupled with surface-enhanced Raman scattering. Zhu Jiaji,Sharma Arumugam Selva,Xu Jing,Xu Yi,Jiao Tianhui,Ouyang Qin,Li Huanhuan,Chen Quansheng Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy In this study, a novel analytical approach is proposed for the identification of pesticide residues in tea by combining surface-enhanced Raman scattering (SERS) with a deep learning method one-dimensional convolutional neural network (1D CNN). First, a handheld Raman spectrometer was used for rapid on-site collection of SERS spectra. Second, the collected SERS spectra were augmented by a data augmentation strategy. Third, based on the augmented SERS spectra, the 1D CNN models were established on the cloud server, and then the trained 1D CNN models were used for subsequent pesticide residue identification analysis. In addition, to investigate the identification performance of the 1D CNN method, four conventional identification methods, including partial least square-discriminant analysis (PLS-DA), k-nearest neighbour (k-NN), support vector machine (SVM) and random forest (RF), were also developed on the basis of the augmented SERS spectra and applied for pesticide residue identification analysis. The comparative studies show that the 1D CNN method possesses better identification accuracy, stability and sensitivity than the other four conventional identification methods. In conclusion, the proposed novel analytical approach that exploits the advantages of SERS and a deep learning method (1D CNN) is a promising method for rapid on-site identification of pesticide residues in tea. 10.1016/j.saa.2020.118994