Rapid Determination of Thiabendazole Pesticides in Rape by Surface Enhanced Raman Spectroscopy

Lin, Lei; Dong, Tao; Nie, Ping; Qu, Fangfang; He, Yong; Chu, Bingquan; Xiao, Shupei

doi:10.3390/s18041082

Cited by 40 publications

(22 citation statements)

References 34 publications

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“…The idea of the de-trending (DT) algorithm is that the spectral absorbance and wavelength are first fitted into a trend line d according to the polynomial; then, the trend line d is subtracted from the original spectra x to achieve the effect of this trend [ 41 ]. First-derivative (1st- Der) [ 42 ] can eliminate interference from other backgrounds and distinguish the overlapping peaks, which improves spectral resolution, sensitivity and the signal to noise ratio of the spectra to a certain extent. In this paper, all data analysis were based on MATLAB R2014a (The Math-Works, Natick, MA, USA), Gaussian.v09 (Gaussian, Inc., Wallingford, CT, USA), and OMNIC v8.2 (Infrared spectrum processing software, Thermo Fisher Scientific, Waltham, MA, USA).…”

Section: Methodsmentioning

confidence: 99%

“…In this paper,

and

represent the coefficient of the determination of the calibration set and the prediction set respectively, while RMSE c and RMSE p represent the root mean square error of the calibration set and the prediction set, respectively. In addition, the RPD was suggested to be at least three for agriculture applications; while 2 < RPD < 3 indicated a model with a good predictive ability; 1.4 < RPD < 2 was an intermediate model needing some improvement; and RPD < 1.4 indicated that the model had a poor predictive ability [ 42 ].…”

Section: Methodsmentioning

confidence: 99%

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Density Functional Theory Analysis of Deltamethrin and Its Determination in Strawberry by Surface Enhanced Raman Spectroscopy

Dong

Lin

et al. 2018

Molecules

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Deltamethrin is widely used in pest prevention and control such as red spiders, aphids, and grubs in strawberry. It is important to accurately monitor whether the deltamethrin residue in strawberry exceeds the standard. In this paper, density functional theory (DFT) was used to theoretically analyze the molecular structure of deltamethrin, gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) were used to enhance the surface enhanced Raman spectroscopy (SERS) detection signal. As a result, the theoretical Raman peaks of deltamethrin calculated by DFT were basically similar to the measured results, and the enhancing effects based on AuNPs was better than that of AgNPs. Moreover, 554, 736, 776, 964, 1000, 1166, 1206, 1593, 1613, and 1735 cm−1 could be determined as deltamethrin characteristic peaks, among which only three Raman peaks (736, 1000, and 1166 cm−1) could be used as the deltamethrin characteristic peaks in strawberry when the detection limit reached 0.1 mg/L. In addition, the 500–1800 cm−1 SERS of deltamethrin were analyzed by the partial least squares (PLS) and backward interval partial least squares (BIPLS). The prediction accuracy of deltamethrin in strawberry (Rp2 = 0.93, RMSEp = 4.66 mg/L, RPD = 3.59) was the highest when the original spectra were pretreated by multiplicative scatter correction (MSC) and then modeled by BIPLS. In conclusion, the deltamethrin in strawberry could be qualitatively analyzed and quantitatively determined by SERS based on AuNPs enhancement, which provides a new detection scheme for deltamethrin residue determination in strawberry.

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Section: Methodsmentioning

confidence: 99%

“…In this paper,

and

Section: Methodsmentioning

confidence: 99%

Density Functional Theory Analysis of Deltamethrin and Its Determination in Strawberry by Surface Enhanced Raman Spectroscopy

Dong

Lin

et al. 2018

Molecules

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“…In this paper, R C 2 and R P 2 represent the coefficient of the determination of the calibration set and the prediction set respectively, while

and

represent the root mean square error of the calibration set and the prediction set respectively. In addition, the RPD was suggested to be at least three for agriculture applications; while 2 < RPD < 3 indicated a model with a good predictive ability; 1.4 < RPD < 2 was an intermediate model needing some improvement; and RPD < 1.4 indicated that the model had a poor predictive ability [ 40 ]. In addition, relative standard deviation (RSD) reflects the degree of discretization between individuals in the reflection group and recovery rate reflects the degree of coincidence between the results of the reaction and the true value.…”

Section: Methodsmentioning

confidence: 99%

Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy

et al. 2018

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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−1 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−1: y = 672.26x + 5748.4, R2 = 0.9948; 1008 cm−1: y = 1155.4x + 8740.2, R2 = 0.9938) and the limit of quantification (LOQ) of these two linear models can reach 1 mg/L. The relative standard deviation (RSD) ranged from 1.36% to 8.02% and the recovery was ranging from 95.90% to 116.65%. In addition, the 300–1700 cm−1 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 (Rp2 = 0.9769, RMSEP = 0.556 mg/L, RPD = 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.

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“…Surface-enhanced Raman spectroscopy (SERS) has the outstanding features of fingerprint characteristics, simple pretreatment, fast spectral measurement, and weak signal interference of water [9,10], which are suitable for detecting pesticide residues in water. Many researchers have used SERS to detect aflatoxins in maize [11], phorate and fenthion pesticides in apple skin [12], and thiabendazole pesticides in rape [13]. Along with SERS, chemometric methods are often used for solving spectral denoising [14,15], feature extraction [16], and modeling [16,17].…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Pesticide Residues in Paddy Water Using Surface-Enhanced Raman Spectroscopy

Weng

Zhu

Dong

et al. 2019

Sensors

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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.

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Rapid Determination of Thiabendazole Pesticides in Rape by Surface Enhanced Raman Spectroscopy

Cited by 40 publications

References 34 publications

Density Functional Theory Analysis of Deltamethrin and Its Determination in Strawberry by Surface Enhanced Raman Spectroscopy

Density Functional Theory Analysis of Deltamethrin and Its Determination in Strawberry by Surface Enhanced Raman Spectroscopy

Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy

Rapid Detection of Pesticide Residues in Paddy Water Using Surface-Enhanced Raman Spectroscopy

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