Quantitative Determination of Chlormequat Chloride Residue in Wheat Using Surface-Enhanced Raman Spectroscopy
Количественное определение остаточных количеств хлормеквата хлорида в пшенице методом поверхностно-усиленной рамановской спектроскопии
2018-07-10
SCID: 54.1/vvd8rfua
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chlormequat chloride residuegold nanorodssupport vector regressionsurface-enhanced Raman spectroscopywheat
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Abstract (AI)
A simple and sensitive method for detection of chlormequat chloride residue in wheat was developed using surface-enhanced Raman spectroscopy (SERS) coupled with chemometric methods on a portable Raman spectrometer. Pretreatment of wheat samples was performed using a two-step extraction procedure. Effective and uniform active substrate (gold nanorods) was prepared and mixed with the sample extraction solution for SERS measurement. The limit of detection for chlormequat chloride in wheat extracting solutions and wheat samples was 0.25 mg/L and 0.25 μg/g, which was far below the maximum residual value in wheat of China. Then, support vector regression (SVR) and kernel principal component analysis (KPCA), multiple linear regression, and partial least squares regression were employed to develop the regression models for quantitative analysis of chlormequat chloride residue with spectra around the characteristic peaks at 666, 713, and 853 cm−1. As for the residue in wheat, the predicted recovery of established optimal model was in the range of 94.7% to 104.6%, and the standard deviation was about 0.007 mg/L to 0.066 mg/L. The results demonstrated that SERS, SVR, and KPCA can provide the accurate and quantitative determination for chlormequat chloride residue in wheat.
Key Findings
1
A portable Raman spectrometer combined with SERS and chemometric methods was developed for quantitative chlormequat chloride detection in wheat.
2
A two-step extraction procedure and gold nanorod substrate enabled sensitive and uniform SERS measurements of wheat extracts.
3
Regression models used characteristic Raman peaks at 666, 713, and 853 cm−1; the optimal model achieved 94.7%–104.6% predicted recovery.
4
The combined SERS, support vector regression, and kernel principal component analysis approach produced standard deviations of approximately 0.007–0.066 mg/L.
5
The detection limit was 0.25 mg/L in wheat extracts and 0.25 μg/g in wheat samples, below China’s maximum residual limit.
Research Object
Chlormequat chloride residue in wheat
Research Subject
Quantitative determination of the residue concentration using SERS measurements and chemometric regression, including detection sensitivity and prediction accuracy
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2018-07-10
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