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Near-infrared imaging for quantitative analysis of active component in counterfeit dimethomorph using partial least squares regression

Yue Huang, Shun-Geng Min, Jin-Li Cao, Sheng-Feng Ye, and Jia Duan

College of Science, China Agricultural University, 100193 Beijing, China

 

E-mail: orange07@126.com

Abstract: Near-infrared (NIR) imaging systems simultaneously record spectral and spatial information. Near-infrared imaging was applied to the identification of (E,Z)-4-(3-(4-chlorophenyl)-3-(3,4-dimethoxyphenyl)acryloyl)morpholine (dimethomorph) in both mixed samples and commercial formulation in this study. The distributions of technical dimethomorph and additive in the heterogeneous counterfeit product were obtained by the relationship imaging (RI) mode. Furthermore, a series of samples which consisted of different contents of uniformly distributed dimethomorph were prepared and three data cubes were generated for each content. The spectra extracted from these images were imported to establish the partial least squares model. The model’s evaluating indicators were: coefficient of determination (R 2) 99.42 %, root mean square error of calibration (RMSEC) 0.02612, root mean square error of cross-validation (RMSECV) 0.01693, RMSECVmean 0.03577, relative standard error of prediction (RSEP) 0.01999, and residual predictive deviation (RPD) 15.14. Relative error of prediction of the commercial formulation was 0.077, indicating the predicted value correlated with the real content. The chemical value reconstruction image of dimethomorph formulation products was calculated by a MATLAB program. NIR microscopy imaging here manifests its potential in identifying the active component in the counterfeit pesticide and quantifying the active component in its scanned image.

Keywords: near-infrared microscopy – micro-image quantitative analysis – partial least squares model – counterfeit dimethomorph

Full paper is available at www.springerlink.com.

DOI: 10.2478/s11696-012-0212-9

 

Chemical Papers 66 (11) 1065–1072 (2012)

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