Citation: WEI Yun, XIA Jing-Jing, XU Wei-Xin, CHEN Yue-Yao, MAO Xin-Ran, XIONG Yan-Mei, MIN Shun-Geng. Quantitative Study on Prohibited Addition of Chlorfenapyr in Bacillus Thuringiensis Formulations by Infrared Spectroscopy[J]. Chinese Journal of Analytical Chemistry, ;2022, 50(3): 482-490. doi: 10.19756/j.issn.0253-3820.210794 shu

Quantitative Study on Prohibited Addition of Chlorfenapyr in Bacillus Thuringiensis Formulations by Infrared Spectroscopy

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

Corresponding author: XIONG Yan-Mei, MIN Shun-Geng,
Received Date: 15 October 2021
Revised Date: 28 December 2021

A method for rapid determination of chlorfenapyr in Bacillus thuringiensis formulations with prohibited additions using attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) combined with partial least squares (PLS) analysis was developed. Three Bacillus thuringiensis formulations from different sources were added with different masses of 97.00% (m/m) chlorfenapyr prodrug to prepare 153 mixed samples with chlorfenapyr concentrations ranging from 0.00% to 5.00%. Three pretreatment methods (Savitzky-Golay smoothing (S-G), standard normal variation (SNV) and multivariate scattering correction (MSC)) and five variable selection algorithms (interval partial least-squares (iPLS), moving window partial least-squares (MWPLS), elimination of uninformative variables (UVE), competitive adaptive reweighted sampling (CARS), and the bootstrapping soft shrinkage (BOSS)) were employed to investigate the effects of different pretreatment methods and variable selection methods on the model results. Among them, the MSC pretreatment method combined with the BOSS algorithm obtained the optimal model results, and with this method, RMSECV=0.0017, R_cv²=0.9859, RMSEP=0.0016, and R_pre²=0.9868. For samples with chlorfenapyr concentration ranging from 0.50% to 5.00%, the average relative error of external test sample prediction was 0.0540, and the variables selected by the BOSS algorithm were mainly concentrated in the absorption region of the characteristic peak of chlorfenapyr. This method not only had excellent modeling effect, but also showed good chemical interpretability, and could be applied to the rapid detection of chlorfenapyr in Bacillus thuringiensis formulations with prohibited additions.
- Infrared spectrum,
- Partial least squares,
- Variable selection,
- Chlorfenapyr,
- Bacillus thuringiensis
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