Quantitative analysis of thiram based on SERS and PLSR combined with wavenumber selection

Abstract

Surfaced-enhanced Raman spectroscopy (SERS) is a novel analytical technology mainly for quick, simple and ultrasensitive analysis. In this study, SERS and partial least squares regression (PLSR) were used for the quantitative analysis of thiram combined with a wavenumber selection method based on the adaptive genetic algorithm (AGA). The conventional genetic algorithm (CGA) was also used for wavenumber selection and the effects of the two were contrasted. Moreover, the impact on individual analytical results was evaluated of different numbers of subintervals included in the wavenumber selection. We achieved the best results (root mean square error of cross-validation = 0.2957 μM, R = 0.9995) by PLSR using an AGA with 5-subintervals. The experiments indicated that the proposed AGA-based wavenumber selection was superior to the CGA and the effect of multi-subintervals on wavenumber selection was better than that of a single subinterval.