Interval Combination Iterative Optimization Approach Coupled with SIMPLS (ICIOA-SIMPLS) for Quantitative Analysis of Surface-Enhanced Raman Scattering (SERS) Spectra

Quantitative analysis of surface-enhanced Raman scattering (SERS) spectra has been a critical step in trace level analysis. In this study, a novel variable selection method called interval combination iterative optimization approach coupled with SIMPLS (ICIOA-SIMPLS) was proposed for simultaneously predicting the volume ratios of various pesticides by quantitative analysis of the SERS spectra of the compounds. Four strategies, including interval selection, model population analysis (MPA), weighted bootstrap sampling (WBS) and soft shrinkage were combined in the current designed ICIOA-SIMPLS approach. Firstly, the SERS spectra were split into a series of equal-width spectral intervals. Secondly, WBS, as a random sampling method was applied based on the initial weights of spectral intervals to generate random combinations of spectral intervals, namely sub-datasets. On this basis, multivariate calibration sub-models were developed by applying SIMPLS followed by MPA to statistically analyze the outputs of sub-models and update the weights of spectral intervals. Finally, using an iterative optimization procedure the optimal spectral interval combination with the lowest root mean squares error of cross-validation (RMSECV) was searched in a soft shrinkage manner. For the sake of investigating the performance of ICIOA-SIMPLS, four methods including SIMPLS, VCPA-SIMPLS, VISSA-SIMPLS and ICIOA-SIMPLS were tested on two groups of SERS spectra for comparison. The findings revealed that the best prediction performance was obtained with ICIOA-SIMPLS. Hence, this proposed method offers a robust and effective variable selection strategy for quantitative analysis of spectroscopic datasets.