Abstract
Fourier-transform near-Infrared (FT-NIR) technology offers a promising alternative to traditional methods for detecting soil Chromium (Cr) contamination. However, the relationship between soil Cr content and the spectra may involve complex non-linear dynamics and data redundancy. Therefore, selecting spectral feature variables and constructing parametric scaling models for rapid estimation has become a focal point in current research. In this study, the parametric scaling support vector machine (PSSVM) method is proposed for optimizing the modeling parameters, the binary modified differential evolution (BDE) algorithm is designed for selecting the feature variables. In combination, a novel combined optimization system is established by embedding the PSSVM model into the BDE iterative process. The system (BDE-PSSVM) is validated by estimating the soil Cr content based on the FT-NIR spectral data. The soil samples are collected from the area around a centralized waste treatment base, serving as the research subject. The original spectral data underwent preprocessing using Savitzky-Golay smoothing. Subsequently, the samples were divided into the training and testing sets by the SPXY algorithm, where the testing samples are strictly excluded from the model training process. Feature selection and the parametric scaling model optimization are simultaneously performed by applying the BDE-PSSVM model. The most optimal model observes the minimal root mean square error of 8.114, which only carries 56 discrete variables. In comparison to some other counterpart modeling methods, the BDE-PSSVM uses less feature variables and yields the better prediction results. This finding indicates that the proposed BDE-PSSVM modeling system provides an efficient way for rapid estimation of soil Cr content in cooperation with the FT-NIR technology. The proposed system is expected to undergo testing for its application in detecting additional analytes.