Raman Spectroscopy Combined with Partial Least Squares for Quantitative Analysis of Two Kinds of Microplastics in Water Samples

Microplastics (MPs) are emerging contaminants in aquatic environments characterized by their polar structure, small particle size (Typically less than 5 mm), large surface area, good stability, and resistance to biodegradation. They pose adverse effects on the normal physiological activities of aquatic organisms and can accumulate in biota, including humans. Therefore, there is an urgent need for rapid and accurate quantitative analysis of MPs in water environments. In this study, Raman spectroscopy combined with partial least squares (PLS) was employed for rapid and accurate quantitative analysis of polyethylene (PE) and polystyrene (PS) MPs in real water samples. Initially, 33 simulated water samples containing different concentrations of MPs were prepared, and their Raman spectra were collected. Six spectral preprocessing methods (Normalization, multiplicative scatter correction, standard normal variate transformation, first derivative, second derivative, and wavelet transform) were investigated for their impact on the predictive performance of PLS calibration models. Subsequently, three variable selection methods including synergy interval partial least squares (SiPLS), variable importance in projection (VIP) and mutual information (MI) were employed to optimize the input variables of the PLS calibration model. The predictive capability of the PLS calibration model was evaluated and validated using leave-one-out cross- validation. Under the optimal conditions of spectral preprocessing, variable selection, input variables and latent variables, the wavelet transform-partial least squares (WT-PLS) calibration model based on distilled water was established, and the contents of PE and PS in real water samples were predicted with prediction correlation coefficients (R2p) of 0.9540 and 0.8472 for PE and PS, respectively, and prediction errors (Errorp) of 0.0690 and 0.1126, respectively. Furthermore, a mixed sample MI-PLS calibration model was developed, demonstrating the best predictive performance in real water samples (With R 2 p values of 0.9776 and 0.9755 for PE and PS, respectively, and Errorp values of 0.0360 and 0.0392, respectively). This method provided a novel approach and new methodology for quantitative analysis of MPs and other organic pollutants in real water samples.