Evaluating chemometric strategies and machine learning approaches for a miniaturized near-infrared spectrometer in plastic waste classification

Optimizing the sorting of plastic waste plays a crucial role in improving the recycling process. In this contribution, we report on a comparative study of multiple machine learning and chemometric approaches to categorize a data set derived from the analysis of plastic waste performed with a handheld spectrometer working in the Near-Infrared (NIR) spectral range. Conducting a cost-effective NIR study requires identifying appropriate techniques to improve commodity identification and categorization. Chemometric techniques, such as Principal Component Analysis (PCA) and Partial Least Squares - Discriminant Analysis (PLS - DA), and machine learning techniques such as Support- Vector Machines (SVM), fine tree, bagged tree, and ensemble learning were compared. Various pre-treatments were tested on the collected NIR spectra. In particular, Standard Normal Variate (SNV) and Savitzky-Golay derivatives as signal pre-processing tools were compared with feature selection techniques such as multiple Gaussian Curve Fit based on Radial Basis Functions (RBF). Furthermore, results were combined into a single predictor by using a likelihood-based aggregation formula. Predictive performances of the tested models were compared in terms of classification parameters such as Non-Error Rate (NER) and Sensitivity (Sn) with the analysis of the confusion matrices, giving a broad overview and a rational means for the selection of the approach in the analysis of NIR data for plastic waste sorting.