ABSTRACT
Given the growing urge for plastic management and regulation in the world, recent studies have investigated the problem of plastic material identification for correct classification and disposal. Recent works have shown the potential of machine learning techniques for successful microplastics classification using Raman signals. Classification techniques from the machine learning area allow the identification of the type of microplastic from optical signals based on Raman spectroscopy. In this paper, we investigate the impact of high-frequency noise on the performance of related classification tasks. It is well-known that classification based on Raman is highly dependent on peak visibility, but it is also known that signal smoothing is a common step in the pre-processing of the measured signals. This raises a potential trade-off between high-frequency noise and peak preservation that depends on user-defined parameters. The results obtained in this work suggest that a linear discriminant analysis model cannot generalize properly in the presence of noisy signals, whereas an error-correcting output codes model is better suited to account for inherent noise. Moreover, principal components analysis (PCA) can become a must-do step for robust classification models, given its simplicity and natural smoothing capabilities. Our study on the high-frequency noise, the possible trade-off between pre-processing the high-frequency noise and the peak visibility, and the use of PCA as a noise reduction technique in addition to its dimensionality reduction functionality are the fundamental aspects of this work.
ABSTRACT
The combination of different polymers in the form of blended plastics has been used in the plastic industry for a long time. Nevertheless, analyses of microplastics (MPs) have been mainly limited to the study of particles made of single-type polymers. Accordingly, two members of the Polyolefins (POs) family, i.e., Polypropylene (PP) and Low-density Polyethylene (LDPE) are blended and extensively studied in this work due to their applications in industry as well as abundance in the environment. It is shown that 2-D Raman mapping only provides information about the surface of blended MPs (B-MPs). While complimentary 3-D volume analysis is needed to fully understand the presence of various polymers in such complex samples. Therefore, 3-D Raman mapping is applied to visualize the morphology of the distribution of polymers within the B-MPs together with the quantitative estimation of their concentrations. A parameter defined as the concentration estimate error (CEE) evaluates the precision of the quantitative analysis. Furthermore, the impact of four excitation wavelengths 405, 532, 633, and 785 nm is investigated on the obtained results. Finally, the application of a line-shaped laser beam profile (line-focus) is introduced for reducing the measurement time from 56 to 2 h.
