Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network

Abstract

With the increasing interest in microplastics (MPs) pollutants, quantitative analysis of MPs in water environment is an important issue. Vibrational spectroscopy, represented by Raman spectroscopy, is widely used in MP detection because they can provide unique fingerprint characteristics of chemical components of MPs, but it is difficult to provide quantitative information. In this paper, an ingenious method for quantitative analysis of MPs in water environment by combining Raman spectroscopy and convolutional neural network (CNN) is proposed. It is innovatively proposed to collect the average mapping spectra (AMS) of the samples to improve the uniformity of Raman spectroscopy detection, and to increase the effective detection range of concentration by filtering different volumes of the same MP solutions. In order to verify the universality and effectiveness of the proposed method, 6 different sizes of Polyethylene (PE) MPs were used as detection objects and mixed into 5 different actual water environments. The R2 and RMSE of CNN for identifying the concentration of PE solutions could reach 0.9972 and 0.033, respectively. Meanwhile, by comparing machine learning models such as Random Forest (RF) and Support Vector Machine (SVM) were compared, and CNN combined with Raman spectroscopy has significant advantages in identifying the concentration of MPs.