Determination of microplastics by FTIR spectroscopy based on quaternion parallel feature fusion and support vector machine

Abstract

This study proposed a FTIR spectroscopy combing with quaternion parallel feature extraction methodology for the first time to address the qualitative and quantitative analysis of microplastics. For the international available microplastic FTIR dataset, the original, the first derivative and the second derivative FTIR spectra of polyethylene(PE), PE + fouling and polypropylene(PP) samples were first formed as an integral representation by a pure quaternion matrix. Then quaternion principal component analysis(QPCA) was conducted to obtain quaternion feature vectors and support vector machine(SVM) was followed to establish the qualitative model. Meanwhile, we experimentally mixed four types of microplastics of polyvinyl chloride(PVC), polystyrene (PS), PP and PE into sand to prepare simulated samples and collected their FTIR spectra. The concentrations of microplastics were 0.5%, 1%, 1.5%, 3%, 5%, 10%, respectively. The QPCA based parallel feature extraction combing with SVR was conducted to establish the quantitative model. The results show that QPCA has transcended traditional PCA and serial feature extraction methods. Furthermore, compared with traditional algorithms of PLS, BPNN, RF, and 1D-CNN, QPCA-SVM has achieved the highest values of accuracy, precision, recall and F1-score, which were 0.98571, 1, 0.99412 and 0.99697, respectively. QPCA-SVR has achieved the smallest RMSEP for four kinds of microplastics, which were 0.26361, 0.80558, 0.37099, and 0.16246, respectively. This work demonstrates QPCA fuses more spectral characteristics and reduces the redundancy of the complex vector, which is beneficial to improve the recognition and regression ability of SVM. The “QPCA enabled FTIR spectroscopy technique” in this study provides a new idea to promote the rapid and accurate determination of microplastics.