Identification of marine microplastics by a combined method of principal component analysis and random forest for fluorescence spectrum processing.

The severely overlapped laser-induced fluorescence spectra between different microplastics pose significant challenges on fluorescence-based particle identification and quantification. To address this problem, this paper proposes a combined method of principal component analysis (PCA) and random forest (RF) for fluorescence spectrum processing. The key idea is to identify the overlapped PCA scores of the first three principal components of fluorescence spectra by the random forest method.