Spectroscopic Identification of Environmental Microplastics
Xi Chen, Jiancheng Zhou, Leiming Yuan, Guangzao Huang, Xiaojing Chen, Wen Shi
Abstract
Spectroscopic technology is widely used in identifying the categories of microplastics (MPs) for its non-destructive, rapid, and without pretreatment characters. Recognition of spectral category is often conducted by matching with spectral reference library, this works well with a known material library, but fails to blindly identify the unknown source of the environmental MPs. In this work, a robust classifier was proposed to differentiate the chemical types of environmental MPs samples, and the recognition rate was higher than 0.97. This robust classifier innovatively proposed an adaptive estimator in the developed k-nearest neighbor (kNN) model as the hard threshold to classify the environmental MPs, and thus the interference of spectral distortions and diversity was effectively eliminated. This method increases the ability to interpret the spectra of realistic environmental MPs samples.