Journal
IEEE ACCESS
Volume 9, Issue -, Pages 47615-47620Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3063293
Keywords
Plastics; Distortion; Libraries; Machine learning; Analytical models; Calibration; Software; Microplastics; automatic identification; spectroscopic; robust classifier; k-nearest neighbor
Ask authors/readers for more resources
A robust classifier was proposed to differentiate the chemical types of environmental microplastics samples, achieving a recognition rate higher than 0.97. The method effectively eliminated interference from spectral distortions and diversity, enhancing the ability to interpret the spectra of realistic environmental microplastics samples.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available