ALBERT

Description: Highlights • Hyperspectral technology was investigated to detect microplastic in soil rapidly. • Two color PE microplastics with particle size from 0.5 to 1 mm were studied. • Support vector machine showed the potential to monitor PE microplastics in soil. • Six kinds of household polymers were applied to validate the developed method. Abstract Hyperspectral imaging technology has been investigated as a possible way to detect microplastics contamination in soil directly and efficiently in this study. Hyperspectral images with wavelength range between 400 and 1000 nm were obtained from soil samples containing different materials including microplastics, fresh leaves, wilted leaves, rocks and dry branches. Supervised classification algorithms such as support vector machine (SVM), mahalanobis distance (MD) and maximum likelihood (ML) algorithms were used to identify microplastics from the other materials in hyperspectral images. To investigate the effect of particle size and color, white polyethylene (PE) and black PE particles extracted from soil with two different particle size ranges (1–5 mm and 0.5–1 mm) were studied in this work. The results showed that SVM was the most applicable method for detecting white PE in soil, with the precision of 84% and 77% for PE particles in size ranges of 1–5 mm and 0.5–1 mm respectively. The precision of black PE detection achieved by SVM were 58% and 76% for particles of 1–5 mm and 0.5–1 mm respectively. Six kinds of household polymers including drink bottle, bottle cap, rubber, packing bag, clothes hanger and plastic clip were used to validate the developed method, and the classification precision of polymers were obtained from 79% to 100% and 86%–99% for microplastics particle 1–5 mm and 0.5–1 mm respectively. The results indicate that hyperspectral imaging technology is a potential technique to determine and visualize the microplastics with particle size from 0.5 to 5 mm on soil surface directly.

Description: Microplastics (MPs) in aquatic organisms are raising increasing concerns regarding their potential damage to ecosystems. To date, Raman and Fourier transform infrared spectroscopy techniques have been widely used for detection of MPs in aquatic organisms, which requires complex protocols of tissue digestion and MP separation and are time- and reagentconsuming. This novel approach directly separates, identifies, and characterizes MPs from the hyperspectral image (HSI) of the intestinal tract content in combination with a support vector machine classification model, instead of using the real digestion/separation protocols. The procedures of HSI acquisition ( 1 min) and data analysis (5 min) can be completed within 6 min plus the sample preparation and drying time (30 min) where necessary. This method achieved a promising efficiency (recall 〉98.80%, precision 〉96.22%) for identifying five types of MPs (particles 〉0.2 mm). Moreover, the method was also demonstrated to be effective on field fish from three marine fish species, revealing satisfying detection accuracy (particles 〉0.2 mm) comparable to Raman analysis. The present technique omits the digestion protocol (reagent free), thereby significantly reducing reagent consumption, saving time, and providing a rapid and efficient method for MP analysis.