High-throughput NIR spectroscopic (NIRS) detection of microplastics in soil

Abstract

The increasing pollution of terrestrial and aquatic ecosystems with plastic debris leads to the accumulation of microscopic plastic particles of still unknown amount. To monitor the degree of contamination, analytical methods are urgently needed, which help to quantify microplastics (MP). Currently, time-costly purified materials enriched on filters are investigated both by micro-infrared spectroscopy and/or micro-Raman. Although yielding precise results, these techniques are time consuming, and are restricted to the analysis of a small part of the sample in the order of few micrograms. To overcome these problems, we tested a macroscopic dimensioned near-infrared (NIR) process-spectroscopic method in combination with chemometrics. For calibration, artificial MP/ soil mixtures containing defined ratios of polyethylene, polyethylene terephthalate, polypropylene, and polystyrene with diameters < 125 μm were prepared and measured by a process FT-NIR spectrometer equipped with a fiber-optic reflection probe. The resulting spectra were processed by chemometric models including support vector machine regression (SVR), and partial least squares discriminant analysis (PLS-DA). Validation of models by MP mixtures, MP-free soils, and real-world samples, e.g., fermenter residue, suggests a reliable detection and a possible classification of MP at levels above 0.5 to 1.0 mass% depending on the polymer. The benefit of the combined NIRS chemometric approach lies in the rapid assessment whether soil contains MP, without any chemical pretreatment. The method can be used with larger sample volumes and even allows for an online prediction and thus meets the demand of a high-throughput method.

Application of a Hybrid Fusion Classification Process for Identification of Microplastics Based on Fourier Transform Infrared Spectroscopy

“…42 Classification methods have only recently been applied to identify microplastic particles. [43][44][45][46][47][48][49] Recent work includes distinguishing microplastics from leaves, soils, and rocks evaluating three classifiers. 45 Again, the quality of the results depended on the classifier used.…”

“…Five microplastics were identified in soil using NIR spectroscopy with partial least squares discriminant analysis (PLS-DA). 47 Plastic debris in the marine environment were first identified by an FT-IR library search and those samples with an HQI less than 0.8 were then identified by a Mahalanobis distance (MD) procedure. 49 There are numerous classification methods, 37 and the process of selecting the best performing classifier is time consuming.…”

Genome-wide association analysis uncovers candidate genes for forage quality traits in Brassica napus stems

“…NIR could also be used in the petrochemical industry for the determination of hydrocarbons, alcohols, ketones and nitrile compounds in organic solvents [29]. Paul et al (2018) [30] detected microplastics in the soil using a combined NIR chemometric approach, which met the demands for high-throughput analysis of large sample volumes. In addition, NIR technology has been widely used to measure chemical compounds in plants, such as lignin in roots, and to identify important QTLs [31].…”

Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

“…As a linear classification method, PLS-DA is based on the principle of PLSR which uses projection of variables to maximize the covariance ( Paul et al, 2019 ). In this study, only the average spectra of samples were considered as the X variables, and the Y variables were determined by a code rule such that IR24 was code 1, 3A26 was code 2 and 4A37 was code 3.…”