Classification and Quantification of Microplastics (<100 µm) Using a Focal Plane Array-Fourier Transform Infrared Imaging System and Machine Learning.

Microplastics are defined as microscopic plastic particles in the range from few micrometers and up to 5 mm. These small particles are classified as primary microplastics when they are manufactured in this size range, whereas secondary microplastics arise from the fragmentation of larger objects. Microplastics are widespread emerging pollutants, and investigations are underway to determine potential harmfulness to biota and human health. However, progress is hindered by the lack of suitable analytical methods for rapid, routine, and unbiased measurements. This work aims to develop an automated analytical method for the characterization of small microplastics (<100 µm) using micro-Fourier transform infrared (µ-FTIR) hyperspectral imaging and machine learning tools. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) models were evaluated, applying different data preprocessing strategies for classification of nine of the most common polymers produced worldwide. The hyperspectral images were also analyzed to quantify particle abundance and size automatically. PLS-DA presented a better analytical performance in comparison with SIMCA models with higher sensitivity, sensibility, and lower misclassification error. PLS-DA was less sensitive to edge effects on spectra and poorly focused regions of particles. The approach was tested on a seabed sediment sample (Roskilde Fjord, Denmark) to demonstrate the method efficiency. The proposed method offers an efficient automated approach for microplastic polymer characterization, abundance numeration, and size distribution with substantial benefits for method standardization.

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

Microplastic research is an emerging field. Consistent accurate identification of microplastic polymer composition is vital for understanding the effect of microplastic pollution in the environment. Fourier transform infrared (FT-IR) spectroscopy is becoming commonplace for identifying microplastics. Conventional spectral identification is based on library searching, a process that utilizes a search algorithm against digital databases containing single spectra of pristine reference plastics. Several conditions on environmental microplastic particles such as weathering, additives, and residues cause spectral alterations relative to pristine reference library spectra. Thus, library searching is vulnerable to misidentification of microplastic samples. While a classification process (classifier) based on a collection of spectra can alleviate misidentification problems, optimization of each classifier (tuning parameter) is required. Additionally, erratic results relative to the particular optimized tuning parameter can occur when microplastic samples originate from new environmental or biological conditions than those defining the class. Presented in this study is a process that utilizes spectroscopic measurements in a hybrid fusion algorithm that depending on the user preference, simultaneously combines high-level fusion with low- and mid-level fusion based on an ensemble of non-optimized classifiers to assign microplastic samples into specific plastic categories (classes). The approach is demonstrated with 17 classifiers using FT-IR for binary classification of polyethylene terephthalate (PET) and high-density polyethylene (HDPE) microplastic samples from environmental sources. Other microplastic types are evaluated for non-class PET and HDPE membership. Results show that high accuracy, sensitivity, and specificity are obtained thereby reducing the risk of misidentifying microplastics.

Microplastics in Sediments of River Yongfeng from Maanshan City, Anhui Province, China.

This study was performed to evaluate the microplastics (MPs) pollution of sediments from River Yongfeng. It was observed that the MPs in sediments were present with contents of 0.5-16.75 mg/kg and abundances of 5-72 items/kg, coupled by variation coefficients of 109% and 91%, respectively, indicating that the distribution of MPs had high spatial variation. The land-based source of business zone is thought to be the significant contributor to accumulation of MPs in those sites with high quantity of MPs. Film was the predominant shape of MPs in river sediments followed by the line and fragment ones. Meanwhile, the MPs detected were mainly composed by four types including Polypropylene (24%), Polyethylene (61%), Polyethylene Terephthalate (8%) and Polystyrene (7%) based on number fraction, respectively, which indicates that Polypropylene and Polyethylene were the major types of MPs in the sediments. Size fraction performance suggests that those MPs < 1000 µm were of ubiquitous presence. The weathering of fringes was universally observed regardless of varying surface among MPs. Despite digestion with oxidative acid solution and subsequent washing by distilled water unexpected elements can still be detected, which should be considered as determining the materials associated.