Robust Automatic Identification of Microplastics in Environmental Samples Using FTIR Microscopy

Abstract

The analysis of microplastics is mainly performed using Fourier transformation infrared spectroscopy/microscopy (FTIR/ μFTIR). However, in contrast to most aspects of the analysis process, for example, sampling, sample preparation, and measurement, there is less known about data evaluation. This particularly critical step becomes more and more important if a large number of samples has to be handled. In this context, it is concerning that the commonly used library searching is not suitable to identify microplastics from real environmental samples automatically. Therefore, many spectra have to be rechecked by the operator manually, which is very time-consuming. In this study, a new fully automated robust microplastics identification method is presented that assigns over 98% of microplastics correctly. The main concept of this new method is to detect and numerically describe the individual vibrational bands within an FTIR absorbance spectrum by curve fitting, which leads to a very compact and highly characteristic peak list. This list allows very accurate and robust library searching. The developed approach is based on the already published microplastics identification algorithm (μIDENT) and extends and improves the field of application to μFTIR data with a special focus on relevant broad, overlapped, or complex vibrational bands.

Contributions of Fourier transform infrared spectroscopy in microplastic pollution research: A review

“…Moreover, training the classifier can increase the analysis speed substantially when dealing with large datasets of FTIR spectra. For example, automated identification methods were tested based on hierarchical cluster analysis (Primpke et al, 2018), shortwave infrared imaging (Schmidt et al, 2018), identification of the most relevant bands (Renner et al, 2017;Renner, Nellessen, et al, 2019), random decision forest method (Hufnagl et al, 2019), modified chemometric identification concept (Renner, Sauerbier, et al, 2019), machine learning method (Kedzierski et al, 2019), Python based lFTIR mapping (Renner et al, 2020) and Hybrid fusion method (Chabuka & Kalivas, 2020). The analysis of FTIR spectra is time-consuming as often it is needed to compare the spectra one by one with the reference spectra.…”

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

“…5,22 This procedure still requires manual decisions or in a recent algorithm for automatic spectral band identification, user operator decisions are still needed for setting thresholds on a few adjustable parameters. 31…”

“…5,22 This procedure still requires manual decisions or in a recent algorithm for automatic spectral band identification, user operator decisions are still needed for setting thresholds on a few adjustable parameters. 31 In an attempt to avoid misidentification by library searching, a Raman library of pristine plastics was augmented with additional polymer samples such as cellulose, fibers, films, and colored plastics. 3 While this approach improved the matching potential, the reference database was still limited and it would need to be expanded to include plastic spectra at various degrees of degradation as well as sample specific additives and possible contaminants.…”

Comparison of pyrolysis gas chromatography/mass spectrometry and hyperspectral FTIR imaging spectroscopy for the analysis of microplastics

“…Especially for FTIR, hyperspectral chemical imaging can be performed fast using focal plane array detectors [26,27]. Currently, several approaches are available to analyze the obtained data, using spectral correlation [23,28], selective band separation and analysis [29,30], machine learning [31], or classifiers [32]. One of the most applied or amended approaches [3] is currently based on the automated analysis pipeline (AAP) [23] using vector-normalized spectra and a specialized database [33].…”