Robust Automatic Identification of Microplastics in Environmental Samples Using FTIR Microscopy

• Published in: Analytical chemistry (Washington) Vol. 91; no. 15; pp. 9656 - 9664
• Main Authors: Renner, Gerrit, Sauerbier, Philipp, Schmidt, Torsten C, Schram, Jürgen
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 06.08.2019
• Subjects: Algorithms; Analytical chemistry; Automation; Chemistry; Curve fitting; Fourier transforms; Infrared analysis; Infrared spectroscopy; Microplastics; Microscopy; Robustness (mathematics); Sample preparation; Searching; Spectrum analysis
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.9b01095

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.