Robust Automatic Identification of Microplastics in Environmental Samples Using FTIR Microscopy.

Area of Science:

• Environmental Science
• Analytical Chemistry
• Spectroscopy

Background:

• Microplastic analysis commonly uses Fourier Transform Infrared spectroscopy/microscopy (FTIR/μFTIR).
• Data evaluation in microplastic analysis is less understood than other steps like sampling or measurement.
• Current library searching methods are inadequate for automatic identification of microplastics in environmental samples, requiring time-consuming manual checks.

Purpose of the Study:

• To develop a fully automated and robust method for microplastic identification from FTIR/μFTIR data.
• To improve the accuracy and efficiency of microplastic analysis, especially for large sample sets.
• To address limitations in current data evaluation techniques for microplastic identification.

Main Methods:

• A novel method based on the μIDENT algorithm was developed.
• The method involves detecting and numerically describing vibrational bands in FTIR spectra using curve fitting.
• This generates a compact and characteristic peak list for accurate library searching.

Main Results:

• The new method achieves over 98% correct microplastic identification.
• It provides accurate and robust library searching capabilities.
• The approach is effective for μFTIR data, handling broad, overlapped, or complex vibrational bands.

Conclusions:

• The developed automated method significantly improves microplastic identification from FTIR/μFTIR data.
• This technique offers a more efficient and reliable solution for analyzing microplastics in environmental samples.
• The enhanced data evaluation approach addresses critical bottlenecks in microplastic research.