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Automated analysis of microplastics in the ocean
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Researchers developed an automated system for analyzing microplastics in ocean water samples using micro-FTIR microscopy and machine learning-assisted data interpretation, greatly reducing the manual analysis time required for large-scale monitoring of ocean plastic pollution.
In the last decades the pollution of the oceans with plastic particles smaller than 5 mm, called microplastics is moving into the focus of science and governments. The determination of microplastics includes several steps starting with sampling, work up and finally analysis. In recent studies with µFTIR microscopy was found as a powerful tool allowing the analysis of complete filters. The measurement is performed mostly by the spectrometer, while the data interpretation had to be made by hand based on false color images. We developed a novel approach to overcome the manual analysis and therefore decrease the high time demand for analysis of microplastics. It was possible to analyze measurement files containing up to 1.8 million single spectra by library searches against an optimized database of different synthetic and natural polymers. The accuracy and data quality was monitored by benchmark tests. The high quality data generated allowed image analysis, giving information for the particle size distribution for each polymer type as well as their distri- bution on the filter. All data was collected with relative ease even for complex sample matrices. This approach has significantly decreased the time demand for the interpretation of FTIR-imaging data and increased the generated data quality.
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Researchers developed a robust automated method for identifying microplastics in environmental samples using FTIR microscopy combined with machine learning-based spectral matching, improving the consistency and efficiency of microplastic identification compared to manual evaluation.
Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning
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Researchers developed a machine learning approach combined with micro-FTIR imaging to automate the analysis of microplastics in environmental samples, reducing the need for expert spectral interpretation. The system can process large datasets more efficiently than current spectral library matching methods, enabling scalable microplastic monitoring.
Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning
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Researchers developed machine learning approaches for automated microplastic identification in environmental samples from micro-FTIR imaging data, demonstrating improved accuracy and speed compared to traditional spectral library search methods for scalable analysis.
Automated Machine-Learning-Driven Analysis of Microplastics by TGA-FTIR for Enhanced Identification and Quantification
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Researchers developed an automated machine-learning system to identify and measure microplastics using a combination of heat analysis and infrared spectroscopy. The system can distinguish between different plastic types more accurately and faster than manual methods. Better detection tools like this are important because reliable measurement of microplastics in food, water, and the environment is essential for understanding human exposure levels.
Automated identification and quantification of microfibres and microplastics
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Researchers developed an automated method using FTIR imaging data analysis to simultaneously identify and quantify both microplastics and microfibers in environmental samples. Automation improves throughput and consistency compared to manual identification, addressing a key bottleneck in large-scale microplastic monitoring.
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