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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Detection Methods Environmental Sources Human Health Effects Marine & Wildlife Policy & Risk Sign in to save

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

Analytical Chemistry 2020 172 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 55 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Fionn Murphy, Jakob Strand Fionn Murphy, Fionn Murphy, Fionn Murphy, Fionn Murphy, Jakob Strand Fionn Murphy, Fionn Murphy, Fionn Murphy, Fionn Murphy, Fionn Murphy, Fionn Murphy, Jakob Strand Vitor Hugo da Silva, Jakob Strand Jakob Strand Fionn Murphy, Fionn Murphy, Jakob Strand Jakob Strand Jakob Strand Jakob Strand Fionn Murphy, Fionn Murphy, Fionn Murphy, José Manuel Amigo, Fionn Murphy, Jakob Strand Vitor Hugo da Silva, Fionn Murphy, José Manuel Amigo, José Manuel Amigo, Jakob Strand Vitor Hugo da Silva, Vitor Hugo da Silva, Fionn Murphy, Fionn Murphy, Fionn Murphy, Fionn Murphy, Fionn Murphy, Fionn Murphy, Fionn Murphy, Fionn Murphy, Jakob Strand Jakob Strand Jakob Strand Fionn Murphy, Fionn Murphy, Jakob Strand Fionn Murphy, Vitor Hugo da Silva, Jakob Strand José Manuel Amigo, Fionn Murphy, Fionn Murphy, Fionn Murphy, Fionn Murphy, Colin A. Stedmon, Colin A. Stedmon, Jakob Strand José Manuel Amigo, José Manuel Amigo, Fionn Murphy, Vitor Hugo da Silva, Jakob Strand Jakob Strand Colin A. Stedmon, Jakob Strand Colin A. Stedmon, Jakob Strand Jakob Strand Jakob Strand Fionn Murphy, Jakob Strand Jakob Strand Fionn Murphy, Jakob Strand Jakob Strand Fionn Murphy, Jakob Strand Fionn Murphy, Jakob Strand Colin A. Stedmon, Jakob Strand Jakob Strand Jakob Strand Vitor Hugo da Silva, Jakob Strand Jakob Strand

Summary

Researchers developed a method using focal plane array Fourier transform infrared imaging to classify and quantify microplastics smaller than 100 micrometers. The technique allows simultaneous chemical identification and size measurement of individual particles across a filter sample, significantly improving throughput compared to manual analysis. The study demonstrates that automated spectroscopic imaging can reliably detect and categorize very small microplastics that are often missed by conventional methods.

Study Type Environmental

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.

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