Hyperspectral Imaging as a Potential Online Detection Method of Microplastics

Area of Science:

• Environmental Science
• Analytical Chemistry
• Spectroscopy

Background:

• Microplastic pollution in aquatic environments is a growing global concern.
• Current identification methods face challenges in speed and efficiency.
• Spectral imaging, particularly hyperspectral imaging (HSI), shows potential for rapid polymer identification.

Purpose of the Study:

• To review recent studies on polymer identification using HSI for microplastic detection.
• To analyze classification algorithms and factors influencing online HSI implementation.
• To assess HSI's limit of detection, particle size effects, and performance across various sample types.

Main Methods:

• Review of existing literature on hyperspectral imaging (HSI) for polymer identification.
• Analysis of chemometric approaches and classification algorithms used with HSI.
• Evaluation of factors affecting online HSI implementation, including detection limits and particle size.

Main Results:

• HSI combined with chemometric analysis can significantly reduce polymer identification times.
• Classification accuracy is influenced by particle size and the chosen algorithm.
• HSI demonstrates potential for detecting various types of microplastic samples.

Conclusions:

• Hyperspectral imaging (HSI) presents a viable and fast alternative for online microplastic detection.
• Further research into optimized algorithms and detection parameters can enhance HSI's effectiveness.
• HSI offers a scalable solution to address the increasing challenge of microplastic pollution monitoring.