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Prospective Application of Artificial Intelligence Towards the Detection, and Classifications of Microplastics with Bibliometric Analysis
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Using bibliometric analysis to map the microplastics research landscape, this study proposes integrating AI techniques — including machine learning with FTIR spectroscopy, holographic imaging, and 3D modeling — to overcome the labor-intensive limitations of traditional MP identification and classification methods. Scalable, AI-driven detection tools are critical for accelerating MP monitoring and research, enabling the high-throughput data collection needed to understand population-level exposure and inform regulatory standards.
Microplastics (MPs) pose a significant threat to aquatic ecosystems, impacting both plant and animal life. Their small size and pervasive presence make their identification and characterization challenging, often requiring time-consuming and labour-intensive analytical methods. This study employs bibliometric analysis to identify influential journals, authors, organizations, countries, and keywords in MP research. Recognizing the limitations of traditional approaches, we propose the integration of artificial intelligence (AI) techniques to enhance MP identification and categorization through machine learning methods. Specifically, we explore the application of novel techniques such as holographic imaging, Fourier-transform infrared spectroscopy (FTIR) coupled with machine learning algorithms, and 3D modelling approaches for the identification and classification of various types of MPs. By leveraging AI technologies, this research aims to overcome existing challenges in MP research and establish a milestone in the application of AI for addressing environmental threats posed by microplastics.
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