Theoretical Studies and Machine Learning Techniques in Microplastics
Microplastics Analysis and Characterization: Assessing Environmental Impact, wiley, ss.615-642, 2026
- Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
- Basım Tarihi: 2026
- Doi Numarası: 10.1002/9781119906612.ch20
- Yayınevi: wiley
- Sayfa Sayıları: ss.615-642
- Anahtar Kelimeler: Engineered water systems, Machine learning, Microplastics, Occurrence, Techniques
- Sivas Cumhuriyet Üniversitesi Adresli: Evet
Özet
Environmental pollution caused by microplastics (MPs) is an important and complex global problem. Current MP identification methods have shown significant limitations, such as low resolution, long imaging time, and limited particle size analyses. Machine learning (ML) algorithms have proven to be very useful in the identification of MP in recent years. Various combinations of ML methods, such as FTIR spectroscopy, Raman spectroscopy, and near-IR spectroscopy, and basic techniques for MP identification, are available. The most widely used ML model is the support vector machine, which effectively improves the traditional analysis method for spectral quality defects and improves the detection accuracy. Neural network models exhibit improved recognition effects with shorter detection times and better recognition efficiency. Critical literature reviews of recent developments in this field are limited.