Advances and innovations in machine learning-based spectral detection methods for trace organic pollutants.

Area of Science:

• Environmental Science
• Analytical Chemistry
• Computer Science

Background:

• Effective detection of trace organic pollutants in water is vital for environmental safety.
• Conventional methods lack the speed, sensitivity, and on-site capabilities required for modern monitoring.
• There is a growing need for advanced analytical techniques to address these limitations.

Purpose of the Study:

• To review recent advancements in applying machine learning (ML) to spectral detection methods for trace organic pollutants.
• To explore how ML techniques enhance the performance of various spectral detection methods.
• To discuss future directions for AI-driven pollutant detection systems.

Main Methods:

• Review of ML techniques including data augmentation (e.g., Generative Adversarial Networks - GANs), intelligent feature extraction (e.g., Convolutional Neural Networks - CNNs), and classification/identification (e.g., Random Forests - RF).
• Analysis of combined spectral techniques (e.g., Surface-Enhanced Raman Spectroscopy - SERS, Infrared Spectroscopy) with ML algorithms.
• Examination of model interpretability and cross-laboratory validation frameworks.

Main Results:

• ML integration enhances the sensitivity, selectivity, and robustness of spectral detection for trace organic pollutants.
• Successful applications include antibiotic database creation using SERS-CNN and microplastic classification via Infrared Spectroscopy-RF.
• Standardized detection processes and evaluation systems are crucial for reliable results.

Conclusions:

• ML offers powerful tools to overcome limitations in traditional spectral detection of water pollutants.
• Future research should focus on efficient ML algorithms, hardware-algorithm integration, and AI-driven autonomous systems for pollutant management.
• This review highlights the transformative potential of ML in environmental monitoring and safety.