Recent Advances in Implementation of Machine Learning for Environmental Nontarget Identification

Area of Science:

• Environmental Chemistry
• Analytical Chemistry
• Computational Chemistry

Background:

• Emerging pollutants present significant risks to human health and ecosystems.
• Nontarget analysis using mass spectrometry is crucial for detecting novel pollutants.
• Structural elucidation of detected compounds is a critical bottleneck in nontarget analysis.

Purpose of the Study:

• To review machine learning (ML) techniques for accelerating nontarget structural elucidation.
• To investigate the design, characteristics, and evaluation of ML-based computational approaches.
• To discuss the application of ML in environmental nontarget analysis for new pollutant identification.

Main Methods:

• Focus on spectral library matching, structural database retrieval, and de novo structure generation using ML.
• Investigated design principles and technical characteristics of computational methods.
• Comparative evaluation of different ML approaches for structural elucidation.

Main Results:

• Machine learning techniques show promise in enhancing the speed and accuracy of structural elucidation.
• Demonstrated applications of ML in identifying new environmental pollutants through nontarget analysis.
• Evaluated the strengths and limitations of various ML-driven computational strategies.

Conclusions:

• ML integration is vital for advancing nontarget analysis and environmental pollutant identification.
• Current ML approaches face challenges but offer significant potential for future development.
• This review promotes ML adoption for more effective environmental monitoring and risk assessment.