Machine Learning with Explainable Artificial Intelligence Vision for Characterization of Solution Conductivity Using Optical Emission Spectroscopy of Plasma in Aqueous Solution

• Published in: Plasma processes and polymers 2021-12, Vol.18 (12), p.n/a
• Main Authors: Wang, Ching‐Yu, Ko, Tsung‐Shun, Hsu, Cheng‐Che
• Format: Article
• Language: English
• Subjects: Analytical chemistry; Aqueous solutions; Artificial intelligence; artificial neural network (ANN); Artificial neural networks; Emission analysis; Emission spectroscopy; Explainable artificial intelligence; explainable artificial intelligence (XAI); Learning theory; Lime; local interpretable model‐agnostic explanations (LIME); Machine learning; Multilayers; Optical emission spectroscopy; Plasma; plasma‐liquid interaction; solution plasma; Spectra; Vision
• ISSN: 1612-8850; 1612-8869
• DOI: 10.1002/ppap.202100096

This study presents an explainable artificial intelligence (XAI) vision for optical emission spectroscopy (OES) of plasma in aqueous solution. We aim to characterize the plasma and OES with XAI. Trained with 18000 spectra, a multilayer artificial neural network (ANN) model accurately predicted the solution conductivity. Local interpretable model‐agnostics explanations (LIME), an XAI method, interpreted the model through perturbing spectral features and fitting the feature contribution with a linear model. LIME showed that OH, Hγ, and Hβ emission lines were critical to the model, differing from the lines typically selected by humans. The results demonstrated that machine captured the spectral features neglected by humans. We believe using XAI for plasma OES analysis impacts the fields of plasma and analytical chemistry. Artificial neural network (ANN) with explainable artificial intelligence (XAI) for plasma optical emission spectroscopy (OES) analysis provides a novel method for characterizing plasma in aqueous solution.