Wang C.-YKo T.-SHsu C.-C.JERRY CHENG-CHE HSU2022-03-222022-03-22202116128850https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114799300&doi=10.1002%2fppap.202100096&partnerID=40&md5=2ce405072fb40b382f819eec61cbe95chttps://scholars.lib.ntu.edu.tw/handle/123456789/598219This 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. ? 2021 Wiley-VCH GmbH.artificial neural network (ANN)explainable artificial intelligence (XAI)local interpretable model-agnostic explanations (LIME)Machine learningoptical emission spectroscopyplasma-liquid interactionsolution plasmaChemical analysisLight emissionLimeMultilayer neural networksEmission linesLinear modelingMultilayer artificial neural networksOptical emission spectroscopies (OES)Solution conductivitySpectral featureOptical emission spectroscopyjournal article10.1002/ppap.2021000962-s2.0-85114799300