Abstract
Cold atmospheric pressure plasma (CAPP) and plasma-activated water (PAW) are emerging nonthermal technologies. Due to the complexity of plasma-generated species as a function of plasma generation conditions, there is a need to develop process validation. This study aims to evaluate plasma interactions with DNA-based surrogates using infrared spectroscopy and gradient boosting decision tree, a machine learning algorithm, for data analysis to validate the decontamination effectiveness of CAPP and PAW. Chitosan-DNA films were developed and treated with CAPP or PAW. Changes in the spectral properties of DNA were characterized with Fourier-transform infrared spectroscopy (FTIR) and correlated to the dosage levels of CAPP and PAW and decontamination efficacy. Using the LightGBM algorithm, both plasma dosage and the inactivation efficacy against bacteria and biofilms were predicted with high accuracy (>89%) based on the spectral features of DNA. In summary, this study illustrates a novel approach for validating the decontamination efficacy of plasma processing.
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