Unveiling the Potential: Can Machine Learning Cluster Colorimetric Images of Cold Atmospheric Plasma Treatment?

Abstract

In this transformative study, machine learning (ML) and t‐distributed stochastic neighbor embedding (t‐SNE) are employed to interpret intricate patterns in colorimetric images of cold atmospheric plasma (CAP)‐treated water. The focus is on CAP's therapeutic potential, particularly its ability to generate reactive oxygen and nitrogen species (RONS) that play a crucial role in antimicrobial activity. RGB, HSV, LAB, YCrCb, and grayscale color spaces are extracted from the colorimetric expression of oxidative stress induced by RONS, and these features are used for unsupervised ML, employing density‐based spatial clustering of applications with noise (DBSCAN). The DBSCAN model's performance is evaluated using homogeneity, completeness, and adjusted rand index with a predictive data distribution graph. The best results are achieved with 3,3′,5,5′‐tetramethylbenzidine–potassium iodide colorimetric assay solution immediately after plasma treatment, with values of 0.894, 0.996, and 0.826. t‐SNE is further conducted for the best‐case scenario to evaluate the clustering efficacy and find the best combination of features to better present the results. Correspondingly, t‐SNE enhances clustering efficacy and adeptly handles challenging points. The approach pioneers dynamic and comprehensive solutions, showcasing ML's precision and t‐SNE's transformative visualization. Through this innovative fusion, complex relationships are unraveled, marking a paradigm shift in biomedical analytical methodologies.

Keywords

• artificial intelligence
• colorimetric sensors
• microfluidic paper‐based analytical devices
• plasma medicine
• plasma‐treated liquids
• standardization