Interpreting Reactive Species Interdependencies in Plasma-Activated Saline: An Exploratory Multivariate Workflow

Nilton Francelosi Azevedo Neto; Orisson Ponce Gomes; Lucas Pereira Piedade; Felipe Souza Miranda; Rodrigo Savio Pessoa

doi:10.56705/ijodas.v7i2.417

Authors

Nilton Francelosi Azevedo Neto Instituto Tecnológico de Aeronáutica
Orisson Ponce Gomes Universite de Pau et des Pays de L’Adour
Lucas Pereira Piedade École de Technologie Supérieure
Felipe Souza Miranda Aeronautics Institute of Technology
Rodrigo Savio Pessoa Aeronautics Institute of Technology

DOI:

https://doi.org/10.56705/ijodas.v7i2.417

Keywords:

Plasma-Activated Saline, Reactive Oxygen and Nitrogen Species, Multiple Linear Regression, Multicollinearity, Oxidation-Reduction Potential

Abstract

Interpreting complex chemical systems with highly correlated predictor variables poses a significant analytical challenge. Plasma-Activated Saline (PAS) exemplifies such a system, where interdependencies among reactive species can mask their individual effects on physicochemical properties such as the Oxidation-Reduction Potential (ORP). This study presents an exploratory workflow to investigate these relationships using a small dataset (n = 10) characterizing PAS generated by a serial DBD-GAPJ reactor. A computational routine combining Exploratory Data Analysis (EDA) and Multiple Linear Regression modeled ORP as a function of O₃, H₂O₂, HClO, NO₃^-, and NO₂^-. While EDA indicated H₂O₂ as the species most correlated with ORP, the regression model (R² = 0.84, adjusted R² = 0.65) revealed strong multicollinearity between O₃ and HClO (r = +0.82; VIF > 6), complicating coefficient interpretation. Leave-one-out cross-validation yielded a negative predictive R² (Q² = -0.33), indicating limited out-of-sample predictive capability given the small sample size. When coefficients were standardized to account for differing concentration ranges, H₂O₂ showed the largest relative influence on ORP, followed by HClO and O₃, although none of the predictors reached conventional statistical significance (p > 0.10). Considered alongside the standard reduction potentials (E°) of the species, these results suggest that H₂O₂, O₃, and HClO are plausible, interdependent contributors to ORP, rather than supporting a single dominant driver. This work illustrates both the value and the limitations of multivariate modeling applied to small, collinear chemical datasets, highlighting standardization, cross-validation, and multicollinearity diagnostics as essential steps for robust chemometric interpretation

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