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

Introduction: Interpreting Plasma-Activated Saline (PAS) is challenging because reactive oxygen, nitrogen, and chlorine species are strongly interdependent, making it difficult to isolate their individual contributions to physicochemical properties such as Oxidation-Reduction Potential (ORP). This study explores these relationships using a multivariate chemometric workflow. Method: A small experimental dataset comprising 10 PAS observations generated by a serial DBD-GAPJ reactor was analyzed using Exploratory Data Analysis and Multiple Linear Regression. ORP was modeled as a function of O3, H2O2, HClO, NO3−, and NO2−. Model interpretation was supported by Variance Inflation Factor analysis, standardized coefficients, Leave-One-Out Cross-Validation, and residual diagnostics. Results and Discussion: H2O2 showed the strongest bivariate correlation with ORP (r = 0.86). The regression model achieved R² = 0.84 and adjusted R² = 0.65, but LOOCV produced Q² = −0.33, indicating poor out-of-sample prediction. Strong multicollinearity among reactive species complicated coefficient interpretation, and none of the individual predictors reached conventional statistical significance. Standardized coefficients identified H2O2 as the strongest relative contributor, while O3 and HClO remained chemically plausible interdependent contributors. Conclusion: The proposed workflow is valuable for interpreting small, collinear chemical datasets, but the results should be regarded as hypothesis-generating rather than predictive, emphasizing the importance of standardization, cross-validation, and multicollinearity diagnostics

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