One-Class Genetic Algorithm for Authentication Analysis of Spectrochemical Data

One-class classifier (OCC) models are widely applied to solve classification problems where control or class modeling from a target class is necessary. In this study, OCC models such as Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OCPLS) were associated with a new variable selection strategy, the one-class genetic algorithm (OGA), for classification analyses in three clinical applications: COVID-19, endometriosis, and dengue samples. DD-SIMCA was implemented in a rigorous approach, using α = 0.05, while OCPLS was performed with partial robust M-regression (PRM). For the three cases, a better classification performance was obtained using the OGA associated with the OCC model. The performance of the OGA-PRM-OCPLS showed better results for both COVID-19 and endometriosis cases when compared to DD-SIMCA, with a classification sensitivity of 100%. However, the best results for dengue classification were obtained by using the OGA-DD-SIMCA model (sensitivity = 100%). The selected variables obtained by the OGA can be used to relate this information to biomarkers capable of distinguishing between case and control groups. These findings have the potential to improve some disease diagnosis using chemometrics for the development of rapid, low-cost, and minimally invasive screening methodologies.