FTIR Spectral Prediction of Ionic Liquids Using a Novel Hybrid MARS–PLS2 Chemometric Framework

ABSTRACT Understanding the biochemical basis of bacterial inhibition from spectral signatures requires modeling approaches capable of handling high‐dimensional, collinear, and multiresponse data. In this study, we proposed a novel hybrid framework that integrates partial least squares regression for multiple responses (PLS2) within the multivariate adaptive regression splines (MARS) model, referred to as the MARS–PLS2 approach. Unlike conventional MARS, which applies linear regression at terminal nodes, the proposed framework leverages PLS2 to simultaneously model five inhibition responses ( Escherichia coli , Bacillus subtilis , MR‐ Staphylococcus aureus , Klebsiella pneumoniae , and S. aureus ), enabling an efficient representation of shared spectral–biochemical variations. A sparsity‐driven feature selection strategy identifies a subset of informative wavenumbers associated with key vibrational modes, including C–H stretching and bending, O–H, C–N, C=C, and N–H stretching, which are biochemically relevant to membrane dynamics, protein interactions, and cellular response mechanisms. The proposed MARS–PLS2 framework substantially enhances prediction accuracy and model interpretability. For example, for E. coli , the MARS–PLS2 model achieves a high , with a significantly lower RMSE of 1.655 and MAE of 1.201, compared with the standard PLS2 model (, RMSE = 6.094, MAE = 5.655) and standard MARS model (, RMSE = 18.508, MAE = 10.412). Similarly, for B. subtilis , the MARS–PLS2 framework yields , RMSE = 1.466, and MAE = 1.124, considerably outperforming the standard PLS2 results (, RMSE = 5.383, MAE = 4.071) and standard MARS (, RMSE = 13.745, MAE = 9.130). Furthermore, validation using permutation testing and bootstrap analysis confirms the robustness and stability of the proposed model, with consistently high values and low prediction errors across resampled datasets, indicating that the observed performance is not due to random variation. Overall, the proposed MARS–PLS2 framework provides a robust, interpretable, and accurate approach for linking spectral features to bacterial inhibition responses, offering both improved predictive performance and meaningful biochemical insight. The framework can be extended to broader chemometric applications, including rapid diagnostics and antimicrobial research.