Development of a predictive model for two- and three-component inorganic systems in aqueous solutions using spectral analysis

This study presents an algorithm for analyzing spectral data through mathematical modeling, constructing prognostic models, and selecting optimal wavelength intervals for designing LED-based multisensor systems. The algorithm is implemented in Python and validated using experimental data from aqueous solutions of inorganic salts.
Key methodological aspects include:
– Application of multivariate calibration methods (PLS regression and multiple linear regression);
– Utilization of Shapley values to identify informative spectral wavelengths;
– Systematic enumeration to determine optimal wavelength intervals.
\end{itemize}
The developed model enables accurate prediction of two- and three-component systems in metal salt solutions using partial spectral data rather than full-spectrum analysis. Cross-validation demonstrates that:
– The model achieves comparable accuracy to full-spectrum approaches;
– The solution remains computationally efficient while maintaining predictive reliability.
The results confirm the model's adequacy for quantitative spectral analysis, particularly in resource-constrained environments where partial spectral data acquisition is advantageous.