RESUMEN
This study employed functional principal component analysis (FPCA) and covariate design of experiments (DoE) to mitigate the susceptibility of chemometric methods to unexpected variations stemming from operational and instrumental factors. A comparative analysis with partial least squares (PLS) revealed that our proposed approach effectively reduced variability across different analysts, days, and instruments. Specifically, FPCA was utilized to compress available spectral wavelength information, while covariate DoE aided in selecting an optimal training set within the experimental space. Subsequently, PLS was applied for the simultaneous determination of tadalafil (TD) and dapoxetine hydrochloride (DP) in their binary mixture. Validation of the proposed method through accuracy profiles demonstrated its reliability, paving the way for its application in pharmaceutical analysis.
