Offline machine learning-based concurrent and rapid determination of acetaminophen, dextromethorphan, guaifenesin, and phenylephrine using UV-vis spectroscopy - Felipe, Mikhael Anthony A. (Animo Repository, 2021)
Currently, wet chemistry techniques such as HPLC dominate the pharmaceutical industry when complying with API content testing. UV-Vis spectroscopy proved to be an efficient and nondestructive method that could potentially obtain API assays simultaneously. However, its performance becomes limited due to severely overlapped spectra, such as the case of Acetaminophen (APAP), Dextromethorphan HBr (DEX), Guaifenesin (GUA), and Phenylephrine HCl (PHE) combination drug. Therefore, four machine learning models, namely, Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest Regression (RFR), and Artificial Neural Network (ANN), were developed, optimized, and tested that would breach this limitation. Subsequently, the four developed models were compared to each other, with ANN having R2 correlations of 99.96% for APAP, 95.65% for DEX, 99.51% for GUA, and 90.08% for PHE, which outperforms the three models. Finally, the optimum ANN model was validated, resulting in correlations of 99.25% for APAP, 95.61% for DEX, 99.34% for GUA, and 89.38% for PHE, proving its capability to generalize its prediction of API concentrations.