Combining IR and Raman Spectroscopies for Enhanced Accuracy and Precision in the Determination of Lipid Composition in Liposomes

Reducing measurement uncertainty is crucial to enable the adoption of rapid point-of-use techniques for clinical and industrial applications. Diagnosis of neonatal respiratory distress syndrome and liposome formulation quality control are two applications for which measuring the ratio of the lecithin to sphingomyelin composition of liposomes is important, for which no rapid measurement currently exists. Raman and infrared spectroscopies are two complementary approaches to examine characteristic molecular vibrations that can spectroscopically measure liposomes and, when combined with machine learning, predict their composition. We show that employing a data-fusion approach the uncertainty in the predicted compositions compared to the individual modalities (IR R2: 0.902 and Raman R2: 0.951) can be reduced to obtain more accurate and precise measurements (low-level fused model R2: 0.973, mean squared error: 0.024, prediction interval width: 0.303, high-level weighted fusion model R2: 0.970, mean squared error: 0.027, prediction interval width: 0.268).