Deciphering molecular changes (such as structure conformation) in complex systems can be challenging. If these conformation changes could be monitored in real time and modeled, it would open up new opportunities to gain a deeper understanding of signal pathways in biological systems. Through the use of confocal Raman spectroscopy, which captures the molecular fingerprint with high precision, we monitored the evolution of these changes over time. The key was to identify the spectral regions within the Raman spectrum. We employed an adaptive principal component analysis (PCA) technique to study Raman spectra and modeled strain conditions in this molecular network. Experiments were completed according to a full factorial design of experiment (DOE) approach with variable parameters including laser power density and stage temperature over the spectral range of 50-4000 /cm. Thermal effects were also introduced through the controllable micro-stage heater. We implemented this adaptive PCA technique on both individual and blended amino acids in order to highlight vibrational modes within complex samples. We examined three structurally similar branched chain amino acids to study similarities and identified specific vibrational modes that indicate molecular bending, rocking, and wagging. Results demonstrate that adaptive PCA is capable of highlighting subtle changes in molecular networks due to environmental and compositional variations. With an understanding of which data (spectral band) is more important, this speeds up computation and provides real-time analysis for monitoring conformational changes.
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