Machine Learning Tools for Raman Spectral Data Processing in Food and Neuroscience Applications

In fifteen words or less, explain the significance of this contribution (Novel Aspect).: Machine learning methods for single-step Raman analysis in maple syrup quality control and neurotransmitter detection.

Abstract Text:

Raman spectroscopy is a powerful tool for analyzing biological and natural products, but real-world implementation faces challenges in data interpretation, reproducibility, and throughput. This presentation will address these challenges by demonstrating machine learning-based approaches for analyzing Raman spectral data from two domains: maple syrup quality control and neurotransmitter detection. We will present a single-step Raman spectroscopy strategy combined with machine learning that enables simultaneous quantification of sucrose concentration, transmittance prediction, and flavor classification in commercial maple syrup samples. Models include Random Forest regressors for sugar concentration, and CNNs trained to classify flavor grades. In addition, background fluorescence signals, typically ignored, were used to estimate optical clarity (transmittance), adding a novel dimension to spectral utility. In neuroscience, surface-enhanced Raman scattering (SERS) was combined with optogenetic stimulation to monitor neurotransmitter release from live neurons. Genetically modified neurons expressing light-activated channels were stimulated using short pulses of blue light, while SERS nanosensors positioned near neuronal axons captured corresponding chemical changes. Raman spectral data were processed using machine learning models, including convolutional neural networks and regression methods, to detect and quantify neurotransmitters such as dopamine and glutamate at trace levels under stimulated conditions.