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Raman spectroscopy's capability to provide meaningful composition predictionsis heavily reliant on a pre-processing step to remove insignificant spectralvariation. This is crucial in biofluid analysis. Widespread adoption ofdiagnostics using Raman requires a robust model which can withstand routinespectra discrepancies due to unavoidable variations such as age, diet, andmedical background. A wealth of pre-processing methods are available, and it isoften up to trial-and-error or user experience to select the method which givesthe best results. This process can be incredibly time consuming andinconsistent for multiple operators.  In this study we detail a method to analyze the statistical variabilitywithin a set of training spectra and determine suitability to form a robustmodel. This allows us to selectively qualify or exclude a pre-processingmethod, predetermine robustness, and simultaneously identify the number ofcomponents which will form the best predictive model. We demonstrate theability of this technique to improve predictive models of two artificialbiological fluids.  Raman spectroscopy is ideal for noninvasive, nondestructive analysis. Routinehealth monitoring which maximizes comfort is increasingly crucial, particularlyin epidemic-level diabetes diagnoses. High variability in spectra of biologicalsamples can hinder Raman's adoption for these methods. Our technique allows thedecision of optimal pre-treatment method to be determined for the operator;model performance is no longer a function of user experience. We foresee thisstatistical technique being an instrumental element to widening the adoption ofRaman as a monitoring tool in a field of biofluid analysis.

Optimized Preprocessing and Machine Learning for Quantitative Raman Spectroscopy in Biology