  Raman spectroscopy's capability to provide meaningful composition predictions
is heavily reliant on a pre-processing step to remove insignificant spectral
variation. This is crucial in biofluid analysis. Widespread adoption of
diagnostics using Raman requires a robust model which can withstand routine
spectra discrepancies due to unavoidable variations such as age, diet, and
medical background. A wealth of pre-processing methods are available, and it is
often up to trial-and-error or user experience to select the method which gives
the best results. This process can be incredibly time consuming and
inconsistent for multiple operators.
  In this study we detail a method to analyze the statistical variability
within a set of training spectra and determine suitability to form a robust
model. This allows us to selectively qualify or exclude a pre-processing
method, predetermine robustness, and simultaneously identify the number of
components which will form the best predictive model. We demonstrate the
ability of this technique to improve predictive models of two artificial
biological fluids.
  Raman spectroscopy is ideal for noninvasive, nondestructive analysis. Routine
health monitoring which maximizes comfort is increasingly crucial, particularly
in epidemic-level diabetes diagnoses. High variability in spectra of biological
samples can hinder Raman's adoption for these methods. Our technique allows the
decision of optimal pre-treatment method to be determined for the operator;
model performance is no longer a function of user experience. We foresee this
statistical technique being an instrumental element to widening the adoption of
Raman as a monitoring tool in a field of biofluid analysis.
