Multimodal multiplex Raman spectroscopy optimized for in vivo chemometrics

We have designed and constructed a multimodal multiplex Raman spectrometer which uses multi-wavelength excitation to better detect signals in the presence of fluorescence by taking advantage of the shift-variance of the Raman signal with respect to excitation frequency. Coupled with partial-least-squares (PLS) regression, the technique applied to ethanol estimation in a tissue phantom achieves root-mean-squared-cross-validation errors (RMSCVE) of 9.2 mmol/L with a model formed with 2 principal components, compared to a single wavelength data set with equivalent energy where 7 principal components were used to achieve an RMSCVE of 39.1 mmol/L.