This paper presents a preliminary application of Raman spectroscopy in conjunction with the chemometric method of Partial Least Squares to predict silicone concentrations in homogeneous turbid samples. The chemometric technique is applied to Raman spectra to develop an empirical, linear model relating sample spectra to polydimethysiloxane (silicone) concentration. This is done using a training set of samples having optical properties and known concentrations representative of those unknown samples to be predicted. Partial Least Squares, performed via cross- validation, was able to predict silicone concentrations in good agreement with true values. The detection limit obtained for this preliminary investigation is on par with that of magnetic resonance spectroscopy. The data acquisition time for this Raman based method is 200 seconds, which compares favorably with the 17 hour acquisition required for magnetic resonance spectroscopy to obtain a similar sensitivity. The combination of Raman spectroscopy and chemometrices shows promise as a tool for quantification of silicone concentrations from turbid samples. ©2005 Copyright SPIE - The International Society for Optical Engineering.

Accurate in vivo optical property data in the ultraviolet to visible range are scarce for many endoscope-accessible organs, yet such information is essential for understanding light propagation and identifying dosimetry standards for biomedical optical spectroscopy. We have performed a preliminary study towards the development of a reflectance-based system for endoscopic measurement of tissue optical properties relevant to fluorescence spectroscopy. To address the constraint of instrument channel diameter and strong attenuation of light in the spectral region of interest, maximum fiber separation distance was limited to 2.5 mm. Measurements were performed on tissue phantoms for a range of optical properties relevant to gastrointestinal mucosal tissues in the ultraviolet to visible range: absorption coefficients from 1 to 256 cm-1 and reduced scattering coefficients from 5 to 25 cm-1. Neural network and partial least squares algorithms were trained on radial reflectance profiles generated by a Monte Carlo model as well as by experimental data. These routines were then used to estimate absorption and reduced scattering coefficients from reflectance data. Results are discussed in terms of the optimization of models for optical property determination.