Self-absorption corrected non-invasive transmission Raman spectroscopy (of biological tissue)†
The first near infrared window in biological tissue (λ ∼ 700–950 nm) is of great interest for its potential to safely deliver light based diagnosis and therapeutic interventions, especially in the burgeoning field of nano-theranostics. In this context, Raman spectroscopy is increasingly being used to provide rapid non-invasive chemical molecular analysis, including bulk tissue analysis by exploiting the near infrared window, with transmission Raman spectroscopy (TRS). The disadvantage of this approach, is that when probing depths of several centimetres self-attenuation artefacts are typically exhibited, whereby TRS spectra can suffer from relative changes in the “spectral features” due to differential absorption of Raman photons by the various constituents of biological tissues. Simply put, for a homogenous substance with increasing thickness, spectral variances occur due to the optical properties of the material and not through changes in the chemical environment. This can lead to misinterpretation of data, or features of interest become obscured due to the unwanted variance. Here we demonstrate a method to correct TRS data for this effect, which estimates the pathlengths derived from peak attenuation and uses expected optical properties to transform the data. In a validation experiment, the method reduced total Raman spectral intensity variances >5 fold, and improved specific peak ratio distortions 35×. This is an important development for TRS, Spatially Offset Raman Spectroscopy (SORS) and related techniques operating at depth in the near IR window; applicable to samples where there is large sample thickness and inter- and intra-sample thickness is variable i.e. clinical specimens from surgical procedures such as breast cancer. This solution is expected to yield lower detection limits and larger depths in future applications such as non-invasive breast cancer diagnosis in vivo.
- This article is part of the themed collection: Biomedical Raman Imaging