Spatially Offset and Transmission Raman Spectroscopy for Determination of Depth of Inclusion in Turbid Matrix

Sara Mosca, Priyanka Dey, Tanveer A. Tabish, Francesca Palombo, Nicholas Stone, Pavel Matousek

Research output: Contribution to journalArticlepeer-review


We propose an approach for the prediction of the depth of a single buried object within a turbid medium combining spatially offset Raman spectroscopy (SORS) and transmission Raman spectroscopy (TRS) and relying on differential attenuation of individual Raman bands brought about by the spectral variation of matrix absorption (and scattering). The relative degree of the Raman band changes is directly related to the path length of Raman photons traveling through the medium, thereby encoding the information on the depth of the object within the matrix. Through a calibration procedure with root mean square error of calibration (RMSEC) = 3.4%, it was possible to predict the depth of a paracetamol (acetaminophen) inclusion within a turbid matrix consisting of polyethylene (PE) by monitoring the relative intensity of two Raman bands of paracetamol exhibiting differential absorption by the matrix. The approach was shown to be largely insensitive to variations of the amount of the inclusion (paracetamol) and to the overall thickness of the turbid matrix (PE) with a root mean square error of prediction (RMSEP) maintained below 10% for the tested cases. This represents a major advantage over previously demonstrated comparable depth determination Raman approaches (with the exception of full Raman tomography requiring complex mathematical reconstruction algorithms). The obtained experimental data validate the proposed approach as an effective tool for the noninvasive determination of the depth of buried objects in turbid media with potential applications including determining noninvasively the depth of a lesion in cancer diagnosis in vivo.
Original languageEnglish
Pages (from-to)8994–9000
JournalAnalytical Chemistry
Issue number14
Publication statusPublished - 16 Jul 2019


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