Spatially offset Raman spectroscopy (SORS) allows chemical characterisation of biological tissues at depths of up to two orders of magnitude greater than conventional Raman spectroscopy. In this study, we demonstrate the use of SORS for the non-invasive prediction of depth of an inclusion within turbid media (e.g. biological tissues) using only external calibration data sets, thus extending our previous approach that required internal calibration. As with the previous methodology, the concept is based on relative changes in Raman band intensities of the inclusion that are directly related to the path length of Raman photons travelling through the medium thereby encoding the information of depth of the inclusion. However, here the calibration model is created using data only from external measurements performed at the tissue surface. This new approach facilitates a fully non-invasive methodology applicable potentially to in vivo medical diagnosis without any a priori knowledge. Monte Carlo simulations of photon propagation have been used to provide insight into the relationship between the spatial offset and the photon path lengths inside the tissues enabling one to derive a general scaling factor permitting the use of spatial offset measurements for the depth prediction. The approach was validated by predicting the depth of surface-enhanced Raman scattering (SERS) labelled nanoparticles (NPs) acting as inclusions inside a slab of ex vivo porcine tissue yielding an average root mean square error of prediction of 7.3% with respect to the overall tissue thickness. Our results pave the way for future non-invasive deep Raman spectroscopy in vivo by enabling, for example, the localisation of cancer lesions or cancer biomarkers in early disease diagnosis and targeted treatments.