Clinical diagnoses of bone health and fracture risk typically rely upon measurements of bone density or structure, but the strength of a bone is also dependent upon its chemical composition. One technology that has been used extensively in ex vivo, exposed-bone studies to measure the chemical composition of bone is Raman spectroscopy. This spectroscopic technique provides chemical information about a sample by probing its molecular vibrations. In the case of bone tissue, Raman spectra provide chemical information about both the inorganic mineral and organic matrix components, which each contribute to bone strength. To explore the relationship between bone strength and chemical composition, our laboratory has contributed to ex vivo, exposed-bone animal studies of rheumatoid arthritis, glucocorticoid-induced osteoporosis, and prolonged lead exposure. All of these studies suggest that Raman-based predictions of biomechanical strength may be more accurate than those produced by the clinically-used parameter of bone mineral density.

The utility of Raman spectroscopy in ex vivo, exposed-bone studies has inspired attempts to perform bone spectroscopy transcutaneously. Although the results are promising, further advancements are necessary to make non-invasive, in vivo measurements of bone that are of sufficient quality to generate accurate predictions of fracture risk. In order to separate the signals from bone and soft tissue that contribute to a transcutaneous measurement, we developed an overconstrained extraction algorithm that is based upon fitting with spectral libraries derived from separately-acquired measurements of the underlying tissue components. This approach allows for accurate spectral unmixing despite the fact that similar chemical components (e.g., type I collagen) are present in both soft tissue and bone and was applied to experimental data in order to transcutaneously detect, to our knowledge for the first time, age- and disease-related spectral differences in murine bone.