Isaac O Afara1,2, Adekunle Oloyede3. 1. Department of Applied Physics, University of Eastern Finland, Kuopio, Finland. 2. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia. 3. School of Chemistry, Physics, and Mechanical Engineering, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia.
Abstract
OBJECTIVE: Spectroscopic techniques, such as near-infrared (NIR) spectroscopy, are gaining significant research interest for characterizing connective tissues, particularly articular cartilage, because there is still a largely unmet need for rapid, accurate and objective methods for assessing tissue integrity in real-time during arthroscopic surgery. This study aims to identify the NIR spectral range that is optimal for characterizing cartilage integrity by (a) identifying the contribution of its major constituents (collagen and proteoglycans) to its overall spectrum using proxy constituent models and (b) determining constituent-specific spectral contributions that can be used for assessment of cartilage in its physiological state. DESIGN: The NIR spectra of cartilage matrix constituent models were measured and compared with specific molecular components of organic compounds in the NIR spectral range in order to identify their bands and molecular assignments. To verify the identified bands, spectra of the model compounds were compared with those of native cartilage. Since water obscures some bands in the NIR range, spectral measurements of the native cartilage were conducted under conditions of decreasing water content to amplify features of the solid matrix components. The identified spectral bands were then compared and examined in the resulting spectra of the intact cartilage samples. RESULTS: As water was progressively eliminated from cartilage, the specific contribution of the different matrix components was observed to correspond with those identified from the proxy cartilage component models. CONCLUSION: Spectral peaks in the regions 5500 to 6250 cm-1 and 8100 to 8600 cm-1 were identified to be effective for characterizing cartilage proteoglycan and collagen contents, respectively.
OBJECTIVE: Spectroscopic techniques, such as near-infrared (NIR) spectroscopy, are gaining significant research interest for characterizing connective tissues, particularly articular cartilage, because there is still a largely unmet need for rapid, accurate and objective methods for assessing tissue integrity in real-time during arthroscopic surgery. This study aims to identify the NIR spectral range that is optimal for characterizing cartilage integrity by (a) identifying the contribution of its major constituents (collagen and proteoglycans) to its overall spectrum using proxy constituent models and (b) determining constituent-specific spectral contributions that can be used for assessment of cartilage in its physiological state. DESIGN: The NIR spectra of cartilage matrix constituent models were measured and compared with specific molecular components of organic compounds in the NIR spectral range in order to identify their bands and molecular assignments. To verify the identified bands, spectra of the model compounds were compared with those of native cartilage. Since water obscures some bands in the NIR range, spectral measurements of the native cartilage were conducted under conditions of decreasing water content to amplify features of the solid matrix components. The identified spectral bands were then compared and examined in the resulting spectra of the intact cartilage samples. RESULTS: As water was progressively eliminated from cartilage, the specific contribution of the different matrix components was observed to correspond with those identified from the proxy cartilage component models. CONCLUSION: Spectral peaks in the regions 5500 to 6250 cm-1 and 8100 to 8600 cm-1 were identified to be effective for characterizing cartilage proteoglycan and collagen contents, respectively.
Authors: David A Reiter; Remigio A Roque; Ping-Chang Lin; Onyi Irrechukwu; Stephen Doty; Dan L Longo; Nancy Pleshko; Richard G Spencer Journal: Magn Reson Med Date: 2010-11-30 Impact factor: 4.668
Authors: Isaac O Afara; Markku Hauta-Kasari; Jukka S Jurvelin; Adekunle Oloyede; Juha Töyräs Journal: Physiol Meas Date: 2015-08-06 Impact factor: 2.833