Articular cartilage posesses unique material properties due to a complex depth‐dependent composition of sub‐components. Raman spectroscopy has proven valuable in quantifying this composition through cartilage cross‐sections. However, cross‐sectioning requires tissue destruction and is not practical in situ. In this work, Raman spectroscopy‐based multivariate curve resolution (MCR) was employed in porcine cartilage samples (n = 12) to measure collagen, glycosaminoglycan, and water distributions through the surface for the first time; these were compared against cross‐section standards. Through the surface Raman measurements proved reliable in predicting composition distribution up to a depth of approximately 0.5 mm. A fructose‐based optical clearing agent (OCA) was also used in an attempt to further improve depth of resolution of this measurement method. However, it did not; mainly due to a high‐spectral overlap with the Raman spectra of main cartilage sub‐components. This measurement technique potentially could be used in situ, to better understand the etiology of joint diseases such as osteoarthritis (OA).

中文翻译：

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通过基于拉曼光谱的多元分析测量整个表面的软骨亚成分分布。

关节软骨具有独特的材料特性，这是由于复杂的，取决于深度的子组件组成。拉曼光谱被证明在通过软骨横断面定量该成分方面很有价值。但是，横截面需要破坏组织，因此不可行。在这项工作中，猪软骨样品（n = 12）中使用了基于拉曼光谱的多元曲线分辨率（MCR）来首次测量胶原蛋白，糖胺聚糖和整个表面的水分布。将这些与横截面标准进行比较。通过表面拉曼测量证明在预测约0.5毫米深度的成分分布方面是可靠的。还使用基于果糖的光学清除剂（OCA）来尝试进一步提高此测量方法的分辨率深度。但是，事实并非如此。主要是由于与主要软骨子成分的拉曼光谱存在高光谱重叠。此测量技术可能会在原位使用，以更好地了解关节疾病（如骨关节炎（OA））的病因。