Shaikh Rubina, Tafintseva Valeria, Nippolainen Ervin, Virtanen Vesa, Solheim Johanne, Zimmermann Boris, Saarakkala Simo, Töyräs Juha, Kohler Achim, Afara Isaac O
Department of Technical Physics, University of Eastern Finland, 70211 Kuopio, Finland.
School of Physics, Clinical and Optometric Sciences, Technological University Dublin, D07 XT95 Dublin, Ireland.
J Pers Med. 2023 Jun 24;13(7):1036. doi: 10.3390/jpm13071036.
Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage.
中红外光谱（MIR）、近红外光谱（NIR）和拉曼光谱都是生物医学应用中成熟的分析技术。由于它们提供互补的化学信息，我们旨在确定将它们结合起来是否能增强其优势并减轻其劣势。本研究调查了融合MIR、NIR和拉曼光谱数据以表征关节软骨完整性的可行性。对来自牛髌骨的骨软骨标本进行机械和酶损伤，然后从受损标本和对照标本中获取MIR、NIR和拉曼数据。我们使用偏最小二乘判别分析（PLS-DA）评估了个体光谱方法将样本分类为损伤组或对照组的能力。通过应用两块（MIR和NIR、MIR和拉曼、NIR和拉曼）和三块方法（MIR、NIR和拉曼）组合数据集，进行多块PLS-DA以评估数据融合的潜力。单块模型的结果显示，NIR（93%）和MIR（92%）的分类准确率高于拉曼光谱（76%）。相比之下，我们分别观察到两块（MIR和NIR）模型和三块模型的最高分类效率为94%和93%。对三块模型中有助于判别分析的光谱特征进行的详细相关分析，为软骨损伤的分子起源提供了更多深入见解。
