Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data

Abstract

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.

Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics

“…The notion of a testing platform that is reagent-free and requires minimal sample preparation is incredibly attractive, especially in the context of achieving a cost-effective yet efficient analysis of numerous samples or patients in routine clinical practices [3]. Data fusion, also known as data blocking, combines two or more systems biology datasets from different "omics" modalities to generate greater organism insights and can be exploited to provide an even more integrated approach [4]. Biospectroscopy approaches employ spectrochemical technologies (Table 1) primarily associated with mid-infrared (MIR) or Raman platforms [5], although near-infrared (NIR) can also be included [4].…”

“…Data fusion, also known as data blocking, combines two or more systems biology datasets from different "omics" modalities to generate greater organism insights and can be exploited to provide an even more integrated approach [4]. Biospectroscopy approaches employ spectrochemical technologies (Table 1) primarily associated with mid-infrared (MIR) or Raman platforms [5], although near-infrared (NIR) can also be included [4]. The application of spectrochemical methods, whose output in the form of vibrational spectra is fed into chemometric algorithms, is gaining increasing recognition as potential screening and/or diagnostic tools in clinical settings [6].…”