Fourier Transform Infrared Microspectroscopy Combined with Principal Component Analysis and Artificial Neural Networks for the Study of the Effect of β-Hydroxy-β-Methylbutyrate (HMB) Supplementation on Articular Cartilage

Abstract

The potential of Fourier Transform infrared microspectroscopy (FTIR microspectroscopy) and multivariate analyses were applied for the classification of the frequency ranges responsible for the distribution changes of the main components of articular cartilage (AC) that occur during dietary β-hydroxy-β-methyl butyrate (HMB) supplementation. The FTIR imaging analysis of histological AC sections originating from 35-day old male piglets showed the change in the collagen and proteoglycan contents of the HMB-supplemented group compared to the control. The relative amount of collagen content in the superficial zone increased by more than 23% and in the middle zone by about 17%, while no changes in the deep zone were observed compared to the control group. Considering proteoglycans content, a significant increase was registered in the middle and deep zones, respectively; 62% and 52% compared to the control. AFM nanoindentation measurements collected from animals administered with HMB displayed an increase in AC tissue stiffness by detecting a higher value of Young’s modulus in all investigated AC zones. We demonstrated that principal component analysis and artificial neural networks could be trained with spectral information to distinguish AC histological sections and the group under study accurately. This work may support the use and effectiveness of FTIR imaging combined with multivariate analyses as a quantitative alternative to traditional collagenous tissue-related histology.

Seeing plants as never before

“…(1) non-targeted chemotyping, a medical diagnostics tool (Butler et al, 2019;Swietlicka et al, 2021) which requires no prior knowledge of the chemical basis underlying specific absorption bands but instead relies on the use of spectral features as part of the phenotype. This idea is ripe for further development through the deployment of machine learning, and could be the basis of an efficient diagnostic tool in the area of phytopathology; (2) AFM microscopy coupled to IR spectroscopy was developed to explore subsurface features in multilayer physical materials (Mester et al, 2020).…”

“…Progress, particularly in the medical field, has paved the way for the use of a number of exciting applications of vibrational imaging in the plant sciences. The following examples have been selected as being especially suitable for the characterization of plant subjects: (1) non‐targeted chemotyping, a medical diagnostics tool (Butler et al ., 2019; Świetlicka et al ., 2021) which requires no prior knowledge of the chemical basis underlying specific absorption bands but instead relies on the use of spectral features as part of the phenotype. This idea is ripe for further development through the deployment of machine learning, and could be the basis of an efficient diagnostic tool in the area of phytopathology; (2) AFM microscopy coupled to IR spectroscopy was developed to explore subsurface features in multilayer physical materials (Mester et al ., 2020).…”