Near Infrared Spectroscopy Assessment of Achilles Tendon Biomechanical Properties

Abstract

The Achilles tendon is the strongest and largest tendon in the human body, playing a critical role in lower limb movement by transmitting forces from the calf muscles to the heel. Damage or degeneration in this tendon can affect movement and often requires early detection and monitoring. This study investigated the potential of near-infrared spectroscopy (NIRS) as a non-destructive method for assessing the biomechanical properties of the Achilles tendon. The main aim was to determine whether NIRS could detect tendon changes under mechanical loading and predict key biomechanical parameters: instantaneous modulus, dynamic modulus, and equilibrium modulus. Measurements were taken on five porcine limbs, both through intact skin and directly on the exposed tendon, under relaxed and stressed conditions. Mechanical testing was carried out after each optical measurement.

Spectral data were analysed using partial least squares regression (PLS-R) and partial least squares discriminant analysis (PLS-DA). The best prediction was seen for instantaneous modulus. In the through-skin condition, R² values were 0.888 (relaxed) and 0.891 (stressed), with RMSECV values of 5.78 MPa and 5.69 MPa. For direct tendon measurements, R² values were 0.898 (relaxed) and 0.769 (stressed), with RMSECV values of 5.50 MPa and 8.28 MPa. These results were also reflected in the PLS-R plots, which showed close agreement between predicted and measured values. Dynamic modulus showed moderate prediction, with R² values ranging from 0.522 to 0.752 and RMSECV from 10.16 MPa to 13.98 MPa. Equilibrium modulus had the lowest prediction accuracy, with R² values between 0.407 and 0.554 and RMSECV from 3.5 MPa to 4.1 MPa.

PLS-DA models classified relaxed and stressed tendon states with accuracies of 81% (direct) and 74% (through-skin). Although the skin and subcutaneous tissue reduced signal clarity, the models still detected changes between the different loading states.

Our results indicate that NIRS has the potential to estimate tendon properties under different loading conditions without invasive access. However, further studies with larger sample sizes and refined modelling approaches are needed to improve reliability and explore clinical applicability.