The application of multivariate calibration models, specifically those using partial least squares (PLS) regression to relate near infrared (NIR) spectral data to analyte concentrations, relies upon accurate knowledge of the concentrations during model building. In a physiologic system, such as human skeletal muscle, these concentrations can be measured using invasive sensors which may have material properties that limit diffusion of analytes to the sensing chemistry, thus taking several minutes to fully respond to an analyte change which actually occurs in seconds. This results in a poor time correlation between reference measurements of analyte concentrations and spectral data, which in turn degrades the performance of the PLS model. We mathematically modeled the response of an invasive sensor measurement and used this response to develop a filter to time-match the raw NIR spectra before building the PLS model. PLS models for interstitial pH in exercising human flexor digitorum profundus muscle were developed with and without the time-matching filter. In a single exercising subject, root mean square error of prediction (RMSEP) = 0.05 pH units and r2 = 0.39 without filtering, but improved to RMSEP = 0.02 pH units with r2 = 0.91 after the time-matching filter was implemented. The time-matching filter was shown to be effective in improving model performance when spectral response is more rapid than the invasive sensor reference measurement.
Muscle pH is an important indicator of inadequate blood flow and available oxygen. Muscle pH can be used to triage and help treat trauma victims and indicate poor peripheral blood flow in diabetic patients. Muscle pH can also be used to indicate exercise intensity and fatigue. We have developed methods to non-invasively measure muscle pH using Near-Infrared Spectroscopy (NIRS) and Partial Least Squares (PLS) analysis. A multi-subject PLS model correlating near infrared tissue spectra, acquired from healthy subjects during repetitive hand-grip exercise, to invasive tissue pH measurements, has been developed and validated. Subject related variations in the spectral signal; impede the development of viable multi-subject model. Within-subject variations in tissue NIR spectra often result from uncontrolled motion or blood volume changes during exercise, while subject-to-subject variations arise from differences in skin pigmentation and the fat layer thickness. We have developed signal processing techniques to account for these mitigating factors. By incorporating this signal processing techniques with PLS calibration, we can generate a pH model that has a relative standard error of prediction of 1.7%
In order to measure muscle physiological parameters such as pH and oxygen partial pressure (PO2) by continuous wave (CW) diffuse reflectance near-infrared spectroscopy (NIRS), light must penetrate through skin and subcutaneous fat layers overlying muscle. In this study, the effect of skin and subcutaneous fat layer and on the spatial sensitivity profile of CW diffuse reflectance near-infrared spectra is investigated through Monte Carlo simulations. The simulation model uses a semi-infinite medium consisting of skin, fat and muscle. The optical properties of each layer are taken from the reported optical data at 750 nm. The skin color is either Caucasian or Negroid and the fat thickness is varied from 0 ~ 20 mm. The spatial sensitivity profile, penetration depth, and sensitivity ratio as functions of optical fiber source-detector separation (SD, 2.5 mm, 5.0 mm, 10.0 mm, 20.0 mm, 30.0 mm and 40.0 mm), skin color and fat thicknesses are predicted by the simulations. It is shown that skin color only slightly influenced the spatial sensitivity profile, while the presence of the fat layer greatly decreased the detector sensitivity. It is also shown that probes with longer SD separations can detect light from deeper inside the medium. The simulation results are used to design a fiber optic probe which ensures that enough light is propagated inside the muscle in NIRS measurement on a leg with a fat layer of normal thickness.