Partial Least-Squares Modeling of Near-Infrared Reflectance Data for Noninvasive in Vivo Determination of Deep-Tissue pH

Abstract

Noninvasive monitoring of deep-tissue pH has been demonstrated with the use of near-infrared spectroscopic measurements and the partial least-squares (PLS) multivariate calibration technique. The near-infrared reflectance spectra (700 to 1100 nm) of the teres major muscle in five New Zealand rabbits were obtained in vivo, along with reference pH values in the muscle measured by microelectrodes. The muscle pH was varied by controlling the blood supply to the muscle. PLS analysis with cross-validation techniques, along with several data preprocessing methods, was used to relate the tissue pH to spectra. When multi-subject PLS calibration models were used to predict a new independent subject, a subject-dependent offset was observed. Several strategies for minimizing the subject-dependent offset were discussed. With a baseline subtraction procedure, the subject-dependent offset was minimized to less than 0.1 pH units while the average standard error of prediction (SEP) was close to 0.05 pH units. This result suggests that it is possible to build a single robust calibration model for all new independent subjects. Tissue chemistry during ischemia (blood flow reduction) is different from the chemistry of reperfusion (blood flow restoration), and it was found that separate calibration models permit more accurate prediction of pH.