PARTIAL LEAST-SQUARES MODELING OF NEAR-INFRARED REFLECTANCE DATA FOR NONINVASIVE IN-VIVO DETERMINATION OF DEEP-TISSUE PH

Noninvasive monitoring of deep-tissue pH has been demonstrated with th e use of near-infrared spectroscopic measurements and the partial leas t-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 ill vivo, along with reference pH v alues in the muscle measured by microelectrodes, The muscle pH was var ied by controlling the blood supply to the muscle, PLS analysis with c ross-validation techniques, along with several data preprocessing meth ods, was used to relate the tissue pH to spectra, When multi-subject P LS calibration models were used to predict a new independent subject, a subject-dependent offset was observed, Several strategies for minimi zing the subject-dependent offset were discussed. With a baseline subt raction 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 nea independent sub jects. Tissue chemistry during ischemia (blood Bow reduction) is diffe rent from the chemistry of reperfusion (blood how restoration), and it was found that separate calibration models permit more accurate predi ction of pH.