Ten healthy human volunteers were subjected to progressive lower body negative pressure (LBNP) to the onset of cardiovascular collapse to compare the response of noninvasively determined skin and fat corrected deep muscle oxygen saturation (SmO2) and pH to standard hemodynamic parameters for early detection of imminent hemodynamic instability. Muscle SmO2 and pH were determined with a novel near infrared spectroscopic (NIRS) technique. Heart rate (HR) was measured continuously via ECG, and arterial blood pressure (BP) and stroke volume (SV) were obtained noninvasively via Finometer and impedance cardiography on a beat-to-beat basis. SmO2 and SV were significantly decreased during the first LBNP level (-15 mmHg), whereas HR and BP were late indicators of impending cardiovascular collapse. SmO2 declined in parallel with SV and inversely with total peripheral resistance, suggesting, in this model, that SmO2 is an early indicator of a reduction in oxygen delivery through vasoconstriction. Muscle pH decreased later, suggesting an imbalance between delivery and demand. Spectroscopic determination of SmO2 is noninvasive and continuous, providing an early indication of impending cardiovascular collapse resulting from progressive reduction in central blood volume.

Spectroscopic assessment of forearm muscle PO2 and SmO2 provides noninvasive and continuous measures that are early indicators of impending cardiovascular collapse resulting from progressive reductions in central blood volume.

The influence of fat thickness on the diffuse reflectance spectra of muscle in the near infrared (NIR) region is studied by Monte Carlo simulations of a two-layer structure and with phantom experiments. A polynomial relationship was established between the fat thickness and the detected diffuse reflectance. The influence of a range of optical coefficients (absorption and reduced scattering) for fat and muscle over the known range of human physiological values was also investigated. Subject-to-subject variation in the fat optical coefficients and thickness can be ignored if the fat thickness is less than 5 mm. A method was proposed to correct the fat thickness influence.

A method to non-invasively and quantitatively measure muscle oxygen saturation (SmO(2)) using broadband continuous-wave diffuse reflectance near infrared (NIR) spectroscopy is presented. The method obtained SmO(2) by first correcting NIR spectra for absorption and scattering of skin pigment and fat, then fitting to a Taylor expansion attenuation model. A non-linear least squares optimization algorithm with set boundary constraints on the fitting parameters was used to fit the model to the acquired spectra. A data preprocessing/optimization scheme for accurately determining the initial values needed for the optimization was also employed. The method was evaluated on simulated muscle spectra with 4 different scattering properties, as well as on in vivo forearm spectra from 5 healthy volunteer subjects during arterial occlusion. Measurement repeatability was assessed on 24 healthy volunteers with 5 repeated measurements, each separated by at least 48 hours.

A demonstration of multivariate optical computing is presented using binary dye mixtures consisting of Bismarck Brown and Crystal Violet. Bismarck Brown was treated as the analyte, while Crystal Violet was treated as a random interfering species. First, a multilayer multivariate optical element (MOE) for the determination of Bismarck Brown was designed using a novel nonlinear optimization algorithm. Next, the MOE was fabricated by depositing alternating layers of two metal oxide films (Nb2O5 and SiO2) on a BK-7 glass substrate via reactive magnetron sputtering. Finally, the MOE was tested on 39 binary dye mixtures using a simple T-format prototype instrument constructed for this purpose. For each sample, measurements of the difference between transmittance through the MOE, and the reflectance from the MOE were made. By setting aside some of the samples for instrument calibration and then using the calibration model to predict the remaining samples, a standard error of prediction of 0.69 μM was obtained for Bismarck Brown using a linear regression model.

A new algorithm for the design of optical computing filters for chemical analysis, otherwise known as multivariate optical elements (MOEs), is described. The approach is based on the nonlinear optimization of the MOE layer thicknesses to minimize the standard error in sample prediction for the chemical species of interest using a modified version of the Gauss–Newton nonlinear optimization algorithm. The design algorithm can either be initialized with random layer thicknesses or with layer thicknesses derived from spectral matching of a multivariate principal component regression (PCR) vector for the constituent of interest. The algorithm has been successfully tested by using it to design various MOEs for the determination of Bismarck Brown dye in a binary mixture of Crystal Violet and Bismarck Brown.

We have demonstrated simultaneous correction for the optical interference of skin and fat in tissue spectra by using a two-distance fiber-optic probe. We obtained the correction by orthogonalizing the spectra collected at a long source-detector distance (SD) to the spectra collected at a short SD and mapped to the long SD space. The method was validated in tissuelike three-layer phantoms as well as preliminarily in human tissue. After the correction, a partial-least-squares model of the phantoms showed enhanced prediction performance.

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