Abstract: A system for providing and using a gray optical standard is disclosed, the standard generally comprising a housing having a cavity and an at least partially transparent portion adjacent to the cavity for receiving the radiation emitted radiation by an optical instrument and a particulate material disposed in the cavity. The particulate material is a mixture of highly absorptive and highly reflective particles having a diameter of about 20 microns or less. In some embodiments, the at least partially transparent portion of the housing is modified to be highly diffusely scattering in the wavelength region of interest.

Abstract: A system for predicting blood glucose values in a patient includes a remote wireless non-invasive spectral device, the remote wireless non-invasive spectral device generating a spectral scan of a body part of the patient. Also included are a remote invasive device and a central processing device. The remote invasive device generates a constituent value for the patient, while the central processing device predicts a blood glucose value for the patient based upon the spectral scan and the constituent value.

Abstract: An automated method for modeling spectral data includes accessing a set of spectral data, corresponding to each of a plurality of samples, each set having associated therewith at least one independently measured constituent value (201).Data transforms are applied to the set of spectral data to generate, for each sample, a set of transformed and untransformed spectral data, which with its associated constituent values, is divided into a calibration sub-set (231) and a validation sub-set (232). One or more of a partial least squares, principal component regression, neural net, or a multiple linear regression analysis is applied to the calibration data sub-sets to obtain corresponding modeling equations for predicting the target substance amount in a sample. The modeling equation with the best correlation between the spectral data in the validation sub-set and the corresponding constituent values in the validation sub-set is identified, preferably as a function of the SEE and SEP.

Abstract: An individualized modeling equation for predicting a patient's blood glucose values is generated as a function of non-invasive spectral scans of a body part and an analysis of blood samples from the patient, and is stored on a central computer. The central computer predicts a blood glucose value for the patient as a function of the individualized modeling equation and a non-invasive spectral scan generated by a remote spectral device. If the spectral scan falls within the range of the modeling equation, the predicted blood glucose level is output to the patient. If the spectral scan falls outside the range of the modeling equation, regeneration of the model is required, and the patient takes a number of noninvasive scans and an invasive blood glucose level determination. The computer regenerates the individualized modeling equation as a function of the set of spectral scans and corresponding blood glucose values.

Abstract: An automated method for modeling spectral data is provided, wherein the spectral data generated by one of diffuse reflectance, clear transmission, or diffuse transmission. The method includes accessing a set of spectral data, the set of spectral data including, corresponding spectral data for each of a plurality of samples, the spectral data for each of the plurality of samples having associated therewith at least one constituent value, the at least one constituent value being a reference value for a target substance in the sample which is measured by a independent measurement technique. A plurality of data transforms are applied to the set of spectral data to generate, for each sample, a set of transformed and untransformed spectral data.

Abstract: An individualized modeling equation for predicting a patient's blood glucose values is generated as a function of non-invasive spectral scans of a body part and an analysis of blood samples from the patient, and is stored on a central computer. The central computer predicts a blood glucose value for the patient as a function of the individualized modeling equation and a non-invasive spectral scan generated by a remote spectral device. If the spectral scan falls within the range of the modeling equation, the predicted blood glucose level is output to the patient. If the spectral scan falls outside the range of the modeling equation, regeneration of the model is required, and the patient takes a number of noninvasive scans and an invasive blood glucose level determination. The computer regenerates the individualized modeling equation as a function of the set of spectral scans and corresponding blood glucose values.

Abstract: A dual beam acousto-optic tunable spectrometer utilizes an optics system to isolate the two radiation beams tuned by an acousto-optic filter, one of which is used to analyze a sample, the other of which is used as a reference. The tuning of the filter and the analysis of the signals is conducted by a microprocessor, which corrects for the many possible sources of noise. A new fluid sample cell utilizing glass balls to collimate radiation incident on the sample and focus radiation transmitted from the sample, is also disclosed.