Automatic process for sample selection during multivariate calibration

• US 20030109998A1
• Filed: 08/02/2002
• Published: 06/12/2003
• Est. Priority Date: 08/03/2001
• Status: Active Grant

• Current Assignee: GLT Acquisition Corporation
• Original Assignee: Sensys Medical, Inc. (Sensys Medical Limited)
• Inventors: Ruchti, Timothy L., Blank, Thomas B., Lorenz, Alexander D.
• Granted Patent: US 6,876,931 B2
• Time in Patent Office: Days
• US Class Current: 702/85
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: G01N 21/274 Calibration, base line adjustment, drift correction
• CPC Class Codes: G01N 21/359 using near infrared light

Abstract

A process for enhancing a multivariate calibration through optimization of a calibration data set operates on a large calibration set of samples that includes measurements and associated reference values to automatically select an optimal sub-set of samples that enables calculation of an optimized calibration model. The process is automatic and bases sample selection on two basic criteria: enhancement of correlation between a partner variable extracted from the independent variable and the dependent variable and reduction of correlation between the dependent variable and interference. The method includes two fundamental steps: evaluation, assigning a measurement of calibration suitability to a subset of data; and optimization, selecting an optimal subset of data as directed by the measurement of suitability. The process is particularly applied in enhancing and automating the calibration process for non-invasive measurement glucose measurement but can be applied in any system involving the calculation of multivariate models from empirical data sets.

Claims

1. A method for optimizing a calibration model comprising the steps of:

providing a calibration set of spectral samples and associated reference values;

assigning a measure of fitness for calibration to individual samples or sub-sets of sample;

determining a subset of samples having an optimal measure of fitness by iteratively creating new groupings of samples until a sub-set results that provides an optimal level of performance;

wherein

spurious correlations are minimized and accessibility of a net analyte signal is maximized.