Automatic process for sample selection during multivariate calibration
First Claim
1. A method for optimizing a calibration set 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 subsets of samples from the calibration set according to a cost function; and
determining a subset of samples having an optimal measure of fitness by creating new groupings of samples until a subset results that provides a target performance, wherein spurious variations are not correlated to a net analyte signal.
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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.
276 Citations
63 Claims
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1. A method for optimizing a calibration set comprising the steps of:
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providing a calibration set of spectral samples and associated reference values;
assigning a measure of fitness for calibration to individual samples or subsets of samples from the calibration set according to a cost function; and
determining a subset of samples having an optimal measure of fitness by creating new groupings of samples until a subset results that provides a target performance, wherein spurious variations are not correlated to a net analyte signal. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63)
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Specification