Method for detecting subpopulations in spectral analysis
First Claim
1. A method for detecting nonhomogeneous samples comprising the steps of:
- assembling a first plurality of known samples to provide a training set of known samples;
sequentially placing each of the training set of known samples in a spectroscopic apparatus for measuring a property of the sample as a function of an observable parameter;
measuring the property of the known samples to obtain a training set of spectra at a second plurality of values of the observable parameters;
storing said training set of spectra;
forming a first bootstrap distribution from the training set;
measuring the property of a third plurality of test samples at the second plurality of values of the observable parameters to obtain a set of test spectra;
storing said set of test spectra;
forming a second bootstrap distribution from the test set;
forming first and second univariate distributions from the first and second bootstrap distributions;
calculating a quantile-quantile relationship of the training and test sets; and
determining whether the test set was drawn from a population substantially identical to the population from which the training set was drawn based on the quantile-quantile relationship.
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Abstract
A method for using spectral analysis to detect subpopulations is disclosed. According to this method, a training set of spectra of a first plurality of samples is obtained and a bootstrap distribution is formed. A test set of spectra of a second plurality of samples is then obtained and a second bootstrap distribution is formed. First and second univariate distributions are then formed from the bootstrap distributions. A quantile-quantile relationship of the training and test sets is then developed and a determination of whether the test set and training set are substantially identical is made. The plurality of test samples are thus used to calculate probability density contours inside a training-set spectral cluster and detect perturbations of those contours using a bootstrap procedure. False samples are detected as subclusters well inside the training set, and trace analyses using a very small number of wavelengths are facilitated.
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Citations
13 Claims
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1. A method for detecting nonhomogeneous samples comprising the steps of:
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assembling a first plurality of known samples to provide a training set of known samples; sequentially placing each of the training set of known samples in a spectroscopic apparatus for measuring a property of the sample as a function of an observable parameter; measuring the property of the known samples to obtain a training set of spectra at a second plurality of values of the observable parameters; storing said training set of spectra; forming a first bootstrap distribution from the training set; measuring the property of a third plurality of test samples at the second plurality of values of the observable parameters to obtain a set of test spectra; storing said set of test spectra; forming a second bootstrap distribution from the test set; forming first and second univariate distributions from the first and second bootstrap distributions; calculating a quantile-quantile relationship of the training and test sets; and determining whether the test set was drawn from a population substantially identical to the population from which the training set was drawn based on the quantile-quantile relationship. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for detecting nonhomogeneous samples of pharmaceutical capsules comprising the steps of:
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assembling a first plurality of known samples from a population of pharmaceutical capsules to provide a training set of known samples; sequentially placing each of the training set of known samples in a spectroscopic apparatus for measuring a property of the sample as a function of an observable parameter; measuring the property of the known samples to obtain a training set of spectra at a second plurality of values of the observable parameters; storing the training set of spectra; forming a first bootstrap distribution from the training set; obtaining a test set of spectra at the second plurality of values of the observable parameters from a third plurality of samples of pharmaceutical capsules; forming a second bootstrap distribution from the test set; forming first and second univariate distributions from the first and second bootstrap distributions; calculating a quantile-quantile relationship of the training and test sets; and determining whether the test set was drawn from a population substantially identical to the population from which the training set was drawn based on the quantile-quantile relationship. - View Dependent Claims (9, 10, 11, 12, 13)
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Specification