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Calibration standardization algorithm for partial least-squares regression: application to the determination of physiological levels of glucose by near-infrared spectroscopy.

Lin Zhang·Gary W Small·Mark A Arnold
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Abstract

Calibration standardization methodology for near-infrared (near-IR) spectroscopy is described for updating a partial least-squares calibration model to take into account changes in instrumental response. The guided model reoptimization (GMR) algorithm uses a transfer set of eight samples to characterize the new response and a database of previously acquired spectra used to develop the original calibration model. The samples in the transfer set need not have been measured under the old instrumental conditions, making the algorithm compatible with samples that change over time. The spectra comprising the transfer set are used to guide an iterative optimization procedure that (1) finds an optimal subset of samples from the original database to use in computing the updated model and (2) finds an optimal set of weights to apply to the spectral resolution elements in order to minimize the effects of instrumental changes on the computed model. The optimization relies on an alternating grid search and stepwise addition/deletion steps. The algorithm is evaluated through the use of combination region near-IR spectra to determine physiological levels of glucose in a synthetic biological matrix containing bovine serum albumin and triacetin in phosphate buffer. The ability to update a calibration to account for changes in the response of a Fourier transform spectrometer over four to six years is examined in this study. Separate spectral databases collected in 1994 and 1996 are used with a transfer set and separate test set of spectra collected in 2000. With the 1994 database, the standardization algorithm achieves a standard error of prediction (SEP) of 0.69 mM for the 2000 test set. This compares favorably to SEP values > 2 mM when the original 1994 calibration model is used without standardization. A similar improvement in the prediction performance of the 2000 test set is obtained after standardization with the 1996 database (SEP = 0.70 mM).

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AlgorithmsAnimalsBuffersCalibrationCattleGlucoseLeast-Squares AnalysisReference StandardsRegression AnalysisSerum Albumin, BovineSpectrophotometry, InfraredTriacetinJournal ArticleResearch Support, U.S. Gov't, P.H.S.

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Lin Zhang, Gary W Small, Mark A Arnold. 2002-08-15. Calibration standardization algorithm for partial least-squares regression: application to the determination of physiological levels of glucose by near-infrared spectroscopy.. https://doi.org/10.1021/ac020023r

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