Journal article
Calibration standardization algorithm for partial least-squares regression: Application to the determination of physiological levels of glucose by near-infrared spectroscopy
Analytical chemistry (Washington), Vol.74(16), pp.4097-4108
2002
DOI: 10.1021/ac020023r
PMID: 12199580
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).
Details
- Title: Subtitle
- Calibration standardization algorithm for partial least-squares regression: Application to the determination of physiological levels of glucose by near-infrared spectroscopy
- Creators
- LIN Zhang - Center for Intelligent Chemical Instrumentation, Department of Chemistry and Biochemistry, Clippinger Laboratories, Ohio University, Athens, Ohio 45701, United StatesGary W Small - Center for Intelligent Chemical Instrumentation, Department of Chemistry and Biochemistry, Clippinger Laboratories, Ohio University, Athens, Ohio 45701, United StatesMark A Arnold - Optical Science and Technology Center and Department of Chemistry, University of Iowa, Iowa City, Iowa 52242, United States
- Resource Type
- Journal article
- Publication Details
- Analytical chemistry (Washington), Vol.74(16), pp.4097-4108
- Publisher
- American Chemical Society
- DOI
- 10.1021/ac020023r
- PMID
- 12199580
- ISSN
- 0003-2700
- eISSN
- 1520-6882
- Language
- English
- Date published
- 2002
- Academic Unit
- Center for Biocatalysis and Bioprocessing; Fraternal Order of Eagles Diabetes Research Center; Chemistry
- Record Identifier
- 9984216608102771
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