Multivariate calibration standardization across instruments for the determination of glucose by Fourier transform near-infrared spectrometry.
The transfer of multivariate calibration models is investigated between a primary (A) and two secondary Fourier transform near-infrared (near-IR) spectrometers (B, C). The application studied in this work is the use of bands in the near-IR combination region of 5000-4000 cm(-)(1) to determine physiological levels of glucose in a buffered aqueous matrix containing varying levels of alanine, ascorbate, lactate, triacetin, and urea. The three spectrometers are used to measure 80 samples produced through a randomized experimental design that minimizes correlations between the component concentrations and between the concentrations of glucose and water. Direct standardization (DS), piecewise direct standardization (PDS), and guided model reoptimization (GMR) are evaluated for use in transferring partial least-squares calibration models developed with the spectra of 64 samples from the primary instrument to the prediction of glucose concentrations in 16 prediction samples measured with each secondary spectrometer. The three algorithms are evaluated as a function of the number of standardization samples used in transferring the calibration models. Performance criteria for judging the success of the calibration transfer are established as the standard error of prediction (SEP) for internal calibration models built with the spectra of the 64 calibration samples collected with each secondary spectrometer. These SEP values are 1.51 and 1.14 mM for spectrometers B and C, respectively. When calibration standardization is applied, the GMR algorithm is observed to outperform DS and PDS. With spectrometer C, the calibration transfer is highly successful, producing an SEP value of 1.07 mM. However, an SEP of 2.96 mM indicates unsuccessful calibration standardization with spectrometer B. This failure is attributed to differences in the variance structure of the spectra collected with spectrometers A and B. Diagnostic procedures are presented for use with the GMR algorithm that forecasts the successful calibration transfer with spectrometer C and the unsatisfactory results with spectrometer B.
Zhang
Lin
L
Center for Intelligent Chemical Instrumentation, Department of Chemistry and Biochemistry, Clippinger Laboratories, Ohio University, Athens, Ohio 45701, USA.
Small
Gary W
GW
Arnold
Mark A
MA
D000465:Algorithms; D001205:Ascorbic Acid / Q000032:analysis; D002138:Calibration / Q000592:standards; D003198:Computer Simulation; D001330:Electronic Data Processing; D005947:Glucose / Q000032:analysis*; D006801:Humans; D019344:Lactic Acid / Q000032:analysis; D015233:Models, Statistical; D015999:Multivariate Analysis; D012015:Reference Standards; D017550:Spectroscopy, Fourier Transform Infrared / Q000295:instrumentation* / Q000379:methods; D019265:Spectroscopy, Near-Infrared / Q000295:instrumentation / Q000379:methods; D014215:Triacetin / Q000032:analysis; D014508:Urea / Q000032:analysis
D019344:Lactic Acid; D014508:Urea; D005947:Glucose; D001205:Ascorbic Acid; D014215:Triacetin