Pure component selectivity analysis of multivariate calibration models from near-infrared spectra

Mark A Arnold; Gary W Small; Dong Xiang; Jiang Qui; David W Murhammer

doi:10.1021/ac035516q

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Pure component selectivity analysis of multivariate calibration models from near-infrared spectra

Journal article

Peer reviewed

Pure component selectivity analysis of multivariate calibration models from near-infrared spectra

Mark A Arnold, Gary W Small, Dong Xiang, Jiang Qui and David W Murhammer

Analytical chemistry (Washington), Vol.76(9), pp.2583-2590

05/01/2004

DOI: 10.1021/ac035516q

PMID: 15117201

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Abstract

A novel procedure is proposed as a method to characterize the chemical basis of selectivity for multivariate calibration models. This procedure involves submitting pure component spectra of both the target analyte and suspected interferences to the calibration model in question. The resulting model output is analyzed and interpreted in terms of the relative contribution of each component to the predicted analyte concentration. The utility of this method is illustrated by an analysis of calibration models for glucose, sucrose, and maltose. Near-infrared spectra are collected over the 5000-4000-cm(-)(1) spectral range for a set of ternary mixtures of these sugars. Partial least-squares (PLS) calibration models are generated for each component, and these models provide selective responses for the targeted analytes with standard errors of prediction ranging from 0.2 to 0.7 mM over the concentration range of 0.5-50 mM. The concept of the proposed pure component selectivity analysis is illustrated with these models. Results indicate that the net analyte signal is solely responsible for the selectivity of each individual model. Despite strong spectral overlap for these simple carbohydrates, calibration models based on the PLS algorithm provide sufficient selectivity to distinguish these commonly used sugars. The proposed procedure demonstrates conclusively that no component of the sucrose or maltose spectrum contributes to the selective measurement of glucose. Analogous conclusions are possible for the sucrose and maltose calibration models.

Multivariate Analysis

Sucrose - chemistry

Reproducibility of Results

Spectroscopy, Near-Infrared - statistics & numerical data

Maltose - analysis

Linear Models

Calibration

Models, Statistical

Spectroscopy, Near-Infrared - methods

Maltose - chemistry

Carbohydrates - chemistry

Sensitivity and Specificity

Glucose - chemistry

Least-Squares Analysis

Sucrose - analysis

Carbohydrates - analysis

Glucose - analysis

Details

Title: Subtitle: Pure component selectivity analysis of multivariate calibration models from near-infrared spectra
Creators: Mark A Arnold - Department of Chemistry, University of Iowa, Iowa City, Iowa 52242, USA. mark-arnold@uiowa.edu
Gary W Small
Dong Xiang
Jiang Qui
David W Murhammer
Resource Type: Journal article
Publication Details: Analytical chemistry (Washington), Vol.76(9), pp.2583-2590
Publisher: United States
DOI: 10.1021/ac035516q
PMID: 15117201
ISSN: 0003-2700
eISSN: 1520-6882
Grant note: DK-60657 / NIDDK NIH HHS
Language: English
Date published: 05/01/2004
Academic Unit: Center for Biocatalysis and Bioprocessing; Chemistry; Chemical and Biochemical Engineering
Record Identifier: 9984003944902771

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