Multivariate Calibration of Infrared Spectra for Quantitative Analysis Using Designed Experiments

$29.00 plus tax (Refund Policy)

Buy Article:

Abstract:

The principal component regression (PCR) and partial least-squares (PLS) methods are used to calibrate and validate models for quantitative prediction of the composition of mixtures from FT-IR spectra. An experimental system of two- and three-component mixtures of xylene isomers was sampled with the use of statistical experimental designs. For two-component mixtures, the prediction error of independent validation samples decreased with increasing numbers of design points in the calibration. Four design points were needed to achieve a prediction accuracy of 0.0013 weight fraction. For three-component mixtures, a Scheffé {3,3} simplex lattice design, which has ten design points, achieved an equivalent accuracy of 0.002 weight fraction. There was little difference in performance between PLS and PCR computations. The results demonstrate the application of statistical methodology to the calibration of infrared spectra and show the importance of including an adequate number of samples in the calibration. The F test on the residual spectrum is shown to be a valuable tool for the identification of spurious data.

Keywords: ATR spectroscopy; Computer applications; Experimental design; Infrared analytical methods; Spectroscopic techniques

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/0003702884428978

Affiliations: Bio-Rad Laboratories, Digilab Division, 237 Putnam Avenue, Cambridge, Massachusetts 02139

Publication date: July 1, 1988

More about this publication?
Related content

Tools

Favourites

Share Content

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more