Picture of UK Houses of Parliament

Leading national thinking on politics, government & public policy through Open Access research

Strathprints makes available scholarly Open Access content by researchers in the School of Government & Public Policy, based within the Faculty of Humanities & Social Sciences.

Research here is 1st in Scotland for research intensity and spans a wide range of domains. The Department of Politics demonstrates expertise in understanding parties, elections and public opinion, with additional emphases on political economy, institutions and international relations. This international angle is reflected in the European Policies Research Centre (EPRC) which conducts comparative research on public policy. Meanwhile, the Centre for Energy Policy provides independent expertise on energy, working across multidisciplinary groups to shape policy for a low carbon economy.

Explore the Open Access research of the School of Government & Public Policy. Or explore all of Strathclyde's Open Access research...

Maintenance of a calibration model for near infrared spectrometry by a combined principal component analysis-partial least squares approach

Setarehdan, S. and Soraghan, J.J. and Littlejohn, D. and Sadler, D. (2002) Maintenance of a calibration model for near infrared spectrometry by a combined principal component analysis-partial least squares approach. Analytica Chimica Acta, 452 (1). pp. 35-45. ISSN 0003-2670

Full text not available in this repository.Request a copy from the Strathclyde author


A novel strategy for building and maintaining calibration models has been developed for use when the future boundaries of the sample set are unknown or likely to change. Such a strategy could have an impact on the economics and time required to obtain and maintain a calibration model for routine analysis. The strategy is based on both principal component analysis (PCA) and partial least squares (PLS) multivariate techniques. The principal action of the strategy is to define how ''similar'' a new sample is to the samples currently defining the calibration dataset. This step is performed by residuals analysis, following PCA. If the new sample is considered to have a spectrum ''similar'' to previously available spectra, then the model is assumed able to predict the analyte concentration. Conversely, if the new sample is considered ''dissimilar'', then there is new information in this sample, which is unknown to the calibration model and the new sample is added automatically to the calibration set in order to improve the model. The strategy has been applied to a real industrial dataset provided by BP Amoco Chemicals. The data consists of spectra of 102 sequential samples of a raw material. The strategy produced an accurate calibration model for both target components starting with only the first four samples, and required a further 17 reference measurements to maintain the model for the whole sampling sequence, which was over a 1-year period.