TY - JOUR
T1 - Maintenance of a calibration model for near infrared spectrometry by a combined principal component analysis-partial least squares approach
AU - Setarehdan, S.
AU - Soraghan, J.J.
AU - Littlejohn, D.
AU - Sadler, D.
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
KW - principal component analysis
KW - partial least squares
KW - calibration
KW - spectrometry
UR - http://dx.doi.org/10.1016/S0003-2670(01)01446-5
U2 - 10.1016/S0003-2670(01)01446-5
DO - 10.1016/S0003-2670(01)01446-5
M3 - Article
SN - 0003-2670
VL - 452
SP - 35
EP - 45
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
IS - 1
ER -