TY - JOUR
T1 - Efficient use of pure component and interferent spectra in multivariate calibration
AU - Sharma, Sandeep
AU - Goodarzi, Mohammad
AU - Wynants, Laure
AU - Ramon, Herman
AU - Saeys, Wouter
N1 - Funding Information:
The authors gratefully acknowledge I.W.T.-Flanders for the financial support through the GlucoSens project (SB-090053) and the Research Foundation-Flanders for funding Wouter Saeys as a Postdoctoral Fellow. The authors also acknowledge Dr. P. Dardenne, Dr. V. Baeten and Dr. J.-A. Fernandez-Piérna from CRA-W for their cooperation in measuring the aqueous glucose solutions and Bjorg Narum, Dr. Tormod Naes and Dr. Tomas Isaksson for providing the powder mixture data set.
PY - 2013/5/17
Y1 - 2013/5/17
N2 - Partial Least Squares (PLS) is by far the most popular regression method for building multivariate calibration models for spectroscopic data. However, the success of the conventional PLS approach depends on the availability of a 'representative data set' as the model needs to be trained for all expected variation at the prediction stage. When the concentration of the known interferents and their correlation with the analyte of interest change in a fashion which is not covered in the calibration set, the predictive performance of inverse calibration approaches such as conventional PLS can deteriorate. This underscores the need for calibration methods that are capable of building multivariate calibration models which can be robustified against the unexpected variation in the concentrations and the correlations of the known interferents in the test set. Several methods incorporating 'a priori' information such as pure component spectra of the analyte of interest and/or the known interferents have been proposed to build more robust calibration models. In the present study, four such calibration techniques have been benchmarked on two data sets with respect to their predictive ability and robustness: Net Analyte Preprocessing (NAP), Improved Direct Calibration (IDC), Science Based Calibration (SBC) and Augmented Classical Least Squares (ACLS) Calibration. For both data sets, the alternative calibration techniques were found to give good prediction performance even when the interferent structure in the test set was different from the one in the calibration set. The best results were obtained by the ACLS model incorporating both the pure component spectra of the analyte of interest and the interferents, resulting in a reduction of the RMSEP by a factor 3 compared to conventional PLS for the situation when the test set had a different interferent structure than the one in the calibration set.
AB - Partial Least Squares (PLS) is by far the most popular regression method for building multivariate calibration models for spectroscopic data. However, the success of the conventional PLS approach depends on the availability of a 'representative data set' as the model needs to be trained for all expected variation at the prediction stage. When the concentration of the known interferents and their correlation with the analyte of interest change in a fashion which is not covered in the calibration set, the predictive performance of inverse calibration approaches such as conventional PLS can deteriorate. This underscores the need for calibration methods that are capable of building multivariate calibration models which can be robustified against the unexpected variation in the concentrations and the correlations of the known interferents in the test set. Several methods incorporating 'a priori' information such as pure component spectra of the analyte of interest and/or the known interferents have been proposed to build more robust calibration models. In the present study, four such calibration techniques have been benchmarked on two data sets with respect to their predictive ability and robustness: Net Analyte Preprocessing (NAP), Improved Direct Calibration (IDC), Science Based Calibration (SBC) and Augmented Classical Least Squares (ACLS) Calibration. For both data sets, the alternative calibration techniques were found to give good prediction performance even when the interferent structure in the test set was different from the one in the calibration set. The best results were obtained by the ACLS model incorporating both the pure component spectra of the analyte of interest and the interferents, resulting in a reduction of the RMSEP by a factor 3 compared to conventional PLS for the situation when the test set had a different interferent structure than the one in the calibration set.
KW - Augmented Classical Least Squares
KW - Glucose
KW - Improved Direct Calibration
KW - Net Analyte Preprocessing
KW - Robust calibration
KW - Science Based Calibration
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U2 - 10.1016/j.aca.2013.03.045
DO - 10.1016/j.aca.2013.03.045
M3 - Article
C2 - 23639394
AN - SCOPUS:84876992632
SN - 0003-2670
VL - 778
SP - 15
EP - 23
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
ER -