Framework for explicit drift correction in multivariate calibration models

Journal of Chemometrics

Volumn 24, Issue 7-8, 2010, Pages 534-543

  Gujral, Paman ^a   Amrhein, Michael ^a   Wise, Barry M ^b   Bonvin, Dominique ^a  

^a EPFL   (Switzerland)

^b EIGENVECTOR RESEARCH INC   (United States)

Author keywords

Calibration; Drift; Orthogonal projection; Shrinkage; Systematic disturbances

Indexed keywords

LEAST SQUARES APPROXIMATIONS; PRINCIPAL COMPONENT ANALYSIS; SHRINKAGE;

CALIBRATION MODEL; DRIFT; DRIFT CORRECTION; DRIFT-CORRECTION METHODS; LATENT VARIABLE; MULTIVARIATE CALIBRATION; ORTHOGONAL PROJECTION; PARTIAL LEAST SQUARE REGRESSION; PRINCIPAL COMPONENTS; SYSTEMATIC DISTURBANCES;

CALIBRATION;

EID: 77957075239     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1291     Document Type: Article

References (29)

1. Kadlec P, Gabrys B, Strandt S. Data-driven soft sensors in the process industry. Comput. Chem. Eng. 2009; 33(4): 795-814.
2. Næs T, Isaksson T, Fearn T, Davies T. A user-friendly guide to multivariate calibration and classification. NIR Publications, Chichester, U.K., 2002.
3. Estienne F, Despagne F, Walczak B, de Noord OE, Massart DL. A comparison of multivariate calibration techniques applied to experimental NIR data sets: part III: robustness against instrumental perturbation conditions. Chemom. Intell. Lab. Syst. 2004; 73(2): 207-218.
4. Larrechi MS, Callao MP. Strategy for introducing NIR spectroscopy and multivariate calibration techniques in industry. Trends Anal. Chem. 2003; 22(9): 634-640.
5. Saiz-Abajo MJ, Mevik BH, Segtnan VH, Næs T. Ensemble methods and data augmentation by noise addition applied to the analysis of spectroscopic data. Anal. Chim. Acta 2005; 533(2): 147-159.
6. Wise BM, Gallagher NB, Butler SW, White D, Barna GG. Development and benchmarking of multivariate statistical process control tools for a semiconductor etch process: improving robustness through model updating. IFAC ADCHEM'97, 1997, Banff, Canada; 78-83.
7. Thijssen PC, Wolfrum SM, Kateman G, Smit HC. A Kalman filter for calibration, evaluation of unknown samples and quality control in drifting systems: part 1. Theory and simulations. Anal. Chim. Acta 1984; 156: 87-101.
8. Brown SD. The Kalman filter in analytical chemistry. Anal. Chim. Acta 1986; 181: 1-26.
9. Lu J, Yao K, Gao F. Process similarity and developing new process models through migration. AIChE J. 2009; 55(9): 2318-2328.
10. Gujral P, Amrhein M, Bonvin D. Drift correction in multivariate calibration models using on-line referencemeasurements. Anal. Chim. Acta 2009; 642: 27-32.
11. Haaland DM,Melgaard DK. New prediction-augmented classical leastsquares (PACLS) methods: application to unmodeled interferents. Appl. Spectrosc. 2000; 54(9): 1303-1312.
12. Haaland DM, Melgaard DK. New classical least-squares/partial leastsquares hybrid algorithm for spectral analyses. Appl. Spectrosc. 2001; 55(1): 1-8.
13. Artursson T, Eklöv T, LundströmI,M °artensson P, Sjöström M, Holmberg M. Drift correction for gas sensors using multivariate methods. J. Chemom. 2000; 14: 711-723.
14. Hansen PW. Pre-processing method minimizing the need for reference analyses. J. Chemom. 2001; 15: 123-131.
15. Martens H, Høy M, Wise BM, Bro R, Brockhoff BP. Pre-whitening of data by covariance-weighted pre-processing. J. Chemom. 2003; 17: 153-165.
16. Wise BM, Gallagher NB, Bro R, Shaver JM, Windig W, Koch RS. PLS Toolbox for use with MATLAB, Version 5.0, Software, Eigenvector Research, Inc., Wenatchee, USA, November 2008.
17. Roger JM, Chauchard F, Bellon-Maurel V. EPO-PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits. Chemom. Intell. Lab. Syst. 2003; 66: 191-204.
18. Andrew A, Fearn T. Transfer by orthogonal projection: making near-infrared calibrations robust to between-instrument variation. Chemom. Intell. Lab. Syst. 2004; 72: 51-56.
19. Zeaiter M, Roger JM, Bellon-Maurel V. Dynamic orthogonal projection. A new method to maintain the on-line robustness of multivariate calibrations. Application to NIR-based monitoring of wine fermentations. Chemom. Intell. Lab. Syst. 2006; 80: 227-235.
20. Dabros M, Amrhein M, Gujral P, Stockar U. On-line recalibration of spectral measurements using metabolite injections and dynamic orthogonal projection. Appl. Spectrosc. 2007; 61: 507-513.
21. Segtnan VH, Mevik BH, Isaksson T, Næs T. Low-cost approaches to robust temperature compensation in near-infrared calibration and prediction situations. Appl. Spectrosc. 2005; 59: 816-825.
22. Zhu Y, Fearn T, Samuel D, Dhar A, Hameed O, Bown SG, Lovat LB. Error removal by orthogonal subtraction (EROS): a customised pretreatment for spectroscopic data. J. Chemom. 2008; 22: 130-134.
23. Thissen U, Pepers M, Üstün B, Melssen WJ, Buydens LMC. Comparing support vector machines to PLS for spectral regression applications. Chemom. Intell. Lab. Syst. 2004; 73: 169-179.
24. Chen T, Morris J, Martin E. Gaussian process regression for multivariate spectroscopic calibration. Chemom. Intell. Lab. Syst. 2007; 87: 59-71.
25. Jianguo S. Statistical analysis of NIR data: Data pretreatment. J. Chemom. 1997; 11: 525-532.
26. Lorber A, Faber K, Kowalski BR. Net analyte signal calculation in multivariate calibration. Anal. Chem. 1997; 69: 1620-1626.
27. Wülfert F, Kok WT, Smilde AK. Influence of temperature on vibrational spectra and consequences for the predictive ability of multivariate models. Anal. Chem. 1998; 70: 1761-1767.
28. Wentzell PD, Andrews DT, Walsh JM, Cooley JM, Spencer P. Estimation of hydrocarbon types in light gas oils and diesel fuels by ultraviolet absorption spectroscopy and multivariate calibration. Can. J. Chem. 1999; 77: 391-400.
29. Zhao C, Gao F, Yao Y, Wang F. A robust calibration modeling strategy for analysis of interference-subject spectral data (2010), AIChE J., Vol. 56, 196-206.