A consensus modeling approach to update a spectroscopic calibration

Chemometrics and Intelligent Laboratory Systems

Volumn 120, Issue , 2013, Pages 142-153

  Shahbazikhah, Parviz ^a   Kalivas, John H ^a  

^a IDAHO STATE UNIVERSITY   (United States)

Author keywords

Calibration maintenance; Calibration transfer; Consensus modeling; Model updating; Multivariate calibration; Partial least squares; Tikhonov regularization

Indexed keywords

ARTICLE; CALIBRATION; CHEMICAL COMPOSITION; CONSENSUS; DYNAMICS; LABORATORY TEST; NEAR INFRARED SPECTROSCOPY; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIORITY JOURNAL; REFERENCE VALUE; ROOT MEAN SQUARE ERROR; SPECTROSCOPY; STANDARDIZATION; STATISTICAL MODEL; STATISTICAL PARAMETERS; TABLET FORMULATION; TEMPERATURE DEPENDENCE;

EID: 84871935564     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2012.06.006     Document Type: Article

References (75)

1. Fearn T.J. Standardisation and calibration transfer for near infrared instruments: a review. Journal of Near Infrared Spectroscopy 2001, 9:229-244.
2. Feudale R.N., Woody N.A., Tan H., Myles A.J., Brown S.D., Ferre J. Transfer of multivariate calibration models: a review. Chemometrics and Intelligent Laboratory Systems 2002, 64:181-192.
3. Cogdill R.P., Anderson C.A., Drennen J.K. Process analytical technology case study, part III: calibration monitoring and transfer. AAPS PharmSciTech 2005, 6:E284-E297.
4. Brown S.D. Transfer of multivariate calibration models. Comprehensive Chemometrics: Chemical and Biochemical Data Analysis 2009, 345-377. Elsevier, Amsterdam, The Netherlands. S.D. Brown, R. Tauler, B. Walczak (Eds.).
5. Maruo K., Oota T., Tsurugi M., Nakagawa T., Arimoto H., Hayakawa M., Tamura M., Ozaki Y., Yamada Y. Noninvasive near-infrared blood glucose monitoring using a calibration model built by a numerical simulation method: trial application to patients in an intensive care unit. Applied Spectroscopy 2006, 60:1423-1431.
6. Westerhaus M.O. Improving repeatability of NIR calibrations across instruments. Proceedings of the Third International Near Infrared Spectroscopy Conference 1991, 671-674. Agriculture Research Centre Publishing, Gembloux, Belgium. R. Briston, N. Bartiaux-Thill (Eds.).
7. Wang Y., Veltkamp D.J., Kowalski B.R. Multivariate instrument standardization. Analytical Chemistry 1991, 63:2750-2756.
8. Kramer K.E., Small G.W. Blank augmentation protocol for improving the robustness of multivariate calibrations. Applied Spectroscopy 2007, 61:497-506.
9. Sulub Y., Small G.W. Spectral simulation methodology for calibration transfer of near-infrared spectra. Applied Spectroscopy 2007, 61:406-413.
10. Condolfi A., Massart D.L. Model updating for the identification of NIR spectra from a pharmaceutical excipient. Applied Spectroscopy 2000, 54:48-53.
11. Wehlburg C.M., Haaland D.M., Melgaard D.K., Martin L.E. New hybrid algorithm for maintaining multivariate quantitative calibrations of a near-infrared spectrometer. Applied Spectroscopy 2002, 56:605-614.
12. Wold S. Exponentially weighted moving principal components analysis and projections to latent structures. Chemometrics and Intelligent Laboratory Systems 1984, 23:149-161.
13. Stork C.L., Kowalski B.R. Weighting schemes for updating regression models - a theoretical approach. Chemometrics and Intelligent Laboratory Systems 1999, 48:151-166.
14. Capron X., Walczak B., de Noord O.E., Massart D.L. Selection and weighting of samples in multivariate regression model updating. Chemometrics and Intelligent Laboratory Systems 2005, 76:205-214.
15. Helland K., Berntsen H.A., Borgen O.S., Martens H. Recursive algorithm for partial least squares. Chemometrics and Intelligent Laboratory Systems 1991, 14:129-137.
16. Woody N.A., Feudale R.N., Myles A.J., Brown S.D. Transfer of multivariate calibration between four near-infrared spectrometers using orthogonal signal correction. Analytical Chemistry 2004, 76:2595-2600.
17. Farrell J.A., Higgins K., Kalivas J.H. Updating a near-infrared multivariate calibration model formed with lab-prepared pharmaceutical tablet types to new tablet types in full production. Journal of Pharmaceutical and Biomedical Analysis 2012, 61:114-121.
18. Kalivas J.H., Siano G.S., Andries E., Goicoechea H.C. Calibration maintenance and transfer using Tikhonov regularization approaches. Applied Spectroscopy 2009, 63:800-809.
19. Kunz M.R., Kalivas J.H., Andries E. Model updating for spectral calibration maintenance and transfer using 1-norm variants of Tikhonov regularization. Analytical Chemistry 2010, 89:3642-3649.
20. Kunz M.R., Ottaway J., Kalivas J.H., Georgiou C.A., Mousdis G.A. Updating a synchronous fluorescence spectroscopic virgin olive oil adulteration calibration to a new geographical region. Journal of Agricultural and Food Chemistry 2011, 59:1051-1057.
21. 1) Tikhonov regularization variants for full wavelength or sparse spectral multivariate calibration models or maintenance. Journal of Chemometrics 2012, 26:218-230.
22. Kadlec P., Gabrys B. Local learning-based adaptive soft sensor for catalyst activation prediction. AICHE Journal 2011, 57:1288-1301.
23. Fujiwara K., Kano M., Hasebe S., Takinami A. Soft-sensor development using correlation-based just-in-time modeling. AICHE Journal 2009, 55:1754-1765.
24. J. Ottaway, J.A. Farrell, J.H. Kalivas, in preparation. Multivariate calibration or maintenance without reference samples.
25. Dyrby M., Engelsen S.B., Nørgaard L., Bruhn M., Lundsberg-Nielsen L. Chemometric quantitation of the active substance3 (containing CN) pharmaceutical tablet using near-infrared (NIR) transmittance and NIR FT-IR Raman spectra. Applied Spectroscopy 2002, 56:579-585.
26. accessed March 15, 2012. http://www.models.life.ku.dk/datasets.
27. Wiülfert F., Kok W. Th., Smilde A.K. Influence of temperature on vibration spectra and consequences for multivariate models. Analytical Chemistry 1998, 70:1761-1767.
28. Næs T., Isaksson T., Fern T., Davies T. A User Friendly Guide to Multivariate Calibration and Classification 2002, NIR Publications, Chichester.
29. Aster R.C., Borchers B., Thurber C.H. Parameter Estimation and Inverse Problems 2005, Elsevier, Amsterdam, The Netherlands.
30. Gorodnitsky I.F., Rao B.D. Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm. IEEE Transactions on Signal Processing 1997, 45:600-616.
31. Fornia M., Casolino C., Millan P. Iterative predictor weighting (IPW) PLS: a technique for the elimination of useless predictors in regression problems. Journal of Chemometrics 1999, 13:155-184.
32. Fornia M., Casolino C., Almansa E.M. The refinement of PLS models by Iterative weighting of predictor variables and objects. Chemometrics and Intelligent Laborary Systems 2003, 68:29-40.
33. Li H., Liang Y., Xu Q., Cao D. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta 2009, 648:77-84.
34. Kondylis A., Whittaker J. Adaptively preconditioned Krylov spaces to identify irrelevant predictors. Chemometrics and Intelligent Laboratory Systems 2010, 104:205-213.
35. Ottaway J., Kalivas J.H., Andries E. Spectral multivariate calibration with wavelength selection using variants of Tikhonov regularization. Applied Spectroscopy 2010, 64:1388-1395.
36. Wold S., Antti H., Lindgren F., Öhman J. Orthogonal signal correction of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems 1998, 44:175-185.
37. Lawson C.L., Hanson R.J. Solving Least Square Problems 1995, SIAM, Philadelphia.
38. Hansen P.C. Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion 1998, SIAM, Philadelphia.
39. Forrester J.B., Kalivas J.H. Ridge regression optimization using a harmonious approach. Journal of Chemometrics 2004, 18:372-384.
40. Green R.L., Kalivas J.H. Graphical diagnostics for regression model determinations with consideration of the bias/variance trade-off. Chemometrics and Intelligent Laboratory Systems 2002, 60:173-188.
41. Gowen A.A., Downey G., Esquerre C., O'Donnell C.P. Preventing over-fitting in PLS calibration models of near-infrared (NIR) spectroscopy data using regression coefficients. Journal of Chemometrics 2011, 25:375-381.
42. Höskuldsson A. The H-principle in modeling with applications to chemometrics. Chemometrics and Intelligent Laboratory Systems 1992, 14:139-153.
43. Myers R.H. Classical and Modern Regression with Applications 1990, Duxbury, Pacific Grove. second ed.
44. Golub G.H., Heath M., Wahba G. Generalized crossvalidation as a method for choosing a good ridge parameter. Technometrics 1979, 21:215-223.
45. Delwiche S.R., Reeves J.B. A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression. Applied Spectroscopy 2010, 64:73-82.
46. Ho T.K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998, 20:832-844.
47. Ho T.K. Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 1994, 16:66-75.
48. Kittler J., Hatef M., Duin R.P.W., Matas J. On combining classifers. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998, 20:226-239.
49. Tong W., Hong H., Fang H., Xie Q., Perkins R. Decision forests: combining the predictions of multiple independent decision tree models. Journal of Chemical Information and Computer Sciences 2003, 43:525-531.
50. Van Rhee A.M. Use of recursion forests in the sequential screening process: consensus selection by multiple recursion trees. Journal of Chemical Information and Computer Sciences 2003, 43:941-948.
51. Hibbon M., Evgenoiu T. To combine or not to combine: selecting among forecasts and their combinations. International Journal of Forecasting 2005, 21:15-24.
52. Hastie T.J., Tibshirani R.J., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2009, Springer-Verlag, New York. second ed.
53. Xu L., Zhou Y.P., Tang L.J., Wu H.L., Jiang J.H., Shen G.L., Yu R.Q. Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration. Analytica Chimica Acta 2008, 616:128-143.
54. Galvão R.K.H., Araújo M.C.U., Martins M.N., José G.E., Pontes M.J.C., Silva E.C., Saldanha T.C.B. An application of subagging for improvement of prediction accuracy of multivariate calibration models. Chemometrics and Intelligent Laboratory Systems 2006, 81:60-67.
55. Pinto L.A., Galvão R.K.H., Araújo M.C.U. Ensemble wavelet modeling for determination of wheat and gasoline properties by near and middle infrared spectroscopy. Analytica Chimica Acta 2010, 682:37-47.
56. Hansen K., Rathke F., Schroeter T., Rast G., Fox T., Kriegl J.M., Mika S. Bias-correction fo regression models: a case study on hERG inhibition. Journal of Chemical Information and Modeling 2009, 49:1486-1496.
57. Wang K., Chen T., Lau R. Bagging for robust non-linear multivariate calibration of spectroscopy. Chemometrics and Intelligent Laboratory Systems 2011, 105:1-6.
58. Borges C., Gómez-Carracedo M.P., Andrade J.M., Duarte M.F., Biscaya J.L., Aires-de-Sousa J. Geographical classification of weathered crude oil samples with unsupervised self-organizing maps and consensus criterion. Chemometrics and Intelligent Laboratory Systems 2010, 101:43-55.
59. Shao X., Bian X., Cai W. An improved boosting partial least squares method for near-infrared spectroscopic quantitative analysis. Analytica Chimica Acta 2010, 666:32-37.
60. Polishchuk P.G., Muratov E.N., Artemenko A.G., Kolumbin O.G., Muratov N.N., Kuz'min V.E. Appliation of random forest approach to QSAR prediction of aquatic toxicity. Journal of Chemical Information and Modeling 2009, 49:2481-2488.
61. Xu L., Jiang J.H., Zhou Y.P., Wu H.L., Shen G.L., Yu R.Q. MCCV stacked regression for model combination and fast spectral interval selection in multivariate calibration. Chemometrics and Intelligent Laboratory Systems 2007, 87:226-230.
62. Ni W., Brown S.D., Man R. Stacked partial least squares regression analysis for spectral calibration and prediction. Journal of Chemometrics 2009, 23:505-517.
63. Ni W., Brown S.D., Man R. Stacked PLS for calibration without standards. Journal of Chemometrics 2011, 25:130-137.
64. Kiralj R., Ferreira M.C. Is your QSAR/QSPR descriptor real or trash?. Journal of Chemometrics 2010, 24:681-693.
65. Henderson H.V., Velleman P.F. Building multiple regression models interactively. Biometrics 1981, 37:391-411.
66. Cook R.D., Weisberg S. Regression diagnostics with dynamic graphics. Technometrics 1989, 31:277-291.
67. Cook R.D. Graphics: Ideas for Studying Regression Through Graphics 1998, Wiley, New York.
68. Leion H., Folestad S., Josefson M., Sparén A. Evaluation of basic algorithms for transferring quantitative multivariate calibrations between scanning grating and FT-IR spectrometers. Journal of Pharmaceutical and Biomedical Analysis 2005, 37:47-55.
69. Bergman E.L., Brage H., Josefson M., Svensson O., Sparén A. Transfer of NIR calibrations for pharmaceutical formulations between different instruments. Journal of Pharmaceutical and Biomedical Analysis 2006, 41:89-98.
70. Kalivas J.H. Learning from Procrustes analysis to improve multivariate calibration. Journal of Chemometrics 2008, 22:227-234.
71. Bro R., Smilde A.K. Centering and scaling in component analysis. Journal of Chemometrics 2003, 17:16-33.
72. Lorber A., Faber K., Kowalski B.R. Local centering in multivariate calibration. Journal of Chemometrics 1996, 10:215-220.
73. Seasholtz M.B., Kowalski B.R. The effect of mean centering in prediction in multivariate calibration. Journal of Chemometrics 1992, 6:103-111.
74. Anderson C.E., Kalivas J.H. Fundamentals of calibration transfer through Procrustes analysis. Applied Spectroscopy 1999, 53:1268-1276.
75. Swierenga H., Haanstra W.G., de Weijer A.P., Buydens L.M.C. Comparison of two different approaches toward model transferability in NIR spectroscopy. Applied Spectroscopy 1998, 52:7-16.