Impact of standardization sample design on tikhonov regularization variants for spectroscopic calibration maintenance and transfer

Journal of Chemometrics

Volumn 24, Issue 3-4, 2010, Pages 218-229

  Kunz, Matthew R ^a   Ottaway, Joshua ^a   Kalivas, John H ^a   Andries, Erik ^b,c  

^a IDAHO STATE UNIVERSITY   (United States)

^b Central New Mexico Community College   (United States)

^c UNIVERSITY OF NEW MEXICO   (United States)

Author keywords

Augmentation; Calibration maintenance; Calibration standardization; Calibration transfer; Consensus modeling; Multivariate calibration; Tikhonov regularization; Wavelength selection

Indexed keywords

MAINTENANCE;

AUGMENTATION; CALIBRATION MAINTENANCE; CALIBRATION STANDARDIZATION; CALIBRATION TRANSFER; CONDITION; CONSENSUS MODELS; MULTIVARIATE CALIBRATION; SPECTROSCOPIC CALIBRATIONS; TIKHONOV REGULARIZATION; WAVELENGTH SELECTION;

CALIBRATION;

EID: 77951083710     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1302     Document Type: Article

References (73)

1. de Noord OE. Multivariate calibration standardization. Chemom. Intell. Lab. Syst. 1994; 25: 85-97.
2. Fearn T. Standardization and calibration transfer for near infrared instruments: a review. J. Near Infrared Spectrosc. 2001; 9: 229-244.
3. Feudale RN, Woody NA, Tan H, Myles AJ, Brown SD, Ferré J. Transfer of multivariate calibration models: a review. Chemom. Intell. Lab. Syst. 2002; 64: 181-192.
4. Cogdill RP, Anderson CA, Drennen JK, III. Process analytical technology case study, part III: calibration monitoring and transfer. AAPS Pharm. Sci. Tech. 2005; 6: E284-E297.
5. Brown SD. Transfer of multivariate calibration models. In Comprehensive Chemometrics: Chemical and Biochemical Data Analysis Vol. 3, Brown SD, Tauler R, Walczak B (eds-in-chief). Kalivas J, (section ed.). Elsevier: Amsterdam, 2009; 345-377.
6. 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.
7. Haaland DM. Synthetic multivariate models to accommodate unmodeled interfering spectral components during quantitative spectral analysis. Appl. Spectrosc. 2000; 54: 246-254.
8. 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. Appl. Spectrosc. 2006; 60: 1423-1431.
9. Westerhaus MO. Improving repeatability of NIR calibrations across instruments. In Proceedings of the Third International Near Infrared Spectroscopy Conference, Biston R, Bartiaux-Thill N (eds). Agriculture Research Centre Publishing: Gembloux, Belgium, 1991; 671-674.
10. Wang Y, Veltkamp DJ, Kowalski BR. Multivariate instrument standardization. Anal. Chem. 1991; 63: 2750-2756.
11. Capron X, Walczak B, de Noord OE, Massart DL. Selection and weighting of samples in multivariate regression model updating. Chemom. Intell. Lab. Syst. 2005; 76: 205-214.
12. Kramer KE, Small GW. Blank Augmentation protocol for improving the robustness of multivariate calibrations. Appl. Spectrosc. 2007; 61: 497-506.
13. Riley MR, Arnold MA, Murhammer DW. Matrix-enhanced calibration procedure for multivariate calibration models with near-infrared spectra. Appl. Spectrosc. 1998; 52: 1339-1347.
14. Sulub Y, Small GW. Spectral simulation methodology for calibration transfer of near-infrared spectra. Appl. Spectrosc. 2007; 61: 406-413.
15. Wehlburg CM, Haaland DM, Melgaard DK, Martin LE. New hybrid algorithm for maintaining multivariate quantitative calibrations of a near-infrared spectrometer. Appl. Spectrosc. 2002; 56: 605-614.
16. Wehlburg CM, Haaland DM, Melgaard DK. New hybrid for transferring multivariate quantitative calibrations of intra-vendor near-infrared spectrometers. Appl. Spectrosc. 2002; 56: 877-886.
17. Stork CL, Kowalski BR. Weighting schemes for updating regression models a theoretical approach. Chemom. Intell. Lab. Syst. 1999; 48: 151-166.
18. Sáiz-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: 147-159.
19. Zhu M. Kernels and ensembles. Am. Statist. 2008; 62: 97-109.
20. Mevik BH, Segtnan VH, Næs T. Ensemble methods and partial least squares regression. J. Chemom. 2004; 18: 498-507.
21. Wold S. Exponentially weighted moving principal components analysis and projections to latent structures. Chemom. Intell. Lab. Syst. 1984; 23: 149-161.
22. Helland K, Berntsen HA, Borgen OS, Martens H. Recursive algorithm for partial least squares. Chemom. Intell. Lab. Syst. 1991; 14: 129-137.
23. Condolfi A, Massart DL. Model updating for the identification of NIR spectra from a pharmaceutical excipient. Appl. Spectrosc. 2000; 54: 48-53.
24. Hansen PC. Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion. SIAM: Philadelphia, PA, 1998.
25. Aster RC, Borchers B, Thurber CH. Parameter Estimation and Inverse Problems. Elsevier: Amsterdam, 2005.
26. Kalivas JH, Siano GS, Andries E, Goicoechea HC. Calibration Maintenance and transfer using Tikhonov regularization approaches. Appl. Spectrosc. 2009; 63: 800-809.
27. Kunz MR, Andries E, Kalivas JH. Model updating for spectral calibration maintenance and transfer using 1-norm variants of Tikhonov regularization. Submitted to Anal. Chem. 2009.
28. Wang Y, Kowalski BR. Calibration transfer and measurement stability of near-infrared spectrometers. Appl. Spectrosc. 1992; 46: 764-771.
29. Sohn M, Barton FE II, Himmelsbach DS. Transfer of near-infrared calibration model for determining fiber content in flax: effects of transfer samples and standardization procedure. Appl. Spectrosc. 2007; 61: 414-418.
30. Sohn M, Himmelsbach DS, Barton FE, II, De Haseth JA. Transfer of calibrations for barley quality from dispersive instrument to Fourier transform near-infrared instrument. Appl. Spectrosc. 2009; 63: 1190-1196.
31. Bouveresse E, Massart DL, Dardenne P. Calibration transfer across near-infrared spectrometric instruments using Shenk's algorithm: effects of different standardization samples. Anal. Chim. Acta 1994; 297: 405-416.
32. Garcia-Olmo J, Garrido-Varo A, De Pedro E. The transfer of fatty acid calibration equations using four sets of unsealed liquid standardization samples. J. Near Infrared Spectrosc. 2001; 9: 49-62.
33. Osborne BG, Kotwal Z, Wesley IJ, Saunders L, Dardenne P, Shenk JS. Optical matching of near infrared reflectance monochromator instruments for the analysis of ground whole wheat. J. Near Infrared Spectrosc. 1999; 7: 167-178.
34. Park KS, Ko YH, Lee H, Jun CH, Chung H, Ku MS. Near-infrared spectral data transfer using independent standardization samples; as case study on the trans-alkylation process. Chemom. Intell. Lab. Syst. 2001; 55: 53-65.
35. Shenk JS, Westerhaus MO. New standardization and calibration procedures for NIRS analytical systems. Crop Sci. 1991; 31: 1694-1696.
36. Zeaiter M, Rutledge D. Preprocessing methods. In Comprehensive Chemometrics: Chemical and Biochemical Data Analysis Vol. 3, Brown SD, Tauler R, Walczak B (eds-in-chief). Kalivas J, (section ed.). Elsevier: Amsterdam, 2009; 121-231.
37. Guenard RD, Wehlburg CM, Pell RJ, Haaland DM. Importance of prediction outlier diagnostics in determining a successful intervendor multivariate calibration model transfer. Appl. Spectrosc. 2007; 61: 747-754.
38. Bates JM, Granger CWJ. The combination of forecasts. Oper. Res. Quart. 1969; 20: 451-468.
39. Bunn DW. Expert Use of Forecasts: Bootstrapping and Linear Models, Wright G, Ayton P (eds). Wiley: New York, 1987; 229-241.
40. Bunn DW. Combing forecasts. Eur. J. Operat. Res. 1988; 33: 223-229.
41. Clemen RT. Combining forecasts: a review and annotated bibliography. Int. J. Forecast. 1989; 5: 559-583.
42. Breiman L. Stacked regressions. Mach. Learn. 1996; 24: 49-64.
43. Breiman L. Bagging predictors. Mach. Learn. 1996; 24: 123-140.
44. Næs T, Isaksson T, Fern T, Davies T. A User Friendly Guide to Multivariate Calibration and Classification. NIR Publications: Chichester, 2002.
45. Hastie TJ, Tibshirani RJ, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, (2nd edn). Springer-Verlag: New York, 2009.
46. Claerbout JF, Muir F. Robust modeling with erratic data. Geophysics 1973; 38: 826-844.
47. Tibshirani R. Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. B 1996; 58: 267-288.
48. Stout F, Kalivas JH, Héberger K. Wavelength selection for multivariate calibrationusingTikhonovregularization. Appl. Spectrosc. 2007;61:85-95.
49. Zou H. The adaptive lasso ad its oracle properties. J. Am. Statist. Assoc. 2006; 101: 1418-1429.
50. Ottaway J, Andries E, Kalivas JH. Spectral multivariate calibration with wavelength selection using variants of Tikhonov regularization. Submitted to Anal. Chim. Acta 2010.
51. Zou H, Hastie T. Regularization and variable selection via the elastic net. J. Royal Statist. Soc. B 2005; 67: 301-320.
52. Gemperline PJ. Rugged spectroscopic calibration for process control. Chemom. Intell. Lab. Syst. 1997; 39: 29-40.
53. Ho TK. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 1998; 20: 832-844.
54. Ho TK. Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 1994; 16: 66-75.
55. Kittler J, Hatef M, Duin RPW, Matas J. On combining classifers. IEEE Trans. Pattern Anal. Mach. Intell. 1998; 20: 226-239.
56. Tong W, Hong H, Fang H, Xie Q, Perkins R. Decision forests: combining the predictions of multiple independent decision tree models. J. Chem. Inf. Comput. Sci. 2003; 43: 525-531.
57. Van Rhee AM. Use of recursion forests in the sequential screening process: consensus selection by multiple recursion trees. J. Chem. Inf. Comput. Sci. 2003; 43: 941-948.
58. Hibbon M, Evgenoiu T. To combine or not to combine: selecting among forecasts and their combinations. Int. J. Forecast. 2005; 21: 15-24.
59. Brown CD, Green RL. Critical factors limiting the interpretation of regression vectors in multivariate calibration. Trend. Anal. Chem. 2009; 28: 506-514.
60. Todeschini R, Consonni V, Pavan M. A distance measure between models: a tool for similarity/diversity analysis of model populations. Chemom. Intll. Lab. Syst. 2004; 70: 55-61.
61. Hansen K, Rathke R, Schroeter T, Rast G, Fox T, Kriegl JM. Bias correction of regression models: a case study on hERG inhibition. J. Chem. Inf. Model. 2009; 49: 1486-1496.
62. Davies AMC, Fearn T. Quantitative analysis via near infrared databases: comparison analysis using restructured near infrared and constituent data-deux (CARNAC-D). J. Near Infrared Spectrosc. 2006; 14: 403411.
63. Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression (with discussion). Anna. Statist. 2004; 32: 407-499.
64. Du YP, Kasemsumran S, Maruo K, Nakagawa T, Ozaki Y. Improvement of the partial least squares model performance for oral glucose intake experiments by inside mean centering and inside multiplicative signal correction. Anal. Sci. 2005; 21: 979-984.
65. Kalivas JH. Learning from Procrustes analysis to improve multivariate calibration. J. Chemom. 2008; 22: 227-234.
66. Wulfert F, Kok TW, Smilde AK. Influence of temperature on vibrational spectra and consequences for the predictive ability of multivariate models. Anal. Chem. 1998; 70: 1761-1767.
67. Wise BM, Gallagher NB, Bro R, Shaver JM. PLS_Toolbox 3.0 for use with MATLAB. Eigenvector Research: Manson, WA, 2003.
68. Forrester JB, Kalivas JH. Ridge regression optimization using a harmonious approach. J. Chemom. 2004; 18: 372-384.
69. Kalivas JH. Calibration methodologies. In Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, Brown SD, Tauler R, Walczak B (eds-in-chief). Kalivas J, (section ed.). Elsevier: Amsterdam, 2009; 1-32.
70. Lawson CL, Hanson RJ. Solving Least Square Problems. SIAM: Philadelphia, PA, 1995.
71. Bouveresse E, Massart DL, Dardenne P. Modified algorithm for standardization of near-infrared spectrometric instruments. Anal. Chem. 1995; 67: 1381-1389.
72. Cole SB, Kalivas JH. Calibration maintenance and transfer using weighted spectral differences. In preparation for J. Chemom.
73. Brenchley JM, Horchner U, Kalivas JH. Wavelength selection characterization for NIR spectra. Appl. Spectrosc. 1997; 51: 689-699.