Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR)

Food Quality and Preference

Volumn 11, Issue 1-2, 2000, Pages 5-16

  Martens, Harald ^a,b   Martens, Magni ^c  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^b NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^c UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

THEOBROMA CACAO;

EID: 0033636234     PISSN: 09503293     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0950-3293(99)00039-7     Document Type: Article

References (19)

1. Centener, V. (1998). Methods and Diagnostics in Multivariate Calibration. Ph.D. thesis, Vrije Universiteit Brussel.
2. CAMO ASA (1998). The Unscrambler, Box 1405 Vika, N-0115 Oslo, Norway.
3. Efron, B. (1982). The Jack-knife, the Bootstrap and Other Resampling Plans. Philadelphia, Pennsylvania: Society for Industrial and Applied Mathematics, ISBN 0-89871-179-7.
4. Efron, B., & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Monographs on Statistics and Applied Probability 57. Chapman and Hall, NY: ISBN 0-412-04231-1.
5. Folkenberg, D., Bredie, W. L. P., & Martens, M. (1999). What is mouthfeel? Sensory-rheological relationships in instant hot cocoa drinks. Journal of Sensory Studies, 14, 181-195.
6. Gower, J. C. (1975). Generalized procrustes analysis. Psychometrika, 40, 33-52.
7. Høskuldsson, A. (1996). Prediction Methods in Science and Technology, Vol. 1, Basic Theory. Lundtofte, Denmark: Thor Publishing, ISBN 87-985941-0-9.
8. Høy, M., Steen, K. J., & Martens, H. (1998). Review of PLSR prediction error in unscrambler. Chemometrics and Intelligent Laboratory Systems, in press.
9. MacFie, H. J. H., & Hedderley, D. (1993). Current practice in relating sensory perception to instrumental measurements. Food Quality and Preference, 4, 41-49.
10. Martens, H., & Dardenne, P. (1998). Validation and verification of regression in small data sets. Chemometrics and Intelligent Laboratory Systems, 44(1,2), 99-122.
11. Martens, M., & Martens, H. (1986). Partial least squares regression. In J. R. Piggott, Statistical Procedures in Food Research (pp. 292-360). London: Elsevier Applied Sciences.
12. Martens, H., & Geladi, P. (1999). Multivariate calibration. Encyclopedia of Statistical Sciences, Vol. 3 (pp. 483-495). New York: J. Wiley and Sons, Inc.
13. Martens, H., & Næs, T. (1987). Multivariate calibration by data compression. In P. Williams, & K. Norris, Near-infrared Technology in the Agriculture and Food Industries (pp. 57-87). St. Paul, Minnesota: American Ass. of Cereal Chem., Inc.
14. Martens, H., & Næs, T. (1989). Multivariate Calibration. Chichester UK: J. Wiley & Sons Ltd.
15. Math Works Inc. (1998). The Matlab Version 5. Natick, MA, USA.
16. Wakeling, I. N., & Morris, J. J. (1993). A test of significance for partial least squares regression. J. Chemometrics, 7, 291-304.
17. Wold, S. (1978). Cross-validatory estimation of the number of components in factor analysis and principal component models. Technometrics, 20, 397-406.
18. Wold, S., Albano, C., Dunn III, W. J., Esbensen, K., Hellberg, S., Johansson, E., & Sjoestroem, M. (1983). Pattern recognition: Finding and using regularities in multi-variate data. In H. Martens, & H. Russwurm Jr., Food Research and Data Analysis (pp. 147-188). London: Applied Science Publishers.
19. Wold, S., Martens, H., & Wold, H. (1983). The multivariate calibration problem in chemistry solved by the PLS method. In A. Ruhe, & B. Kägstroem, Matrix Pencils (pp. 286). Heidelberg: Springer.