Inverse, classical, empirical and non-parametric calibrations in a bayesian framework

Journal of Near Infrared Spectroscopy

Volumn 18, Issue 1, 2010, Pages 27-38

  Fearn, Tom ^a   Pérez Marín, Dolores ^b   Garrido Varo, Ana ^b   Guerrero Ginel, José Emilio ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Bayes; Calibration; Classical; Compound feedstuff; Empirical; Inverse; Kernel density estimation; Local; Nir spectroscopy; Non linear

Indexed keywords

EID: 77951155475     PISSN: 09670335     EISSN: None     Source Type: Journal    
DOI: 10.1255/jnirs.855     Document Type: Article

References (16)

1. K.H. Norris, R.F. Barnes, J.E. Moore and J.S. Shenk, "Predicting forage quality by infrared reflectance spectroscopy", J. Anim. Sci. 43, 889 (1976).
2. H. Martens and T. Naes, Multivariate Calibration. John Wiley & Sons Ltd. Chichester, UK (1989).
3. T. Naes, K. Kvaal, T. lsaksson and C. Miller, "Artificial neural networks in multivariate calibration", J. Near Infrared Spectrosc. 1, 1 (1993). doi: 10.1255/jnirs.1
4. R.P. Cogdill and P. Dardenne, "Least-squares support vector machines for chemometrics: an introduction and evaluation", J. Near Infrared Spectrosc. 12, 93 (2004). doi: 10.1255/jnirs.412
5. A.M.C. Davies and T. Fearn, "Quantitative analysis via near infrared databases: comparison analysis using restructured near infrared and constituent data-deux (CARNAC-D)". J. Near Infrared Spectrosc. 14, 403 (2006). doi: 10.1255/jnirs.712
6. D. Pérez-Marín, A. Garrido-Varo and J.E. Guerrero, "Implementation of LOCAL algorithm with near-infrared spectroscopy for compliance assurance in compound feedingstuffs". Appl. Spectrosc. 59, 69 (2005). doi: 10.1366/0003702052940585
7. D. Pérez-Marín, A. Garrido-Varo, J.E. Guerrero and J.C. Gutiérrez-Estrada. "Use of artificial neural networks in near-infrared reflectance spectroscopy calibrations for predicting the inclusion percentages of wheat and sunflower meal in compound feedingstuffs", Appl. Spectrosc. 60,1062 (2006). doi: 10.1366/000370206778397506
8. D. Pérez-Marín, A. Garrido-Varo, J.E. Guerrero, T. Fearn and A.M.C. Davies, "Advanced non-linear approaches for predicting the ingredient composition in compound feedingstuffs by near-infrared reflection spectroscopy", Appl. Spectrosc. 62, 536 (2008). doi: 10.1366/ 000370208784344389
9. P.M. Lee, Bayesian Statistics an Introduction, 3rd Edn. Arnold, London, UK (2004).
10. A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin, Bayesian Data Analysis, 2nd Edn. Chapman and Hall/CRC, London. UK (2004).
11. P.J. Brown, Measurement, Regression and Calibration. Clarendon Press, Oxford, UK (1993).
12. T. Fearn, "Flat or natural? A note on the choice of calibration samples", in Near Infra-Red Spectroscopy: Bridging the Gap between Data Analysis and NIR Applications, Ed by K.I. Hildrum, T. Isaksson, T. Naes and A. Tandberg. Ellis Horwood, Chichester, UK, p. 61 (1992).
13. T. Lwin and J.S. Maritz, "A note on the problem of statistical calibration". J. R. Statist. Soc. C 29, 135 (1980).
14. P.J. Brown, "Multivariate calibration", J. R. Statist. Soc, B 44, 287 (1982).
15. C. de Boor, A Practical Guide to Splines. Springer Verlag, New York. USA (1978).
16. M.P. Wand and M.C. Jones. Kernel Smoothing. Chapman and Hall/CRC, London, UK (2004).