Selection of representative calibration sample sets for near-infrared reflectance spectroscopy to predict nitrogen concentration in grasses

Chemometrics and Intelligent Laboratory Systems

Volumn 111, Issue 1, 2012, Pages 59-65

  Shetty, Nisha ^a   Rinnan, Åsmund ^b   Gislum, René ^a  

^a AARHUS UNIVERSITY   (Denmark)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

CADEX; NIR; Nitrogen; PLSR; Puchwein's method; Representative calibration samples

Indexed keywords

NITROGEN;

ACCURACY; ANALYTIC METHOD; ARTICLE; CALIBRATION; COMPUTER AIDED DESIGN; CONTROLLED STUDY; CULTIVAR; EXPERIMENTAL DESIGN; GRASS; INTERMETHOD COMPARISON; LABORATORY TEST; LEAF NITROGEN CONTENT; LEARNING ALGORITHM; MATHEMATICAL MODEL; NEAR INFRARED REFLECTANCE SPECTROSCOPY; NONHUMAN; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROCESS MODEL; SCORING SYSTEM;

EID: 84856090649     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2011.11.013     Document Type: Article

References (22)

1. Mark H., Workman J. Chemometrics in Spectroscopy 2007, Academic Press, San Diego, Calif.
2. Næs T., Isaksson T., Fearn T., Davies T. A User-Friendly Guide to Multivariate Calibration and Classification 2002, NIR Publications, Chichester, UK.
3. Lorber A., Kowalski B.R. The effect of interferences and calibration design on accuracy: implications for sensor and sample selection. Journal of Chemometrics 1988, 2:67-79.
4. Zhou Z.H., Wu J.X., Tang W. Ensembling neural networks: many could be better than all. Artificial Intelligence 2002, 137:239-263.
5. Ferré J., Rius F.X. Selection of the best calibration sample subset for multivariate regression. Analytical Chemistry 1996, 68:1565-1571.
6. Ferré J., Rius F.X. Constructing D-optimal designs from a list of candidate samples. Trends in Analytical Chemistry 1997, 16:70-73.
7. Kennard R.W., Stone L.A. Computer aided design of experiments. Technometrics 1969, 11:137-148.
8. Snee R.D. Validation of regression models: methods and examples. Technometrics 1977, 19:415-428.
9. Næs T. The design of calibration in NIR reflectance analysis by clustering. Journal of Chemometrics 1987, 1:121-134.
10. Puchwein G. Selection of calibration samples for near-infrared spectrometry by factor analysis of spectra. Analytical Chemistry 1988, 60:569-573.
11. Honigs D.E., Hieftje G.H., Mark H.L., Hirschfeld T.B. Unique-sample selection via near-infrared spectral subtraction. Analytical Chemistry 1985, 57:2299-2303.
12. Shenk J.S., Westerhaus M.O. Population, definition, sample selection, and calibration procedures for near-infrared reflectance spectroscopy. Crop Science 1991, 31:469-474.
13. Hildrum K.I., Isaksson T., Naes T., Tandberg A. Near infra-red spectroscopy; bridging the gap between data analysis and NIR applications 1992, Ellis Horwood, Chichester.
14. Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 1964, 36:1627-1638.
15. Geladi P., McDougall D., Martens H. Linearization and scatter-correction for near-infrared reflectance spectra of meat. Applied Spectroscopy 1985, 39:491-500.
16. Martens H., Nielsen J.P., Engelsen S.B. Light scattering and light absorbance separated by extended multiplicative signal correction. Application to near-infrared transmission analysis of powder mixtures. Analytical Chemistry 2003, 75(3):394-404.
17. Chin W.W., Newsted P.R. Structural equation modelling analysis with small samples using partial least squares. Statistical strategies for small sample research 1999, 307-341. Sage, Thousand Oaks, CA. R.H. Hoyle (Ed.).
18. Nasser F., Wisenbaker J. A Monte Carlo study investigating the impact of item parceling on measures of fit in confirmatory factor analysis. Educational and Psychological Measurement 2003, 63:729-757.
19. Marsh H.W., Hau K.-T., Balla J.R., Grayson D. Is more ever too much? The number of indicators per factor in confirmatory factor analysis. Multivariate Behavioral Research 1998, 33:181-220.
20. Dillon W.R., Kumar A., Mulani N. Offending estimates in covariance structure analysis: comments on the causes of and solutions to Heywood cases. Psychological Bulletin 1987, 101:126-135.
21. Isaksson T., Næs T. Selection of samples for calibration in near-infrared spectroscopy. Part II: selection based on spectral measurements. Applied Spectroscopy 1990, 44(7):1152-1158.
22. Wakelinc I.N., Macfie H.J.H. A robust PLS procedure. Journal of Chemometrics 1992, 6(4):189-198.