Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-17T21:19:44.350Z Has data issue: false hasContentIssue false
  • English
  • Français

The spread of the calibration set in near-infrared reflectance spectroscopy

Published online by Cambridge University Press:  27 March 2009

G. Z. Wetherill  and
I. Murray
G. Z. Wetherill
Affiliation:
Scottish Agricultural Statistics Service, The King's Building, Mayfield Road, Edinburgh EH9 3JZ
I. Murray
Affiliation:
North of Scotland College of Agriculture, 581 King Street, Aberdeen AB9 1UD
Get access Rights & Permissions [Opens in a new window]

Summary

Frequently in near-infrared reflectance spectroscopy, a calibration is developed using very restricted data sets, e.g. material from one season, a small area or of a limited type: consequently, the predictions may have limited validity. This paper describes the use of both restricted and wide calibration sets for the prediction of crude protein in grass, silage and hay. Results show that predictions from the wider calibration sets are often as good as or better than predictions from restricted calibration sets. Therefore the use of wide calibration sets should be considered much more frequently in near-infrared reflectance.

Type
Research Article
Information
The Journal of Agricultural Science , Volume 109 , Issue 3 , December 1987 , pp. 539 - 544
Copyright
Copyright © Cambridge University Press 1987

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Draper, N. R. & Smith, H. (1981). Applied Regression Analysis, 2nd edn.New York: Wiley.Google Scholar
Eisen, E. J., Bundy, T. R., McClure, W. F. & Hörstgen-Schwark, G. (1984). Estimating body composition in mice by Near-Infrared Spectrophotometry. Journal of Animal Science 58, 1181–1190.CrossRefGoogle Scholar
Hocking, R. R. (1976). The analysis and selection of variables in linear regression. Biometrics 32, 1–49.CrossRefGoogle Scholar
Marten, G. C, Halgerson, J. L. & Cherney, J. H. (1983). Quality prediction of small grain forages by near infrared reflectance spectroscopy. Crop Science 23, 94–96.CrossRefGoogle Scholar
Murray, I. (1983 a). Rapid silage evaluation by near infrared reflectance (NIRR) spectroscopy. Research Investigations and Field Trials 1983–84, pp. 83–87.Google Scholar
Murray, I. (1983 b). Fundamental outline of NIR analysis — some forage and feed examples. NIR Analysis — How Near Infrared Reflects Composition, North of ScotlandCollege of Agriculture.Google Scholar
Murray, I. (1984). Evaluation of hay by near infrared reflectance (NIR). Research Investigations and Field Trials 1983–84, pp. 92–94.Google Scholar
Murray, I. & Hall, P. A. (1983). Animal feed evaluation by use of near infrared reflectance (NIR) spectro-computer. Analytical Proceedings 20, 75–79.Google Scholar
Shenk, J. S., Landa, I., Hoover, M. R. & Westerhaus, M. O., (1981). Description and evaluation of a near infrared reflectance spectro-computer for forage and grain analysis. Crop Science 21, 355–358.CrossRefGoogle Scholar
Shenk, J. S. & Westerhaus, M. O. (1985). Accuracy of NIRS instruments to analyze forage and grain. Crop Science 25, 1120–1122.CrossRefGoogle Scholar
Winch, J. E. & Major, H. (1981). Predicting nitrogen and digestibility of forages using near infrared reflectance photometry. Canadian Journal of Plant Science 61, 45–51.CrossRefGoogle Scholar