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Dried grass silage analysis by NIR reflectance spectroscopy—A Comparison of stepwise multiple linear and principal component techniques for calibration development on raw and transformed spectral data
Author(s) -
Downey Gerard,
Robert Paul,
Bertrand Dominique,
Devaux MarieFrancoise
Publication year - 1989
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180030207
Subject(s) - principal component regression , principal component analysis , calibration , linear regression , stepwise regression , mathematics , multivariate statistics , silage , data transformation , chemometrics , regression analysis , standard error , statistics , analytical chemistry (journal) , chemistry , chromatography , computer science , food science , data mining , data warehouse
Calibrations to predict crude protein (CP) and in vitro dry matter digestibility (IVDMD) in dried grass silage from reflectance data collected at 19 wavelengths on an InfraAlyzer 400R have been developed using stepwise multiple linear (SML) and principal component (PC) regression techniques. A direct comparison of the efficacy of each multivariate technique in this applications has been possible by using identical calibration development and evaluation sample sets. The effect of two data transformation steps prior to PC regression was also investigated. PC regression of raw reflectance data yielded no significant improvement in the standard errors of prediction (SEP) for CP and IVDMD over those obtained by SMLR, viz. 0·61 vs 0·63 and 2·9 vs 3·0 respectively. Computation time for development and evaluation of the PC regression equation was less than for selection of the best SMLR equation, and PCR equations may be more robust. Data transformation to reduce granularity effects prior to PCR did not produce any improvement in predictive accuracy for either IVDMD or CP.