Application of principal component analysis to the prediction of lucerne forage protein content and in vitro dry matter digestibility by NIR spectroscopy

Abstract Application of principal component regression (PCR) was proposed for the development of a prediction equation of forage composition by near infra‐red spectroscopy. PCR involves two steps: (a) the creation of new synthetic variables by principal component analysis (PCA) of spectral data, and (b) multiple linear regression with these new variables. Results obtained by this procedure have been compared with those generated by the conventional application of multiple linear regression (MLR) on spectral data. The comparison used the determination of protein content and in vitro dry matter digestibility (IVDMD) in 345 samples of lucerne forages. For protein determination, results of both procedures were quite similar (correlation coefficients: 0.978 and 0.980; standard errors of calibration: 0.86 and 0.84% DM; standard errors of prediction: 0.81 and 0.80% DM respectively for MLR and PCR prediction equations). The same was observed for IVDMD determination (correlation coefficients: 0.942 and 0.951; standard errors of calibration: 1.89 and 1.71% DM; standard errors of prediction: 2.22 and 2.22% DM, respectively). A large number of PCA variables were necessary for an accurate prediction of both constituents. The influence of the number of regression terms introduced in the PCR equation has been studied. The criterion for stopping the introduction of new terms in PCR did not seem as critical as in MLR.