Example of PCR, interpretation of calibration equations NIR analysis of 3 constituents Casein Lactate Glucose Scatter corrected (eliminates unwanted effects)

All objects were measured by NIR (many hundred var. in X)   Exp. design PC1=76.5% PC2=23.1% SUM=99.6% All objects were measured by NIR (many hundred var. in X) The NIR spectra were submitted to PCA Number of variables much larger than the number of objects

Principal components in regression Loadings   Principal components in regression Loadings ”Weighted” regression coefficients Glucose: 2.10 and -0.31 Lactate: -0.65 and 1.65 Two components explain > 99% of y Cross-validation Mean centred glucose spectrum Lactate-casein spectrum

Conclusions The NIR data matrix can be adequately modelled by a linear Beer’s law model (linear function of constituents) Good predictions are obtained Glucose is closely related to the first component and then responsible for more spectral variability than the other two constituents Which wavelengths are affected by the different constituents. Verified by comparing with spectral patterns of the constituents No outliers, strengthening the confidence of the results. The interpretation of scores, X-loadings and y-loadings fit well together