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

2. 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

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

4. 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