A cross-validation method
was used to determine the number of factors in the regression
models and to avoid overfitting. For cross-validation, the calibration
set was partitioned in four groups; each group was then predicted
using a calibration developed on the other samples; finally,
validation errors were combined to obtain a standard error of
cross-validation (SECV)