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).