Abstract
A statistical analysis was performed on a large spectral data set to analyse the effect of orchard, season and cultivar. Season and cultivar were responsible for a major amount of the spectral variability, whereas the influence of the orchard was low and only appeared for certain cultivars during specific seasons. The robustness of the calibration models for soluble solids content with respect to the three factors was tested based on external validations. It was found that the accuracy of the models increased considerably when including more variability in the calibration set. Further, overfitting of the calibration model was avoided. On the other hand, adding more data to the calibration set increased the chance of adding atypical data, which resulted in reduced model accuracy. It is, therefore, important to construct the calibration data set in such a way that it is representative for future measurements. When the effect of a certain factor is known a priori, e.g. cultivar, it is recommended to use specific calibration models.
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