Abstract
The development of internal breakdown of South African Bulida apricots during cold storage, rendering the fruit unsuitable for canning, causes significant post-harvest losses. Regression models to predict internal post-storage quality using near infrared (NIR) spectroscopy and multivariate classification techniques were developed using NIR spectra of the intact fruit collected prior to storage and subjective quality evaluations performed after a cold storage period of four weeks. A correct classification rate of 69% was obtained using multivariate adaptive regression splines (MARS) compared to 50% obtained by soft independent modelling by class analogy (SIMCA). NIR regression models developed for soluble solids content (SSC) of intact fruit as well as for direct NIR measurements on the exposed fruit tissue gave similar results, thus confirming sufficient NIR light penetration into the intact fruit. The best prediction results were obtained when two spectral measurements per fruit (one on each half of the fruit), compared to single measurements, were used.
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