To preserve the quality of fresh pear fruit after harvest and deliver quality fruit year-round a controlled supply chain and long-term storage are applied. During storage, however, internal disorders can develop due to suboptimal storage conditions that may not cause externally visible symptoms. This makes them impossible to detect using current commercial quality grading systems in a reliable and non-destructive way.
Scientists at KU Leuven (Belgium) have developed a combination of a Support Vector Machine coupled with a feature extraction algorithm and X-ray Computed Tomography to successfully detect internal disorders in Conference and Cepuna pear fruit non-destructively.
"Classifiers were able to distinguish defective from sound fruit with classification accuracies ranging between 90.2 and 95.1% depending on the cultivar and number of used features - the scientists explain - Moreover, low false positive and negative rates were obtained, respectively ranging between 0.0 and 6.7%, and 5.7 and 13.3%. Classifiers trained on Conference data were transferred effectively to the Cepuna cultivar, suggesting the generalizability also to other cultivars".
Continuing to develop both hardware and software to increase inspection speed and reduce equipment costs can allow the method to be implemented in industrial applications, e.g., inline translational X-ray CT.
Source: Tim Van De Looverbosch, Md. Hafizur Rahman Bhuiyand, Pieter Verboven, Manuel Dierick, Denis Van Loo, Jan De Beenbouwer, Jan Sijbers, Bart Nicolaï, 'Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning', 2020, Food Control, Vol. 113, 107170.