Development of an automated method for the identification of defective hazelnuts based on RGB image analysis and colourgrams

Over the past decades, Red-Green-Blue (RGB) image analysis has gained increasing importance in industrial applications, since it has widely proved to be a suitable tool for food quality and process control. This article describes the development of a fast and objective method for the automated identification of defective hazelnut kernels based on multivariate analysis of RGB images. To this aim, an overall sample set of 2000 half-cut hazelnut kernels, previously assigned by industrial expert assessors as sound or defective (i.e. rotten or pest-affected), was collected and imaged using a digital camera. The colour-related information of the images was converted into one-dimensional signals, named colourgrams, which were firstly explored through the Principal Component Analysis and subsequently used to build classification models, based on both Partial Least Square-Discriminant Analysis (PLS-DA) and interval-PLS-DA (iPLS-DA) algorithms. A tree-structure hierarchical classification approach has been considered, i.e. the discrimination between sound and defective kernels as a first rule, and the discrimination between the two types of defect as a second rule. The best sound vs defective classification model was able to correctly recognize approximately the 97% of the test set defective samples, while the best rotten vs pest-affected model allowed classifying correctly more than 92% of the test set samples. Moreover, the image reconstruction performed using the selected colourgram features led to an exhaustive interpretation of the decision-making criteria adopted by the classification algorithms and further confirmed the reliability of the proposed method.