Hyperspectral machine vision logics applied to hazelnut quality assessment

Hazelnuts present different market values according to their quality. Such a quality is usually quantified in terms of freshness of the products, as well as presence and entity of defects, mould and decays. Such groups of parameters represent a fundamental set of attributes conditioning hazelnuts organolectic properties and their overall quality in terms of marketable products. Sorting strategies exist but they fail when an higher degree of detection is required especially if addressed to discriminate between hazelnuts freshness and when aiming to perform an “early detection” of pathogen agents responsible of the development of moulds and decays. In this paper, the possibility to apply an hyperpsectral imaging (HSI) approach for hazelnut quality detection has been investigated. The proposed approach is based on the utilization of an integrated hardware and software (HW&SW) platform embedding conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Although HSI was originally developed for remote sensing, it has recently emerged as a powerful process analytical tool, for non-destructive analysis, in many research and industrial sectors. The results showed as HSI allows to perform a full characterization of such a kind of product in terms of quality assessment. Simple, reliable and robust sorting logic can be successfully developed through the spectral signatures collection and analysis.