Scientists at Instituto Valenciano de Investigaciones Agrarias (IVIA) with Universidad Miguel Hernández de Elche and Universitat Politècnica de València have investigated two machine vision techniques for pomegranate quality monitoring.
Scientists collected the pomegranate fruit Mollar de Elche at seven different harvest times. Colour and hyperspectral images of the intact fruit and arils were acquired at each harvest. Physicochemical properties such as total soluble solids, titratable acidity, maturity index, BrimA, internal colour, total phenolic compounds content and antioxidant activity were measured in the juice of each fruit. Relationships between colour (L*, a*, b*) and spectral (720–1050 nm) data obtained from the images of the intact fruit and arils were investigated for physicochemical properties using partial least square regression models. Discrimination of the different maturity stages also was carried out using partial least square discriminant analysis models.
"Similar results were obtained in the prediction of the physicochemical properties using the colour and hyperspectral images of the intact fruit. However, the predictions achieved for the information about the arils were higher using hyperspectral imaging. In the discrimination of maturity stage, the highest accuracies were obtained using hyperspectral imaging, where 95% of intact fruit and 100% of arils where correctly classified - The scientists explain - These results indicate the great potential of machine vision techniques, especially hyperspectral imaging, for monitoring the quality of intact Mollar de Elche pomegranate fruit and arils".
Source: Sandra Munera, Francisca Hernández, Nuria Aleixos, Sergio Cubero, José Blasco, 'Maturity monitoring of intact fruit and arils of pomegranate cv. ‘Mollar de Elche’ using machine vision and chemometrics', October 2019, Postharvest Biology and Technology, Volume 156, 110936.