CIELAB – Spectral image MATCHING: An app for merging colorimetric and spectral images for grapes and derivatives. (July 2021)

• Record Type: Journal Article
• Authors: Rodríguez-Pulido, Francisco J.; Gordillo, Belén; Heredia, Francisco J.; González-Miret, M. Lourdes
• Is Part Of: Food control. Volume 125(2021)
• Publication Date: 2021-07
• Subjects: CIELAB -- NIR -- Spectral imaging -- Image matching -- MATLAB; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2021.108038
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Imaging techniques have revolutionised the way quality is assessed in food products. Using cameras, it is possible to estimate not only the chemical composition of a product but also its geometric distribution. However, the limited range of detectors implies the use of different measuring equipment. The presence of small and discrete samples or very heterogeneous samples makes joining both sets of data a complicated task. This work arises from the need to merge images with colour information and NIR spectral information on grape samples and derivatives. An application has been created under MATLAB to join this type of images so that it is possible to simultaneously extract the colour and/or spectral information of each pixel or object present in the image. Although the software can be used in a wide range of applications, it has been successfully applied to grape and grape seed samples. In red grape bunches, it was possible to evaluate individually grapes and notice differences due to changes in visible and infrared regions at the same time. In the case of white grape seeds, it was proved that merged images were better to discriminate between varieties than the single CIELAB or spectral images. Highlights: A new software was developed for merging colorimetric and NIR spectral images. New resulting images allow getting the CIELAB information and NIR spectrum from any pixel at the same time. This program has been successfully applied to grape bunches and grape seeds images.