CIELAB-Spectral image MATCHING: An app for merging colorimetric and spectral images for grapes and derivatives

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.

Automated summary

The use of imaging techniques to combine color and spectral information for quality assessment in food products is an effective method, despite challenges. This approach has been successfully applied to grape and grape seed samples, showing that merged images can better differentiate between varieties.