A digitalization strategy for quality control in food industry based on Artificial Intelligence techniques

In food products marketing, ensuring to the consumer identical organoleptic properties is vital for maintaining the client fidelity. Systematically achieve it by using as sensory information the valuations of a tasting panel, is unfeasible. Routinely, to assemble a tasting panel involves organizational and economics costs, as well as the sensory fatigue and the subjectivity of the panel members. In this paper is proposed a vitualization strategy or computational model focused on food products quality control, based on cooperation and data exchange between the main agents involved in the process: quality managers, professional tasters, production managers, inspection authorities, etc. Virtualization (digitalization) is supported on a ICatador cloud platform which has intelligent algorithms embedded to predict the food sensory properties. These algorithms have Near InfraRed Spectroscopy data of the product samples as input. As a validation scenario, our virtualization approach has been applied to the ripening cheese elaboration. Thanks to advanced visualization techniques, the quality manager can immediately and systematically know the merit figure related to a product sensory quality. The ICatador solution, has two profitable aspects. Sensory analysis is performed without routinely gathering a professional tasting panel. As a huge amount of data coming from the elaboration process itself are available, the intelligent algorithms are enriched by these data for the adaptation to the product elaboration process. In this way, we will be able to fine-tune continuously the machine-learning algorithms to the particular process and use them intelligently to increase the competitiveness.