The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allows measurement of these property perceptions that are performed by professional panels. However, systematic evaluations of attributes by these tasting panels, which avoid the subjective character for an individual taster, have a high economic, temporal and organizational cost. The process is only applied in a sampled way so that its result cannot be used on a sound and complete quality system. In this paper, we present a method that allows making use of a non-destructive measurement of physical–chemical properties of the target product to obtain an estimation of the sensory description given by QDA-based procedure. More concisely, we propose that through Artificial Neural Networks (ANNs), we will obtain a reliable prediction that will relate the near-infrared (NIR) spectrum of a complete set of cheese samples with a complete image of the sensory attributes that describe taste, texture, aspect, smell and other relevant sensations.

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.