Authors:
J. A García-Esteban
1
;
Belén Curto
1
;
Vidal Moreno
1
and
Beatriz Martínez
2
Affiliations:
1
Department of Computer Science and Automatics, University of Salamanca, Faculty of Science, Salamanca and Spain
;
2
Technology Meat Station of Castilla y León, Guijuelo and Spain
Keyword(s):
Monitoring of Food Quality Control, Food Sensory Predictions, Food Machine Learning Estimation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computer-Based Manufacturing Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Human Factors & Human-System Interface
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Knowledge-Based Systems
;
Quality Control and Management
;
Symbolic Systems
Abstract:
The iberian ham is a high valued product, due to this fact, it is very important to offer to the costumer a high quality food product and to ensure its organoleptic properties. Producers have to evaluate, periodically, its sensorial attributes by a professional tasting panel. Due to high elevated organizational and economics costs, in addition to, the sensory fatigue and the subjectivity of the panel members, only a few product lots are sampled. In this paper is proposed a cloud manufacturing based platform to monitor the quality of Iberian ham. The success of this solution is based on cooperation and data exchange between the main agents involved in the process: quality manager, professional tasters, production manager, inspection authorities, etc. Intelligent algorithms have been embedded into the cloud monitoring platform to predict the ham sensory properties, using the Near InfraRed Spectroscopy data from the product samples as input. The key feature of the solution is that the s
ensory analysis is performed without gathering routinely a professional tasting panel, but the solution also allows to the quality manager, with advanced visualization techniques, to monitor what is the merit figure related with a specific type of ham or shoulder. Another important aspect of the solution is that, due to the huge amount of data coming from the elaboration process itself are available is possible to fine-tune continuously the machine-learning algorithms to the particular producer and use them intelligently to increase the competitiveness.
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