Determine product quality reliably.
When the ISO 9002 standards are extensively applied, the present quality classifying systems will have to be substituted by others that are faster, more efficient and economical. Although the research undertaken in this project will focus on developing a quality-determining tool that can be used on many foods, this study specifically will focus on two products: olives destined for table consumption and potatoes.
Olives have to be sorted based on their size and external defects, and on an homogeneous color. At present, olives are classified after harvesting, after fermentation and prior to filling and packing. However, this classification must be done manually, which is extremely expensive, and the technique only allows the characterization of a small, not representative, part of the production.
In addition, the sorting and manual selection is based on the subjectivity of the worker, whose criteria do not remain invariable after long hours of work in a monotonous task, under stressful working conditions.
The main objective of scientists is to build a system that can automatically classify a product and which is capable of analyzing 2500 kg of olives per hour and 10,000 kg of potatoes per hour. The researchers plan to develop an artificial vision system for quality inspection of olives and potatoes. The work includes the illumination design and configuration of the cameras, the development of the software to obtain information from the images and the development of software for product selection.
Scientists intend to implement algorithms over a specific platform so that the images can be processed in real time. There will also be a user's graphic interface. Development of a process line that can handle the products at high speed also is planned. Researchers will have to develop techniques for separating the product at high speed, and for synchronizing the produce rejection mechanism, camera, computer and belt conveyor.
Further information. Ricardo Diaz Pujol, Department of Instrumentation and Automation, Instituto Tecnologico Agroalimentario (AINIA), Parque Tecnologico de Valencia, Benjamin Franklin, 5-11 46980-Paterna, Valencia, Spain; phone: +34 961 366 090; fax: +34 961 318 008; URL: http://www.ainia.es.
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|Publication:||Emerging Food R&D Report|
|Date:||Aug 1, 2001|
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