Digital Image-based Inductive Characterization and Classification for Improving the Quality Inspection of Diverse Food Products

With the increasingly demanding international regulations for import and export of food products, as well as with the increased awareness and sophistication of consumers, the food industry needs accurate, fast and efficient quality inspection means. Each producer seeks to ensure that their products satisfy all consumer's expectations and that the appropriate quality level of each product is offered and sold to each different socio-economic consumer group. This paper presents three study cases where digital image analysis and inductive characterization techniques have been successfully applied to improve the quality inspection process. Three very different and unrelated basic food products are studied: Hass Avocado, Manila Mango and Corn Tortillas. Each one of these products has some special and particular features that complicate the quality inspection process, but each of these products is also very important in economical terms for the sheer volume of their production and marketing. Experimental results of each case shows that the general technique has great accuracy and significantly lower costs.