Journal of Food Processing & Technology

Detecting, identifying, and quantifying adulterations in foods through computational artificial intelligence

9^th Euro-Global Summit & Expo on Food & Beverages

July 11-13, 2016 Cologne, Germany

John C Cancilla, Regina Aroca-Santos, Gemma Matute, Enrique S Pariente, Kacper W Wierzchos and Jose S Torrecilla

Complutense University of Madrid, Spain
University of South Florida, USA

Posters & Accepted Abstracts: J Food Process Technol

Abstract:

In the past, many food sectors have suffered the impact of fraudulent activities which involve the adulteration of foods, which not only endanger the economy and prestige of the producers or the quality of the goods, but in occasions the health of the consumers. One of the main fields our research group focuses is on the design of different mathematical tools based on algorithms such as artificial neural networks (ANNs), which are a relevant part of the computational artificial intelligence field, to aid in this particular sense. They allow the detection of adulterated foods, the identification of particular adulterants, and even their quantification. In this work, an array of applications will be covered, as they are designed with the intention to help protect the integrity of the food sector. Specifically, regarding extra virgin olive oil (EVOO), various tools based on supervised ANNs to model spectroscopic data have been created and optimized to quantify and identify lower grade olive oils (pomace and refined) or oils with different botanical origins (corn and sunflower). On the other hand, non-supervised ANNs combined with calculations based on chaotic parameters have also been employed to detect edible oils that are used to adulterate EVOO. Finally, an assortment of linear and non-linear mathematical models have been employed to characterize binary mixtures of 6 vinegars with different origins (red wine, white wine, molasses, apple, apple cider, and rice). All of these models have been validated accordingly, resulting in reliable and accurate tools that can be useful for many phases during the distribution chain of foods, ranging from producers all the way to the final consumers.

Biography :

Email: jkiddnj@hotmail.com