A fast and non-invasive imaging procedure to fight red tuna fraud

Recently, a food fraud regarding tuna samples has been detected in Europe. Some batches of tuna treated with illegal adulterants like vegetable extracts, including beetroot, were detected after several cases of food poisoning. This paper proposes a new fast and non-destructive technique based on smartphone imaging which contributes to the fight against tuna fraud. The color of different samples of three species of tuna (Thunnus thynnus, Thunnus albacares and Thunnus obesus) has been analyzed by a colorimetrically characterized smartphone. Descriptive analysis of the samples shows that different species of tuna can be grouped by their color in CIELAB space, and that samples adulterated with beetroot extracts can be detected by the proposed image processing procedure. A logistic regression model was implemented to distinguish between real and fraudulent samples, and its suitability checked by k-folding cross validation (n = 189), achieving a 0.59% false positive rate with a 10% false negative rate.