Browsing by Author "Mariotti, Maria"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Furan Occurrence in Starchy Food Model Systems Processed at High Temperatures: Effect of Ascorbic Acid and Heating Conditions
    (AMER CHEMICAL SOC, 2012) Mariotti, Maria; Granby, Kit; Fromberg, Arvid; Risum, Jorgen; Agosin, Eduardo; Pedreschi, Franco
    Furan, a potential carcinogen, has been detected in highly consumed starchy foods, such as bread and snacks; however, research on furan generation in these food matrixes has not been undertaken, thus far. The present study explored the effect of ascorbic acid addition and cooking methods (frying and baking) over furan occurrence and its relation with the non-enzymatic browning in a wheat flour starchy food model system. Results showed that furan generation significantly increased in the presence of ascorbic acid after 7 mm of heating (p < 0.05). The strongest effect was observed for baked products. Additionally, the furan content in fried products increased with the increase of the oil uptake levels. As for Mallard reactions, in general, the furan level in all samples linearly correlated with their degree of non-enzymatic browning, represented by L* and a* color parameters (e.g., wheat flour baked samples showed a R-2 of 0.88 and 0.87 for L* and a*, respectively), when the sample moisture content decreased during heating.
  • No Thumbnail Available
    Item
    Statistical pattern recognition classification with computer vision images for assessing the furan content of fried dough pieces
    (2018) Leiva-Valenzuela, Gabriel A.; Mariotti, Maria; Mondragon, German; Pedreschi, Franco
    This research tested furan classification models in fried matrices based on the pattern recognition of images. Samples were fried at 150, 160, 170, 180, and 190 degrees C for 5, 7, 9, 11, 13, and 30 min. Furan was measured by GC-MS. Corresponding images were acquired and processed to extract 2175 chromatic and textural features. Principal component analysis was used to reduce features to 8-12 principal components. In parallel, sequential forward selection coupled with linear discriminant analysis (LDA) was the best strategy to select only 5-7 features. LDA was the best classifier with 91.39-97.60% recognizing above 113 mu g/kg and 69.54-83.80% to classify images from class 1 (0-38 mu g/kg) from class 2 (39-113 mu g/kg). Also, support vector machine recognized 87.71-96.74% of class 3 (114-398 mu g/kg) from class 4 (399-646 mg/kg). The technique may be used to detect high amount of furan in fried starchy matrices. (C) 2017 Elsevier Ltd. All rights reserved.