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Statistical pattern recognition classification with computer vision images for assessing the furan content of fried
Gabriel A Leiva-Valenzuela1, María Mariotti2, Germán Mondragón3
1Department of Chemical and Bioprocess Engineering, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna 4860, Macul, Santiago, Chile.
Abstract:
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°C for 5, 7, 9, 11, 13, and 30min. 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 113µg/kg and 69.54-83.80% to classify images from class 1 (0-38µg/kg) from class 2 (39-113µg/kg). Also, support vector machine recognized 87.71-96.74% of class 3 (114-398µg/kg) from class 4 (399-646µg/kg). The technique may be used to detect high amount of furan in fried starchy matrices.