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Statistical pattern recognition classification with computer vision images for assessing the furan content of fried dough pieces.
- Source :
-
Food chemistry [Food Chem] 2018 Jan 15; Vol. 239, pp. 718-725. Date of Electronic Publication: 2017 Jun 19. - Publication Year :
- 2018
-
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.<br /> (Copyright © 2017 Elsevier Ltd. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1873-7072
- Volume :
- 239
- Database :
- MEDLINE
- Journal :
- Food chemistry
- Publication Type :
- Academic Journal
- Accession number :
- 28873627
- Full Text :
- https://doi.org/10.1016/j.foodchem.2017.06.095