Predicting furan content in a fried dough system using image analysis

Abstract

The aim of this paper is to test different models for predicting furan content in a dough system, based on partial least squares regression using colour images. Starch dough systems were fried at five temperatures between 150 and 190 °C and for 5, 7, 9, 11 and 13 min. The furan content was quantified using gas chromatography/mass spectrometry, while the corresponding images were simultaneously obtained and processed in order to extract 2914 features. Good furan content predictions were obtained using computer vision image chromatic features using correlation coefficient of prediction (Rp = 0.86). However, the best prediction correlation was obtained using the image textural features (Rp = 0.93), when the number of features was reduced to 10 by algorithms applications. These results suggest that furan content in fried dough systems can be predicted using features of computer vision images.