Noodle is a type of pasta, mainly composed of wheat flour (WF), widely consumed due to its easy preparation. Recently, there has been a growing concern in the food industry about nutritionally enriched processed wheat products, and the analytical methods used to characterize these products. We implemented a computer vision system (CVS) using image analysis and prediction algorithms, to predict three different components in pasta: hydrolyzed soy protein (HSP), fructo-oligosaccharide (FOS), and WF. Pasta samples used in the experiments were produced with 12 different combinations of these components, varying the amounts of HSP, FOS, and WF. Microscopy images of samples were acquired, preprocessed, and segmented to extract image features. We investigated 56 image features from four types (color, intensity, texture, and border) along with four machine learning algorithms (gradient boost machine, multilayer perceptron artificial neural network, support vector machine, and random forest) and partial least-squares to predict the quantity of noodle components. Accurate results were obtained for HSP and WF, with coefficient of regression (R-2) of 0.82 and 0.75, and root mean square error (RMSE) of 0.12 and 0.15, respectively. On the other hand, FOS was not accurately identified (R-2 = 0.39, RMSE = 0.21). The results support the potential application of CVS in the processing industry for noodle production. (C) 2018 SPIE and IS&T
