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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Computer vision system for characterization of pasta (noodle) composition

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Author(s):
Mastelini, Saulo Martielo [1] ; Alves Sasso, Matheus Gustavo [2] ; Centini Campos, Gabriel Fillipe [1] ; Schmiele, Marcio [3] ; Pedrosa Silva Clerici, Maria Teresa [2] ; Barbin, Douglas Fernandes [2] ; Barbon Jr, Sylvio
Total Authors: 7
Affiliation:
[1] State Univ Londrina UEL, Dept Comp Sci, Londrina - Brazil
[2] State Univ Campinas UNICAMP, Dept Food Engn, Campinas, SP - Brazil
[3] Fed Univ Vales do Jequitinhonha & Mucuri UFVJM, Dept Sci & Technol, Diamantina - Brazil
Total Affiliations: 3
Document type: Journal article
Source: JOURNAL OF ELECTRONIC IMAGING; v. 27, n. 5 SEP 2018.
Web of Science Citations: 0
Abstract

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 (AU)

FAPESP's process: 15/24351-2 - Applications of image analyses and NIR spectroscopy for quality assessment and authentication of food products
Grantee:Douglas Fernandes Barbin
Support type: Research Grants - Young Investigators Grants