Computer vision system for characterization of pasta (noodle) composition

Autor(es)

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

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF ELECTRONIC IMAGING; v. 27, n. 5 SEP 2018.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 15/24351-2 - Análise de imagens e espectroscopia de infravermelho próximo (NIR) na avaliação de qualidade e autenticação de alimentos
• Beneficiário: Douglas Fernandes Barbin
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores