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Open AccessArticle

Prediction of Pasting Properties of Dough from Mixolab Measurements Using Artificial Neuronal Networks

Faculty of Food Engineering, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
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Foods 2019, 8(10), 447; https://doi.org/10.3390/foods8100447
Received: 16 August 2019 / Revised: 20 September 2019 / Accepted: 26 September 2019 / Published: 1 October 2019
(This article belongs to the Section Food Engineering and Technology)
An artificial neuronal network (ANN) system was conducted to predict the Mixolab parameters which described the wheat flour starch-amylase part (torques C3, C4, C5, and the difference between C3-C4and C5-C4, respectively) from physicochemical properties (wet gluten, gluten deformation index, Falling number, moisture content, water absorption) of 10 different refined wheat flours supplemented bydifferent levels of fungal α-amylase addition. All Mixolab parameters analyzed and the Falling number values were reduced with the increased level of α-amylase addition. The ANN results accurately predicted the Mixolab parameters based on wheat flours physicochemical properties and α-amylase addition. ANN analyses showed that moisture content was the most sensitive parameter in influencing Mixolab maximum torque C3 and the difference between torques C3 and C4, while wet gluten was the most sensitive parameter in influencing minimum torque C4 and the difference between torques C5 and C4, and α-amylase level was the most sensitive parameter in predicting maximum torque C5. It is obvious that the Falling number of all the Mixolab characteristics best predicted the difference between torques C3 and C4. View Full-Text
Keywords: white wheat flour; α-amylase; Mixolab; Falling number; artificial neuronal networks white wheat flour; α-amylase; Mixolab; Falling number; artificial neuronal networks
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Codină, G.G.; Dabija, A.; Oroian, M. Prediction of Pasting Properties of Dough from Mixolab Measurements Using Artificial Neuronal Networks. Foods 2019, 8, 447.

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