CHEMICAL ENGINEERING TRANSACTIONS VOL. 75, 2019 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Sauro Pierucci, Laura Piazza Copyright © 2019, AIDIC Servizi S.r.l. ISBN 978-88-95608-72-3; ISSN 2283-9216 Mass Transfer Modeling Phenomena at Breakfast Cereal Hydrated with Lactose-free Milk by Artificial Neural Network, Empirical Models and Response Surface Andréia Ibiapina a , Eduardo H. de Oliveira b , Camila M. S. Soares a , Aynaran O. de Aguiar a , Warley G. da Silva a , Tarso da C. Alvim a , Antonio A.de Melo Filho c , Glêndara A. S. Martins a a Laboratory of Konetic and Process Modeling, Federal University of Tocantins, PPGCTA/ UFT, Palmas, TO, Brazil; b Academic course in food engineering, Federal University of Tocantins, Palmas, TO, Brazil; c Post-Graduate in Chemistry Program, Center for Research and Post-Graduate in Science and Technology, Federal University of Roraima, PPGQ/NPPGCT/UFRR, Boa Vista, RR, Brazil. Breakfast cereals are extruded products and have crispness, usually, it is consumed in the hydrated form with milk which can cause great absorption of humidity and alteration of its characteristics. In order to study the hydration kinetics above the characteristics of the cereal, a 2³ design, having as independent variables time, temperature and the proportion of hydration. The data were treated using the response surface methodology, empirical models and artificial neural network. Physicochemical analyses were performed during hydration to monitor and analyze possible changes at milk and cereal composition. The processing conditions caused a significant effect (<0.05) on moisture and ash analysis at cereals and ashes and reducing sugars in milk. When compared to empirical models of the literature, the neural network presented better adjustments and greater capacity to predict the response variables behavior (R 2 = 0.97). Keywords: Kinetics, Mass transference, Hydrolysis. 1. Introduction Taking into account the importance of milk consumption and the lactose intolerance that many people have, the production of reduced lactose content or lactose free products has started (Feijoo et al., 2017). Breakfast cereals are extracted from ultra-processed foods, long consumed. They are highly nutritious being mainly maize with or without the addition of other ingredients during processing (Siqueira et al., 2018). Grains and cereals humidification depends to variables such as temperature and initial water content. The water content variation in grains and cereals on a certain period of hydration can be used to describe the behavior of hydration data through mathematical modeling using empirical or phenomenological models, which allows simulating the parameters and processes behavioral (Balbinoti et al., 2018). The artificial neural network is a computational model composed of simple processing elements (artificial neurons) which apply a certain mathematical function to the data (activation function) generating a unique response (Binoti et al., 2014). The objective of this work was to study the kinetics of morning cereal hydration with skimmed and hydrolyzed milk for quality loss analysis physicochemical during the mass transfer process and determination of the best behavior prediction model. 2. Material and methods The milk used was of the skimmed type, purchased in local commerce, in its bottled form and ready for consumption. Commercial β-galactosidase (lactase) enzymes from Kluyveromyces lactis yeasts (Maxilact® LX-5000), according to the legislation specification (Brazil, 2003). For the hydrolysis process, 1% of the enzyme was used over a period of 90 minutes at a temperature of 37 °C. DOI: 10.3303/CET1975085 Paper Received: 21 June 2018; Revised: 23 September 2018; Accepted: 4 February 2019 Please cite this article as: Ibiapina A., Silva De Oliveira E.H., Da Silva Soares C.M., Oliveira De Aguiar A., Da Silva W.G., Da Costa Alvim T., De Melo Filho A.A., Souza Martins G.A., 2019, Mass Transfer Modeling Phenomena at Breakfast Cereal Hydrated with Lactose-free Milk by Artificial Neural Network, Empirical Models and Response Surface , Chemical Engineering Transactions, 75, 505-510 DOI:10.3303/CET1975085 505