Print Email Facebook Twitter Appetite ratings of foods are predictable with an in vitro advanced gastrointestinal model in combination with an in silico artificial neural network Title Appetite ratings of foods are predictable with an in vitro advanced gastrointestinal model in combination with an in silico artificial neural network Author Bellmann, S. Krishnan, S. de Graaf, A. de Ligt, R.A. Pasman, W.J. Minekus, M. Havenaar, R. Publication year 2019 Abstract The expected increase of global obesity prevalence makes it necessary to have information about the effects of meal intakes on the feeling of appetite. Because human clinical studies are time and cost intensive, there is a need for a reliable alternative. The aim of this study was to develop and evaluate an in vitro-in silico technology to predict the feelings of fullness and hunger after consumption of different types of meals. In this technology the results from an in vitro gastrointestinal model (tiny-TIMagc) on gastric viscosity and intestinal digestion of different meals were used as input data for an in silico artificial neural network (ANN). The predictions of the feeling of fullness and hunger were compared with actual human scores for these parameters after intake of the same type of meals. From these first series of experiments, with a relatively small number of in vitro digestive parameters as input for in silico modeling, a reasonable prediction of appetite rating for foods can be realized at a time- and cost-effective way. © 2019 Elsevier Ltd Subject Food and NutritionHealthy LivingArtificial neural networkGastric viscosityIn silico modelingin vitro gastrointestinal modelPrediction of fullnessPrediction of hungerCost effectivenessNeural networksViscosityAppetiteClinical studyCost effectiveCost-intensiveGastrointestinal modelIn-silico modelsInput datasIntestinal digestionForecasting To reference this document use: http://resolver.tudelft.nl/uuid:c61bb4ea-b683-4663-8bde-83232a35a85f DOI https://doi.org/10.1016/j.foodres.2019.03.051 TNO identifier 866584 ISSN 9639-969 Source Food Research International, 122, 77-86 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.