Appetite ratings of foods are predictable with an in vitro advanced gastrointestinal model in combination with an in silico artificial neural network
Appetite ratings of foods are predictable with an in vitro advanced gastrointestinal model in combination with an in silico artificial neural network
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DOI：
10.1016/j.foodres.2019.03.051
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发表时间：
2019-08-01
影响因子：
8.1
通讯作者：
Havenaar，Robert
中科院分区：
文献类型：
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作者：
Bellmann，Susann;Krishnan，Shaji;Havenaar，Robert
全球肥胖患病率的预期增加使得有必要了解膳食摄入量对食欲的影响的信息。由于人体临床研究需要大量时间和成本，因此需要一种可靠的替代方案。本研究的目的是开发和评估体外计算机模拟技术，以预测食用不同类型膳食后的饱腹感和饥饿感。在这项技术中，体外胃肠道模型 (tiny-TIMagc) 关于不同膳食的胃粘度和肠道消化的结果被用作硅人工神经网络 (ANN) 的输入数据。将饱腹感和饥饿感的预测与摄入相同类型膳食后这些参数的实际人类得分进行比较。从这些第一系列的实验中，使用相对少量的体外消化参数作为硅模型的输入，可以以时间和成本有效的方式实现对食物食欲评级的合理预测。
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 anin vitro-in silicotechnology to predict the feelings of fullness and hunger after consumption of different types of meals. In this technology the results from anin vitrogastrointestinal model (tiny-TIMagc) on gastric viscosity and intestinal digestion of different meals were used as input data for anin silicoartificial 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 ofin vitrodigestive parameters as input forin silicomodeling, a reasonable prediction of appetite rating for foods can be realized at a time- and cost-effective way.