Serpen Arda, Gökmen Vural
Department of Food Engineering, Hacettepe University, Beytepe, Ankara, Turkey.
Mol Nutr Food Res. 2007 Apr;51(4):383-9. doi: 10.1002/mnfr.200600121.
The artificial neural network (ANN) modeling approach was used to predict acrylamide formation and browning ratio (%) in potato chips as influenced by time x temperature covariants. A series of feed-forward type network models with back-propagation training algorithm were developed. Among various network configurations, 4-5-3-2 configuration was found as the best performing network topology. Four neurons in the input layer were reflecting the asparagine concentration, glucose concentration, frying temperature, and frying time. The output layer had two neurons representing acrylamide concentration and browning ratio of potato chips. The ANN modeling approach was shown to successfully predict acrylamide concentration (R = 0.992) and browning ratio (R = 0.997) of potato chips during frying at different temperatures in time-dependent manner for potatoes having different concentrations of asparagine and glucose. It was concluded that ANN modeling is a useful predictive tool which considers only the input and output variables rather than the complex chemistry.
采用人工神经网络（ANN）建模方法来预测薯片在时间x温度协变量影响下丙烯酰胺的形成和褐变率（%）。开发了一系列采用反向传播训练算法的前馈型网络模型。在各种网络配置中，发现4-5-3-2配置是性能最佳的网络拓扑结构。输入层的四个神经元反映天冬酰胺浓度、葡萄糖浓度、油炸温度和油炸时间。输出层有两个神经元，分别代表薯片的丙烯酰胺浓度和褐变率。结果表明，对于具有不同天冬酰胺和葡萄糖浓度的土豆，ANN建模方法能够以时间依赖的方式成功预测不同温度下油炸过程中薯片的丙烯酰胺浓度（R = 0.992）和褐变率（R = 0.997）。得出的结论是，ANN建模是一种有用的预测工具，它只考虑输入和输出变量，而不考虑复杂的化学反应。
