Optimized Neural Network for Instant Coffee Classification through an Electronic Nose
作者: Evandro BonaRui Sérgio dos Santos Ferreira da SilvaDionísio BorsatoDenisley Gentil Bassoli
刊名: International Journal of Food Engineering, 2011, Vol.7 (6)
来源数据库: Berkeley Electronic Journal
关键词: multilayer perceptronbootstrapensemble averagesequential simplex optimizationdesirability functions
英文摘要: Flavor is one of the most important features of food, especially of coffee. The evaluation of this sensory feature is complex yet indispensable in quality control of instant coffees. In this work, an artificial neural network (ANN) was developed for instant coffee classification based on an electronic nose (EN) aroma profile. To this purpose, a hybrid algorithm was developed, containing: bootstrap resample methodology; factorial design and sequential simplex optimization to tune network parameters; an ensemble multilayer perceptron (MLP) trained with backpropagation for coffee classification; and causal index procedure for knowledge extraction from the trained ANN. The produced neural network classifier correctly recognizes 100% of coffees studied. Furthermore, the causal index employment...
全文获取路径: BE Press  (合作)
影响因子:0.463 (2011)

  • instant 瞬间
  • desirability 称心
  • causal 因果
  • methodology 方法学
  • employment 雇用
  • bootstrap 靴绊
  • through 经过
  • purpose 目的
  • perceptron 感知器感知机
  • ensemble