In short, all three works adopt Naive Bayes, SVM or Adaboost as the classification method. The recently popular machine-learning methods such as deep neuron network (DNN) (LeCun et al., 2015), random forest (RF) (Breiman, 2001), and gradient boosting machine (GBM) (Friedman, 2002), frequently manifest the promising performance in the kaggle competition (www.kaggle.com/competitions), but were not used in the bitterant prediction before. In addition, the simple K-nearest neighbors (KNN) method (Itskowitz and Tropsha, 2005), which is generally used as the baseline for the comparison of machine-learning methods, was never applied in the bitterant prediction as well. Moreover, the consensus voting strategy based on the multiple machine-learning methods also was not employed to build the bitterant classification model in the past. Secondly, the previous works make use of the fully or partially hypothetical non-bittterant dataset. Therefore, there is a pressing need to make use of the fully experimental dataset with the relatively large size to develop the consensus model for the bitterant prediction that can be utilized by the food scientists in an easy-to-use and free software.