ChemSweet

How to form your own prediction pipeline:

The details of constructing your own workflow are shown in Figure 1 and Figure2. Explanation of workflow:

1) The local model file is read by the Model Reader node above, while the below reads the model-Normalizer.zip file for normalizer;

2) The node of File Reader is used to read the data that needs to be predicted;

3) Convert numbers of label to strings using Number to String node is required in classification models;

4) Select the corresponding prediction node according to the model read by the model reader;

5) Output for the prediction result. In addition, evaluation nodes can be chosen according to your task.

Figure 1. KNIME usage example of classification model.

Figure 2. KNIME usage example of logSw prediction model.

Publication

Zhengfei Yang, Ran Xiao,Guoli Xiong, et al. A novel multi-layer prediction approach for sweetness evaluation based on systematic machine learning modeling. Food Chemistry, 2022, 372: 131249.

Contact

• Dong Jie biomed@csu.edu.cn

• Dongsheng Cao oriental-cds@163.com