Automated Prediction of Brewing Methods for Coffee Flavors by Multiple Linear Regression, Support Vector Regression, Random Forest, and Neural Network
- Resource Type
- Conference
- Authors
- Xue, Qiao; Bie, Yongjing; Lin, Pengxiao; Wu, Jeffery; Lv, Yixiao; Chen, Yan Ping; Liu, Yuan
- Source
- 2023 2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR) AIHCIR Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR), 2023 2nd International Conference on. :390-394 Dec, 2023
- Subject
- Computing and Processing
Temperature measurement
Support vector machines
Human computer interaction
Liquids
Linear regression
Artificial neural networks
Predictive models
coffee brewingt
componentst
flavort
machine-learningt
GC/MS
- Language
Many studies have been done to identify the correlation between volatile components in brewed coffee liquid and different coffee brewing parameters and methods, but few are focusing on taste and non-volatile components. Our work selected 15 non-volatile taste-determining chemical components in brewed Arabica coffee under different brewing parameters (water temperature and water/coffee ratio) and quantitatively measured the components using GCMS. The taste activity values (TAVs) of 15 chemicals are calculated, among which 5 chemicals with TAV> 1 are used to train machine learning (ML) models. Among these models, Neural Network (NN) and Random Forest perform the best. And NN is used to establish the relationship between TAV of flavor-determining chemical components and the flavor of brewed coffee.