Development and evaluation of roasting degree prediction model of coffee beans by machine learning
Masaki Okamura, Masato Soga, Yasuhiro Yamada, Kazuki Kobata, Daishi Kaneda
Abstract
Coffee beans are roasted to bring out their unique aroma and taste. When a user tries to roast coffee beans by a roasting machine, he/she can use a roasting curve in which temperature is set to the vertical axis and time is set to the horizontal axis. In the roasting of coffee beans, the user needs to select an appropriate roasting curve depending on the desired characteristics. However, it is difficult for him/her to know what characteristics he/she wants to produce and what roasting curve he/she should use to roast the coffee beans to produce the characteristics. Therefore, roasting is a skilful technique performed by experts. In this study, we collected various data related to roasting. Furthermore, we developed a learning model that outputs color information of the resulting beans from the input information on roasting using multiple machine learning algorithms. Finally, we compared and verified the accuracy of the model.