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In Journal of the science of food and agriculture

BACKGROUND : Specialty coffee fascinates people with its bountiful flavors. Currently, flavor descriptions of specialty coffee beans are only offered by certified coffee cuppers. However, such professionals are rare, and the market demand is tremendous. The hypothesis of this study was to investigate the feasibility to train machine learning (ML) and deep learning (DL) models for predicting the flavors of specialty coffee using near-infrared spectra of ground coffee as the input. Successful model development would provide a new and objective framework to predict complex flavors in food and beverage products.

RESULTS : In predicting seven categories of coffee flavors, the developed models using the ML method (i.e., support vector machine) and the deep convolutional neural network (DCNN) achieved similar performance with the recall and accuracy of 70-73% and 75-77%, respectively. Through the proposed visualization method - a focusing plot, the potential correlation among the highly-weighted spectral region of the DCNN model, the predicted flavor categories, and the corresponding chemical composition was presented.

CONCLUSION : This study has proven the feasibility to apply ML and DL methods on the near-infrared spectra of ground coffee to predict specialty coffee flavors. The effective models provided moderate prediction for seven flavor categories based on 266 samples. The results of classification and visualization indicate that the developed DCNN model is a promising and explainable method for coffee flavor prediction. This article is protected by copyright. All rights reserved.

Chang Yu-Tang, Hsueh Meng-Chien, Hung Shu-Pin, Lu Juin-Ming, Peng Jia-Hung, Chen Shih-Fang


Flavor prediction, convolutional neural network, near-infrared spectroscopy, random forest, support vector machine, visualization