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.

中文翻译：

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使用机器和深度学习方法基于近红外光谱预测特色咖啡风味

背景技术精品咖啡以其丰富的风味吸引着人们。目前，特色咖啡豆的风味描述仅由经过认证的咖啡杯测师提供。但是，这样的专业人才很少，市场需求巨大。本研究的假设是研究训练机器学习 (ML) 和深度学习 (DL) 模型以使用研磨咖啡的近红外光谱作为输入来预测精品咖啡风味的可行性。成功的模型开发将提供一个新的客观框架来预测食品和饮料产品中的复杂风味。结果 在预测七类咖啡风味时，使用 ML 方法开发的模型（即，支持向量机）和深度卷积神经网络（DCNN）实现了相似的性能，召回率和准确率分别为 70-73% 和 75-77%。通过所提出的可视化方法——聚焦图，呈现了 DCNN 模型的高加权光谱区域、预测的风味类别和相应的化学成分之间的潜在相关性。结论 本研究证明了将 ML 和 DL 方法应用于研磨咖啡的近红外光谱以预测特色咖啡风味的可行性。有效模型为基于 266 个样本的七种风味类别提供了适度的预测。分类和可视化的结果表明，开发的 DCNN 模型是一种有前景且可解释的咖啡风味预测方法。本文受版权保护。