咖啡风味是消费者在品鉴和选择咖啡时考虑的主要因素之一。传统且常见的咖啡香气评估方法是基于昂贵的设备或通过人工感官进行评判。这些方法耗时、成本高且需要训练有素的评估人员。因此,我们提出一种低成本、便携式的咖啡识别方法,结合改进卷积神经网络(convolutional neural network,CNN)对5种不同品种的咖啡进行识别分类。咖啡气味通过电子鼻采集,设计具有三层卷积层的改进CNN分类模型,使用Leaky-ReLU和Dropout模型优化技术,相较于常用的CNN模型性能有一定的提升。与其他多种用于机器嗅觉的模型作对比,实验结果表明,改进CNN模型对咖啡五分类问题准确率达到84.80%,该模型能够对咖啡实现有效的分类。

Coffee flavor is one of the main factors consumers consider when tasting and choosing coffee. Traditional and common methods of assessing coffee aroma are based on expensive equipment or artificial senses. These methods are time-consuming, costly, and require trained evaluators. Therefore, we propose a low-cost and portable coffee recognition method that combines an improved convolutional neural network (CNN) to recognize and classify five different varieties of coffee. The coffee smell is collected through the electronic nose, and an improved CNN classification model with three convolution layers was designed using Leaky-ReLU and Dropout model optimization techniques, which has improved performance compared to commonly used CNN models. Compared with other models used for machine olfaction, the experimental results show that the accuracy of improved CNN model for coffee five classification can reach 84.80%, and the model can effectively classify coffee.

TS273

国家自然科学基金(61571140)。