基于电子鼻的咖啡烘焙等级分类

D. Lelono, Lutfi Satrio Adi, Andi Dharmawan, J. E. Istiyanto, Moh. Idham Ananta Timur
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引用次数: 0

摘要

咖啡豆在做饮料前必须经过烘烤。而咖啡的味道很大程度上取决于咖啡豆的质量和烘焙效果。到目前为止,测试咖啡的香气仍然是用他们领域的专家的眼睛、舌头和鼻子。电子鼻是一种模仿人类气味的装置。该仪器可以根据烘焙程度对咖啡的香气进行分类,这是一种常用的非主观方法。四种阿拉比卡咖啡豆的烘焙水平,分别是绿色、浅色、中等和深色,被用作电子鼻的输入。10个气体传感器作为检测系统,每个样品的数据采集由一个周期组成,其中包括5个采集阶段。在对数据集进行预处理和特征提取后,使用主成分分析(PCA)和k近邻分析(KNN)进行分析。结果表明,KNN方法对样本的最佳K值为K=5,系统性能评价将测试数据和训练数据分成5个部分,正确率为67.5%,精密度为70.22%,召回率为67.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of the Coffee Roasting Level Based on Electronic Nose
Coffee beans must be roasted before serving for drinks. While the taste of coffee is largely determined by the quality and results of the roasted beans. So far, testing the aroma of coffee is still using the eyes, tongue and nose of people who are experts in their fields. Electronic nose exists as a device with the design to imitate human smell. This instrument can be used classify coffee's aroma based on the roasting level that is commonly used as a non-subjective method. Four types of Arabica coffee bean roasting level which are green, light, medium, and dark are used as an input to the electronic nose. Ten gas sensors as detector system, and the data acquisition consist of one cycle per sample which includes five phases of collecting phase. After pre-processing and feature extraction has been done to the data set, analysis is carried out using Principal Component Analysis (PCA) and K-Nearest Neighbour (KNN). The Results show the best K value of the KNN method for the sample is K=5, a system performance evaluation shows the test data and training data into 5-fold with an accuracy value of 67.5%, a precision value of 70.22%, and a recall value of 67.5%.
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