The E-nose Dataset of base liquor and commercial liquor in our article: A Machine Learning Method for the Quality Detection of Base Liquor and Commercial Liquor Using Multidimensional Signals from an Electronic Nose

Nine types of Chinese liquor (six types of base liquors and three types of commercial liquors) are tested in our study. The six types of base liquors with different aging durations were denoted as BL (year), where BL represents the base liquor, and (year) represents the aging duration. Thus, the six types of base liquors were BL (13), BL (11), BL (8), BL (6), BL (5), and BL (3). The three commercial liquors (CL) were blended using different proportions of the six base liquors, including CL1, CL2, and CL3. The data is acquired by a commercial E-nose (PEN3, Airsense Analytics GmbH, Germany), with 10 MOS sensors in our laboratory.

本研究共测试九款中国白酒，包含六种基酒与三款市售白酒。六种具有不同陈化时长的基酒采用统一命名规则：BL(年份)，其中BL为基酒（base liquor）的缩写，括号内的数字代表陈化时长。因此本次涉及的六种基酒分别为BL(13)、BL(11)、BL(8)、BL(6)、BL(5)与BL(3)。三款市售白酒（commercial liquors，缩写CL）均通过六种基酒按不同比例调配得到，分别为CL1、CL2与CL3。本实验在实验室中采用德国Airsense Analytics GmbH公司生产的PEN3型商用电子鼻采集数据，该设备搭载10个金属氧化物半导体（MOS，Metal Oxide Semiconductor）传感器。

搜集汇总

背景与挑战

背景概述

该数据集包含9种中国白酒（6种不同陈化年份的基酒和3种混合商品酒）的电子鼻传感器数据，用于基于机器学习的质量检测研究。数据通过配备10个金属氧化物半导体传感器的商业电子鼻（PEN3）采集，覆盖模式识别和数据挖掘应用场景。数据集支持白酒质量控制与分类任务，适用于电子鼻信号分析和机器学习模型开发。