Smell Classification of Wines by the Learning Vector Quantization Method
Smell Classification of Wines by the Learning Vector Quantization Method
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学习矢量量化方法对葡萄酒的气味分类
DOI：
10.1145/2695664.2695833
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Yoshinori Ikeda
中科院分区：
文献类型：
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作者：
Sigeru Omatu;Mitsuaki Yano;Yoshinori Ikeda
我们考虑了一个白色葡萄酒和红葡萄酒的竞争神经网络的学习矢量量化方法的分类。首先，我们使用金属氧化物半导体气体传感器测量气味数据，该传感器基于氧化和还原过程将气味数据转换为电压。两种葡萄酒，白色葡萄酒和红葡萄酒，分类使用气味数据。由于葡萄酒的气味密度相当薄，我们使用起泡方法使密度水平更高。在这里，我们采用了一种分子筛单阱。通过这种方法，我们获得高浓度水平的葡萄酒的气味数据。在吸收过程之后，我们将石英管的温度从室温升至300摄氏度。利用学习矢量量化方法对两种葡萄酒进行了分类。我们表明，分类准确率为白色葡萄酒是97%左右，红葡萄酒是83.4%左右，分别。
We consider a classification of white wine and red wine by a learning vector quantization method of competitive neural network. First, we measure smell data using metal-oxide semiconductor gas sensors which change smell data into electrical voltages based on oxidation and reduction processes. Two kinds of wines, white wine and red wine, are classified using smell data. Since a smell density of wine is rather thin, we use a bubbling method to make the density level higher. Here, we adopt a mono trap which is a kind of molecular sieves. By this way we obtain smell data of wines of high concentration level. After absorbing process, we take the temperature of a silica tube from a room temperature to 300 degrees Celsius. Using the learning vector quantization method, we classify two kinds of wines. We show that the classification accuracy rate for the white wine is around 97% and that for the red wine is around 83.4%, respectively.