基于电子鼻的牛肉和猪肉香气分类主成分分析方法

Imam Tazi, N. Isnaini, M. Mutmainnah, Avin Ainur
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引用次数: 7

摘要

食用肉类产品有几个测试过程。感官评价是一种基于颜色、质地、气味和味觉的评价。本研究旨在找出电子鼻中包含的10个气体传感器阵列对基于气味的牛肉和猪肉气味模式的响应模式。使用的分类方法是使用主成分分析(PCA)方法。预计该方法将简化以香气为基础的牛肉和猪肉的差异检测。使用的肉类是超市里出售的标准牛肉和猪肉。然后将牛肉和猪肉的样品研磨至光滑。之后,称量到1盎司。肉类样本使用由10个气体传感器组成的电子鼻进行测试。采用多变量分析方法对牛肉和猪肉的香气进行分类。数据处理结果表明,利用电子鼻对牛肉和猪肉进行香气分类是完美的。基于PCA方法,PC1的比例为93.4%,PC2的比例为4.9%。从第二个累积数,得到第一个PC的值为98.3%。此值表示只能用2维数据,可以表示10维数据。加载图显示,MQ-138和MQ-3传感器是测试牛肉和猪肉样品中最强大的传感器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Principal Component Analysis (PCA) Method for Classification of Beef and Pork Aroma Based on Electronic Nose
There are several testing processes for consuming meat products. Organoleptic evaluation is an evaluation based on color, texture, smell, and taste. This research aims to find out the response pattern of 10 gas sensor array contained in the electronic nose against the odor pattern of beef and pork base on a smell. The classification method used is using the Principal Component Analysis (PCA) method. This method is expected to simplify the test of differences in beef and pork based on the aroma. The meat used is standard beef and pork consumption that has been sold in supermarkets. Samples of beef and pork are then ground until smooth. After that, it is weighed until it reaches 1 ounce. The meat samples were tested using an electronic nose consisting of 10 gas sensors. The multivariate analysis method was used to classify the aroma of beef and pork. The results of the data processing showed that the aroma classification of beef and pork which was indexed by the electronic nose was perfect. Based on the PCA method, the proportion of PC1 is 93.4%, and PC2 is 4.9%. From the second cumulative number, the value of the first PC was obtained 98.3%. This value indicates that only with 2-dimensional data, can represent ten dimensions of data. The loading plot shows that the MQ-138 and MQ-3 sensors are the most powerful sensors in testing samples of beef and pork.
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