Principal Component Analysis (PCA) method for classification of beef and pork aroma based on electronic nose

Tazi, Imam (2019) Principal Component Analysis (PCA) method for classification of beef and pork aroma based on electronic nose. Indonesian Journal of Halal Research, 1 (1). pp. 5-8. ISSN 2657-0165

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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 consumption beef and pork that has been sold in supermarkets. The samples of beef and pork are then ground until smooth. After that, it is weighed for about 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 by using only 2-dimensional data, it 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.

Item Type: Journal Article
Keywords: array sensor; classification; electronic nose; PCA
Subjects: 02 PHYSICAL SCIENCES > 0299 Other Physical Sciences > 029904 Synchrotrons; Accelerators; Instruments and Techniques
Divisions: Faculty of Mathematics and Sciences > Department of Physics
Depositing User: Imam Tazi
Date Deposited: 09 Jun 2020 22:12


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