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Principal Component Analysis (PCA) Method for Classification of Beef and Pork Aroma Based on Electronic Nose

Indonesian Journal of Halal Research · Vol. 1 No. 1 (2019) · hlm. 5–8

DOI
10.15575/ijhar.v1i1.4155
ISSN
2657-0165, 2656-3754
Terbit
2019-02-28
Penerbit
UIN Sunan Gunung Djati Bandung
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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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  • Universitas Islam Negeri Maulana Malik Ibrahim
  • Universitas Halu Oleo
  • National Research and Innovation Agency
  • IPB University
  • Khairun University
  • Indonesia Open University
  • Hasanuddin University
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
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Rekan penulisOrganisasiArtikel bersama
Muthmainnah MuthmainnahUniversitas Islam Negeri Maulana Malik Ibrahim2
La NalefoAfiliasi tidak diketahui2
Sitti Nur IsnianAfiliasi tidak diketahui2
Dedi RianaUniversitas Islam Negeri Maulana Malik Ibrahim1
Mohamad SyahadiNational Research and Innovation Agency1
Wiwis SasmitaninghidayahUniversitas Islam Negeri Maulana Malik Ibrahim1
Lia ApriliaNational Research and Innovation Agency1
Wildan Panji TresnaNational Research and Innovation Agency1
Iva Khuzaini KhasanahUniversitas Islam Negeri Maulana Malik Ibrahim1
Farid Samsu HanantoUniversitas Islam Negeri Maulana Malik Ibrahim1
Arista RomadaniUniversitas Islam Negeri Maulana Malik Ibrahim1
Agus MulyonoUniversitas Islam Negeri Maulana Malik Ibrahim1
Mokhamad TironoUniversitas Islam Negeri Maulana Malik Ibrahim1
Siti Belinda AmriAfiliasi tidak diketahui1
Masykur KimsanAfiliasi tidak diketahui1
santi santiAfiliasi tidak diketahui1
Rosmawaty RosmawatyAfiliasi tidak diketahui1
Mardin MardinAfiliasi tidak diketahui1
Ramis RaufKhairun University1
Afriani UlyaUniversitas Halu Oleo1
Fardan Rezkiawan FaidaUniversitas Halu Oleo1
Eko HariantoIndonesia Open University1
Muthmainnah MuthmainnahUniversitas Halu Oleo1
Khoirul AiniUniversitas Islam Negeri Maulana Malik Ibrahim1
Ninik ChamidahUniversitas Islam Negeri Maulana Malik Ibrahim1
Kusairi KusairiUniversitas Islam Negeri Maulana Malik Ibrahim1
Mutmainnah MutmainnahHasanuddin University1
Desniar DesniarIPB University1
Joko SantosoIPB University1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.uinsgd.ac.id/index.php/ijhar/oai, pertama di-harvest 7 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Principal Component Analysis (PCA) Method for Classification of Beef and Pork Aroma Based on Electronic Nose</dc:title>
	<dc:creator>Tazi, Imam</dc:creator>
	<dc:creator>Isnaini, Nur Laila</dc:creator>
	<dc:creator>Mutmainnah, Mutmainnah</dc:creator>
	<dc:creator>Ainur, Avin</dc:creator>
	<dc:subject xml:lang="en-US">Electronic Nose</dc:subject>
	<dc:subject xml:lang="en-US">Array Sensor</dc:subject>
	<dc:subject xml:lang="en-US">Classification</dc:subject>
	<dc:subject xml:lang="en-US">PCA</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">UIN Sunan Gunung Djati Bandung</dc:publisher>
	<dc:date>2019-02-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.uinsgd.ac.id/index.php/ijhar/article/view/4155</dc:identifier>
	<dc:identifier>10.15575/ijhar.v1i1.4155</dc:identifier>
	<dc:source xml:lang="en-US">Indonesian Journal of Halal Research; Vol. 1 No. 1 (2019): February; 5-8</dc:source>
	<dc:source>2657-0165</dc:source>
	<dc:source>2656-3754</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.uinsgd.ac.id/index.php/ijhar/article/view/4155/2537</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 Indonesian Journal of Halal Research</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>