Towards authentication of beef, chicken and lard using micro near-infrared spectrometer based on support vector machine classification
NIR (near infrared) spectrometer utilized a quick reliable mean of molecular chemical detection. In this paper, we propose a method on authenticating fats originated from beef, chicken and lard. These compositions can be identified by NIR spectrometers through qualitative and quantitative analysis...
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Asian Research Publishing Network (ARPN)
2016
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| Online Access: | http://irep.iium.edu.my/51090/ http://irep.iium.edu.my/51090/ http://irep.iium.edu.my/51090/1/jeas_0316_3933.pdf http://irep.iium.edu.my/51090/4/51090_Towards%20authentication%20of%20beef%2C%20chicken%20and%20lard%20using%20micro%20near%C2%ADinfrared_Scopus.pdf |
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iium-510902017-09-05T17:01:04Z http://irep.iium.edu.my/51090/ Towards authentication of beef, chicken and lard using micro near-infrared spectrometer based on support vector machine classification Alfar, Ibrahim J. Khorshidtalab, Aida Akmeliawati, Rini Ahmad, Salmiah Jaswir, Irwandi TA164 Bioengineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices NIR (near infrared) spectrometer utilized a quick reliable mean of molecular chemical detection. In this paper, we propose a method on authenticating fats originated from beef, chicken and lard. These compositions can be identified by NIR spectrometers through qualitative and quantitative analysis. Yet most of the analysis lack the capacity to find a pattern in the spectrums to be used in classification or regression models. The disadvantage of spectrum after all is the inability to show the concentration of fatty acids, yet these fatty acid components are shared by all kinds of fat/oil. Therefore, a new method is proposed to create a clear and a distinguishable pattern for the classification. The spectrum of each group (beef fat, chicken fat and pig fat “lard”) of samples were acquired using a readymade micro-NIR spectrometer. The raw data required further processing before using it in the classifier. These processes including standard normal variant and Savitsky-Golay smoothing. Furthermore, the processed data was classified using support vector machine (SVM) with polynomial kernel. The trained SVM model showed 98.33% accuracy for 10-fold cross validation and 86.67% accuracy for unseen/testing data. For each individual kind of fat the model was able to identify the unseen data satisfactorily as follows lard with 100% accuracy and combined, chicken and beef showed 80% accuracy. Asian Research Publishing Network (ARPN) 2016-03 Article PeerReviewed application/pdf en http://irep.iium.edu.my/51090/1/jeas_0316_3933.pdf application/pdf en http://irep.iium.edu.my/51090/4/51090_Towards%20authentication%20of%20beef%2C%20chicken%20and%20lard%20using%20micro%20near%C2%ADinfrared_Scopus.pdf Alfar, Ibrahim J. and Khorshidtalab, Aida and Akmeliawati, Rini and Ahmad, Salmiah and Jaswir, Irwandi (2016) Towards authentication of beef, chicken and lard using micro near-infrared spectrometer based on support vector machine classification. ARPN Journal of Engineering and Applied Sciences, 11 (6). pp. 4130-4136. ISSN 1819-6608 http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_0316_3933.pdf |
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Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English English |
| topic |
TA164 Bioengineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
| spellingShingle |
TA164 Bioengineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Alfar, Ibrahim J. Khorshidtalab, Aida Akmeliawati, Rini Ahmad, Salmiah Jaswir, Irwandi Towards authentication of beef, chicken and lard using micro near-infrared spectrometer based on support vector machine classification |
| description |
NIR (near infrared) spectrometer utilized a quick reliable mean of molecular chemical detection. In this paper, we
propose a method on authenticating fats originated from beef, chicken and lard. These compositions can be identified by
NIR spectrometers through qualitative and quantitative analysis. Yet most of the analysis lack the capacity to find a pattern
in the spectrums to be used in classification or regression models. The disadvantage of spectrum after all is the inability to
show the concentration of fatty acids, yet these fatty acid components are shared by all kinds of fat/oil. Therefore, a new
method is proposed to create a clear and a distinguishable pattern for the classification. The spectrum of each group (beef
fat, chicken fat and pig fat “lard”) of samples were acquired using a readymade micro-NIR spectrometer. The raw data
required further processing before using it in the classifier. These processes including standard normal variant and Savitsky-Golay smoothing. Furthermore, the processed data was classified using support vector machine (SVM) with polynomial kernel. The trained SVM model showed 98.33% accuracy for 10-fold cross validation and 86.67% accuracy for unseen/testing data. For each individual kind of fat the model was able to identify the unseen data satisfactorily as follows lard with 100% accuracy and combined, chicken and beef showed 80% accuracy. |
| format |
Article |
| author |
Alfar, Ibrahim J. Khorshidtalab, Aida Akmeliawati, Rini Ahmad, Salmiah Jaswir, Irwandi |
| author_facet |
Alfar, Ibrahim J. Khorshidtalab, Aida Akmeliawati, Rini Ahmad, Salmiah Jaswir, Irwandi |
| author_sort |
Alfar, Ibrahim J. |
| title |
Towards authentication of beef, chicken and lard using micro near-infrared spectrometer based on support vector machine classification |
| title_short |
Towards authentication of beef, chicken and lard using micro near-infrared spectrometer based on support vector machine classification |
| title_full |
Towards authentication of beef, chicken and lard using micro near-infrared spectrometer based on support vector machine classification |
| title_fullStr |
Towards authentication of beef, chicken and lard using micro near-infrared spectrometer based on support vector machine classification |
| title_full_unstemmed |
Towards authentication of beef, chicken and lard using micro near-infrared spectrometer based on support vector machine classification |
| title_sort |
towards authentication of beef, chicken and lard using micro near-infrared spectrometer based on support vector machine classification |
| publisher |
Asian Research Publishing Network (ARPN) |
| publishDate |
2016 |
| url |
http://irep.iium.edu.my/51090/ http://irep.iium.edu.my/51090/ http://irep.iium.edu.my/51090/1/jeas_0316_3933.pdf http://irep.iium.edu.my/51090/4/51090_Towards%20authentication%20of%20beef%2C%20chicken%20and%20lard%20using%20micro%20near%C2%ADinfrared_Scopus.pdf |
| first_indexed |
2023-09-18T21:12:17Z |
| last_indexed |
2023-09-18T21:12:17Z |
| _version_ |
1777411332785569792 |