Kaedah Kolorimetri untuk Analisis Kuantitatif Kapsaisin Secara Pencaman Corak Menggunakan Jaringan Neural Tiruan
A quantitative study for capsaicin based on the use of 2,6-dichloro-pbenzoquinone- 4-chlorimide (Gibbs reagent) and artificial neural network (ANN) has been carried out. The characterization include pH optimization, effect of reagent concentration, dynamic range of capsaicin concentration, photo st...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English Malay |
| Published: |
Universiti Putra Malaysia Press
2002
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| Online Access: | http://psasir.upm.edu.my/id/eprint/3834/ http://psasir.upm.edu.my/id/eprint/3834/1/Pages_from_JST_VOL_13_NO._1-6.pdf |
| _version_ | 1848839642443415552 |
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| author | Mat Arip, Mohamad Nasir Ahmad, Musa Mokhtar, Ahmed Mahir Taib, Mohd. Nasir Heng, Lee Yook |
| author_facet | Mat Arip, Mohamad Nasir Ahmad, Musa Mokhtar, Ahmed Mahir Taib, Mohd. Nasir Heng, Lee Yook |
| author_sort | Mat Arip, Mohamad Nasir |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | A quantitative study for capsaicin based on the use of 2,6-dichloro-pbenzoquinone-
4-chlorimide (Gibbs reagent) and artificial neural network (ANN) has been carried out. The characterization include pH optimization, effect of reagent concentration, dynamic range of capsaicin concentration, photo stability,
limit of detection and reproducibility. The optimum response was obtained at pH 11.0 and Gibbs reagent concentration of 2.96 x 104 M. The reproducibility
of the method was very satisfactory with RSD values of 3.55%, 2.44% and 4.52% for capsaicin concentration of 200 ppm, 500 ppm and 800 ppm, respectively.
Photostability test showed that the reagent was very stable with RSD value of 0.013% for the duration of 38 hours. A three layer feed-forward neural network
was used and network training was performed by using back propagation algorithm. For the determination of capsaicin, a neural network with 20 hidden
neurons, 0.00001% learning rate and trained over 47,738 cycles produced the best result. This network was able to extend the narrow dynamic range of
capsaicin from 0 - 200 ppm to 0-600 ppm. The average interpolation error
produced by this network was approximately 0.06 % |
| first_indexed | 2025-11-15T07:14:42Z |
| format | Article |
| id | upm-3834 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English Malay |
| last_indexed | 2025-11-15T07:14:42Z |
| publishDate | 2002 |
| publisher | Universiti Putra Malaysia Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-38342013-05-27T07:11:37Z http://psasir.upm.edu.my/id/eprint/3834/ Kaedah Kolorimetri untuk Analisis Kuantitatif Kapsaisin Secara Pencaman Corak Menggunakan Jaringan Neural Tiruan Mat Arip, Mohamad Nasir Ahmad, Musa Mokhtar, Ahmed Mahir Taib, Mohd. Nasir Heng, Lee Yook A quantitative study for capsaicin based on the use of 2,6-dichloro-pbenzoquinone- 4-chlorimide (Gibbs reagent) and artificial neural network (ANN) has been carried out. The characterization include pH optimization, effect of reagent concentration, dynamic range of capsaicin concentration, photo stability, limit of detection and reproducibility. The optimum response was obtained at pH 11.0 and Gibbs reagent concentration of 2.96 x 104 M. The reproducibility of the method was very satisfactory with RSD values of 3.55%, 2.44% and 4.52% for capsaicin concentration of 200 ppm, 500 ppm and 800 ppm, respectively. Photostability test showed that the reagent was very stable with RSD value of 0.013% for the duration of 38 hours. A three layer feed-forward neural network was used and network training was performed by using back propagation algorithm. For the determination of capsaicin, a neural network with 20 hidden neurons, 0.00001% learning rate and trained over 47,738 cycles produced the best result. This network was able to extend the narrow dynamic range of capsaicin from 0 - 200 ppm to 0-600 ppm. The average interpolation error produced by this network was approximately 0.06 % Universiti Putra Malaysia Press 2002 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/3834/1/Pages_from_JST_VOL_13_NO._1-6.pdf Mat Arip, Mohamad Nasir and Ahmad, Musa and Mokhtar, Ahmed Mahir and Taib, Mohd. Nasir and Heng, Lee Yook (2002) Kaedah Kolorimetri untuk Analisis Kuantitatif Kapsaisin Secara Pencaman Corak Menggunakan Jaringan Neural Tiruan. Pertanika Journal of Science & Technology, 13 (1). pp. 75-86. ISSN 0128-7680 Malay |
| spellingShingle | Mat Arip, Mohamad Nasir Ahmad, Musa Mokhtar, Ahmed Mahir Taib, Mohd. Nasir Heng, Lee Yook Kaedah Kolorimetri untuk Analisis Kuantitatif Kapsaisin Secara Pencaman Corak Menggunakan Jaringan Neural Tiruan |
| title | Kaedah Kolorimetri untuk Analisis Kuantitatif Kapsaisin Secara
Pencaman Corak Menggunakan Jaringan Neural Tiruan |
| title_full | Kaedah Kolorimetri untuk Analisis Kuantitatif Kapsaisin Secara
Pencaman Corak Menggunakan Jaringan Neural Tiruan |
| title_fullStr | Kaedah Kolorimetri untuk Analisis Kuantitatif Kapsaisin Secara
Pencaman Corak Menggunakan Jaringan Neural Tiruan |
| title_full_unstemmed | Kaedah Kolorimetri untuk Analisis Kuantitatif Kapsaisin Secara
Pencaman Corak Menggunakan Jaringan Neural Tiruan |
| title_short | Kaedah Kolorimetri untuk Analisis Kuantitatif Kapsaisin Secara
Pencaman Corak Menggunakan Jaringan Neural Tiruan |
| title_sort | kaedah kolorimetri untuk analisis kuantitatif kapsaisin secara
pencaman corak menggunakan jaringan neural tiruan |
| url | http://psasir.upm.edu.my/id/eprint/3834/ http://psasir.upm.edu.my/id/eprint/3834/1/Pages_from_JST_VOL_13_NO._1-6.pdf |