Chemometrics fuzzy autocatalytic set method for halal authentication of gelatin
Fourier transform infrared (FTIR) spectroscopy is one of the established techniques in food adulteration analysis. The issue of halal authenticity of food and pharmaceutical products has become a concern among Muslims due to fraud and unknown sources of ingredients. The most common non-halal ingredi...
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| 主要作者: | |
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2021
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| 主題: | |
| 在線閱讀: | http://eprints.utm.my/id/eprint/101944/1/NurfarhanaHassanPFS2021.pdf |
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| 總結: | Fourier transform infrared (FTIR) spectroscopy is one of the established techniques in food adulteration analysis. The issue of halal authenticity of food and pharmaceutical products has become a concern among Muslims due to fraud and unknown sources of ingredients. The most common non-halal ingredient found in some food and pharmaceuticals products is porcine gelatin. In addition, there are other sources of gelatin such as bovine and fish. Fuzzy autocatalytic set (FACS) is a concept that incorporates fuzziness and autocatalytic set. The study related to the characteristics and uses of FACS method is progressing significantly, especially in the area of chemical engineering and also signal and network analysis. However, in this research, FACS is implemented on FTIR spectra of bovine, porcine, and fish gelatins since their spectra exhibit very similar patterns and thus further analysis is required to differentiate them. The spectra of the gelatin are analyzed using a new developed chemometrics technique, namely chemometrics FACS (c-FACS). Several definitions and theorems are introduced to furnish the c-FACS technique. A coded algorithm called Multisystem Dynamic Identification of Gelatin Sources is built for the technique. The algorithm is executed on the gelatins’ spectra. The results showed that each gelatin has a unique range signature of wavenumbers and porcine gelatin particularly showed distinct pattern compared to others. The efficiency and the rigorousness of the c-FACS are established when compared to the principal component analysis (PCA) method. |
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