Application of machine learning approach on halal meat authentication principle, challenges, and prospects: a review
Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Becaus...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/115428/1/115428.pdf |
| _version_ | 1825940665474744320 |
|---|---|
| author | Mustapha, Abdul Ishak, Iskandar Zaki, Nor Nadiha Mohd Ismail-Fitry, Mohammad Rashedi Arshad, Syariena Sazili, Awis Qurni |
| author_facet | Mustapha, Abdul Ishak, Iskandar Zaki, Nor Nadiha Mohd Ismail-Fitry, Mohammad Rashedi Arshad, Syariena Sazili, Awis Qurni |
| author_sort | Mustapha, Abdul |
| collection | UPM |
| description | Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat. |
| first_indexed | 2025-03-07T13:05:15Z |
| format | Article |
| id | upm.eprints-115428 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2025-03-07T13:05:15Z |
| publishDate | 2024 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-1154282025-03-04T04:43:36Z http://psasir.upm.edu.my/id/eprint/115428/ Application of machine learning approach on halal meat authentication principle, challenges, and prospects: a review Mustapha, Abdul Ishak, Iskandar Zaki, Nor Nadiha Mohd Ismail-Fitry, Mohammad Rashedi Arshad, Syariena Sazili, Awis Qurni Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat. Elsevier 2024-06-30 Article PeerReviewed text en cc_by_nc_nd_4 http://psasir.upm.edu.my/id/eprint/115428/1/115428.pdf Mustapha, Abdul and Ishak, Iskandar and Zaki, Nor Nadiha Mohd and Ismail-Fitry, Mohammad Rashedi and Arshad, Syariena and Sazili, Awis Qurni (2024) Application of machine learning approach on halal meat authentication principle, challenges, and prospects: a review. Heliyon, 10 (12). art. no. e32189. pp. 1-13. ISSN 2405-8440 https://www.sciencedirect.com/science/article/pii/S2405844024082203?via%3Dihub 10.1016/j.heliyon.2024.e32189 |
| spellingShingle | Mustapha, Abdul Ishak, Iskandar Zaki, Nor Nadiha Mohd Ismail-Fitry, Mohammad Rashedi Arshad, Syariena Sazili, Awis Qurni Application of machine learning approach on halal meat authentication principle, challenges, and prospects: a review |
| title | Application of machine learning approach on halal meat authentication principle, challenges, and prospects: a review |
| title_full | Application of machine learning approach on halal meat authentication principle, challenges, and prospects: a review |
| title_fullStr | Application of machine learning approach on halal meat authentication principle, challenges, and prospects: a review |
| title_full_unstemmed | Application of machine learning approach on halal meat authentication principle, challenges, and prospects: a review |
| title_short | Application of machine learning approach on halal meat authentication principle, challenges, and prospects: a review |
| title_sort | application of machine learning approach on halal meat authentication principle challenges and prospects a review |
| url | http://psasir.upm.edu.my/id/eprint/115428/1/115428.pdf |
| work_keys_str_mv | AT mustaphaabdul applicationofmachinelearningapproachonhalalmeatauthenticationprinciplechallengesandprospectsareview AT ishakiskandar applicationofmachinelearningapproachonhalalmeatauthenticationprinciplechallengesandprospectsareview AT zakinornadihamohd applicationofmachinelearningapproachonhalalmeatauthenticationprinciplechallengesandprospectsareview AT ismailfitrymohammadrashedi applicationofmachinelearningapproachonhalalmeatauthenticationprinciplechallengesandprospectsareview AT arshadsyariena applicationofmachinelearningapproachonhalalmeatauthenticationprinciplechallengesandprospectsareview AT saziliawisqurni applicationofmachinelearningapproachonhalalmeatauthenticationprinciplechallengesandprospectsareview |