Application Of Shannon’s Entropy-Analytic Hierarchy Process (AHP) For The Selection Of The Most Suitable Starch As Matrix In Green Biocomposites For Takeout Food Packaging Design
Starch is a natural polymer and eligible for short-term, single-use food packaging applications. Nevertheless, different starches have different features and properties determined by their botanical plant origins. This paper presents an approach that combines Shannon’s entropy and the Analytic Hiera...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
NC State University
2020
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| Online Access: | http://eprints.utem.edu.my/id/eprint/25082/2/16657-63523-1-PB.PDF http://eprints.utem.edu.my/id/eprint/25082/ https://ojs.cnr.ncsu.edu/index.php/BioRes/article/view/BioRes_15_2_4065_Salwa_Shannon_Entropy_Hierarchy/7659 |
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| Summary: | Starch is a natural polymer and eligible for short-term, single-use food packaging applications. Nevertheless, different starches have different features and properties determined by their botanical plant origins. This paper presents an approach that combines Shannon’s entropy and the Analytic Hierarchy Process method to aid the selection process of starch as matrix in green biocomposites for takeout food packaging design. The proposed selection system ranks alternative starches in terms of the key design elements, i.e. strength, barrier property, weight, and cost. Shannon’s entropy established corresponding weight values for the indicators selected. Six starches: wheat, maize, potato, cassava, sago, and rice were appraised using gathered data from the literature to determine their suitability as a more sustainable option. This study found that sago starch obtained the highest priority score of 26.8%, followed by rice starch (20.2%). Sensitivity analysis was then carried out to further verify the results; sago starch was at the top rank for five of six different scenarios tested. The results showed that sago starch is the starch that can best satisfy the design requirements. Despite the results attained, the selection framework used could be enhanced with a more comprehensive attributes assessment and extensive dataset. |
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