Rule-Based Discovery Technique for Ingredient Aware Mobile Application

  • Mohamad Afendee Mohamed Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, Besut 22200, Terengganu, Malaysia
  • Mohd Khalid Awang Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, Besut 22200, Terengganu, Malaysia
  • Mohd Isa Awang Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, Besut 22200, Terengganu, Malaysia
  • Abd Rasid Mamat Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, Besut 22200, Terengganu, Malaysia

Abstract

Food additives may come from natural and chemical sources. In some countries, this ingredient is coded into e-numbering system. E-Number identifies the additive substance, and it can be used to determine its possible sources hence the halal status and its value for health. However the use of scientific name or the coded number is confusing to consumers. This article presents an android-based mobile application that provides a database access to the detailed information about the additives. Information retrieval is done based on rule-based technique. The application also offers customer profiling services whereby upon user registration and sharing current health conditions, the consumer will be provided with extra information on the possible consequences of consuming the food. The system prototype system was analysed for the usability in terms of user satisfactions using System Usability Scale (SUS). The user satisfaction is rated from good to excellent according to SUS score in the range of 70%-80%. The application is expected to increase consumers’ awareness of choosing the right food that is halal and healthy.

References

Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the system usability scale. Intl. Journal of Human–Computer Interaction, 24(6), 574-594.

Bangor, A., Kortum, P., & Miller, J. (2009). Determining what individual SUS scores mean: Adding an adjective rating scale. Journal of Usability Studies, 4(3), 114-123.

Bevan, N. (2001). International standards for HCI and usability. International Journal of Human-Computer Studies, 55(4), 533-552.

Brill, E. (1992). A simple rule-based part of speech tagger. In Proceedings of the workshop on Speech and Natural Language (pp. 112-116). Association for Computational Linguistics.

Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability Evaluation In Industry, 189(194), 4-7.

Caron, F., Vanthienen, J., & Baesens, B. (2013). Comprehensive rule-based compliance checking and risk management with process mining. Decision Support Systems, 54(3), 1357-1369.

Cassino, R., Tucci, M., Vitiello, G., & Francese, R. (2015). Empirical validation of an automatic usability evaluation method. Journal of Visual Languages & Computing, 28, 1-22.

Chamhuri, N and Batt, P J. (2013). Exploring the Factors Influencing Consumers’ Choice of Retail Store When Purchasing Fresh Meat in Malaysia. International Food and Agribusiness Management Review,16(3), 99-122.

Fernandez, A., Abrahão, S., & Insfran, E. (2013). Empirical validation of a usability inspection method for model-driven Web development. Journal of Systems and Software, 86(1), 161-186.

Gray, W. D., & Salzman, M. C. (1998). Damaged merchandise? A review of experiments that compare usability evaluation methods. Human–Computer Interaction, 13(3), 203-261.

Güngörmüş, C., & Kılıç, A. (2012). The safety assessment of food additives by reproductive and developmental toxicity studies. Food Additive, InTech, 31-48.

Harrati, N., Bouchrika, I., Tari, A., & Ladjailia, A. (2016). Exploring user satisfaction for e-learning systems via usage-based metrics and system usability scale analysis. Computers in Human Behavior, 61, 463-471.

Hartson, H. R., Andre, T. S., & Williges, R. C. (2001). Criteria for evaluating usability evaluation methods. International Journal of Human-Computer Interaction, 13(4), 373-410.

Kortum, P. T., & Bangor, A. (2013). Usability ratings for everyday products measured with the System Usability Scale. International Journal of Human-Computer Interaction, 29(2), 67-76.

Laurie C. Dolan, Ray A. Matulka and George A. Burdock. (2010). Naturally Occurring Food Toxins. Toxins 2, 2289-2332.

Man Li, Ke-Xue Zhu, Xiao-Na Guo, Kristof Brijs, and Hui-Ming Zhou. (2014). Natural Additives in Wheat-Based Pasta and Noodle Products: Opportunities for Enhanced Nutritional and Functional Properties. Comprehensive Reviews in Food Science and Food Safety, 13(4), 347–357.

Newcombe, R. (2013). E Numbers and Health Issues, Available from: http://www.exploreenumbers.co.uk/E-Numbers-and-Health-Issues.html.

Pandey, R. M., & Upadhyay, S. K. (2012). Food additive. In Food Additive. InTech.

Rood, J. (2015). Food Additives Linked to Inflammation. Available from: http://www.the-scientist.com/?articles.view/articleNo/42301/title/Food-Additives-Linked-to-Inflammation/.

Valacich, J. S., George, J. F., & Hoffer, J. A. (2015). Essentials of systems analysis and design. Pearson Education.

Vesley, D. (1999). Food Safety: Chemical Agents. In Human Health and the Environment (pp. 137-146). Springer US.

Worsley, A. (2002). Nutrition knowledge and food consumption: Can nutrition knowledge change food behaviour?. Asia Pacific J Clin Nutr, 11, 579–585.

Published
2017-06-30
How to Cite
Mohamed, M. A., Awang, M. K., Awang, M. I., & Mamat, A. R. (2017). Rule-Based Discovery Technique for Ingredient Aware Mobile Application. Malaysian Journal of Applied Sciences, 2(1), 1-10. Retrieved from https://journal.unisza.edu.my/myjas/index.php/myjas/article/view/19
Section
Research Articles