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系統識別號 U0026-0309201819514900
論文名稱(中文) 智能化食品安全監控與管理系統設計
論文名稱(英文) Design of Smart Food Safety Monitoring and Management System
校院名稱 成功大學
系所名稱(中) 製造資訊與系統研究所
系所名稱(英) Institute of Manufacturing Information and Systems
學年度 106
學期 2
出版年 107
研究生(中文) 陳姿蓉
研究生(英文) Tzu-Jung Chen
學號 P96054113
學位類別 碩士
語文別 中文
論文頁數 144頁
口試委員 指導教授-陳裕民
共同指導教授-陳宗義
口試委員-陳育仁
中文關鍵字 食品安全  食安監控與管理系統  食安追溯系統  智能系統  資料科學  資料探勘 
英文關鍵字 Food safety  Food safety monitoring and management system  Food safety traceability system  Smart system  Data science  Data mining 
學科別分類
中文摘要 近年來,食安事件層出不窮,不僅造成社會不安、對人民健康造成危害,甚至重創國家形象與經濟發展,故食品安全管理已成為各國之重要政策。隨著資料科學(Data Science)與人工智慧(Artificial Intelligence, AI)的興起,系統智能化的理想已逐漸能夠實現,本研究期盼透過智能化系統的支援,解決食安問題,為人類帶來更多福祉。

本研究以食品安全理念為體,以策略、組織、流程、方法與技術為用,以產品生命週期(Product Life Cycle, PLC)為範圍,依據系統工程「全面性」、PDCA「持續改善」與風險管理「預防」之概念,設計一個「全方位食安管理模式」。針對此「全方位食安管理模式」分析其系統之功能需求,參考資料科學之概念以及人工智慧與機器學習之原理,規劃與設計「智能化食安監控與管理系統」之功能架構。並依該功能架構界定資料分析之需求、設計「資料分析架構」,運用資料探勘(Data Mining)與人工智慧技術,分析影響因子與影響因子之影響模式、進行影響因子整體之趨勢、變因與問題,以建置「資料分析機制」。最後,藉由案例驗證「全方位食品安全管理模式」之可行性與「資料分析架構」之正確性。
英文摘要 In recent years, an endless stream of food safety incidents has emerged, causing social unrest, harming human health, and severely damaging the national image and economic development. Therefore, food safety management has become a crucial policy area for all countries. With the rise of data science and artificial intelligence, the concept of system intelligence has gradually been realized, and we intended to solve the problems of food safety through an intelligent system to thereby promote human welfare.

This study design a smart food safety monitoring and management system, which includes (1) comprehensive food safety management model by system engineering, PDCA and risk management; (2) functional architecture of smart food safety monitoring and management system by comprehensive food safety management model, data science and the principles of artificial intelligence and machine learning; (3) data analysis framework by defined this functional architecture’s requirement of data analysis and using data mining and artificial intelligence technology to establish data analysis mechanism. Finally, give an evaluation and implementation for the result.
論文目次 中文摘要 I
Abstract II
誌謝 VI
目錄 VII
表目錄 X
圖目錄 XI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 問題分析 3
1.5 研究項目與方法 4
1.6 研究步驟 5
第二章 文獻探討 7
2.1 研究領域探討 7
2.1.1 食品安全監控與管理 7
2.1.2 食安追溯系統 8
2.2 相關概念探討 10
2.2.1 資料科學 10
2.2.2 智能 12
2.2.3 系統工程 13
2.2.4 風險管理 14
2.2.5 PDCA 循環 16
2.3 相關技術探討 17
2.3.1 關聯法則 17
2.3.2 類神經網路 19
2.3.3 決策樹 20
2.4 類似之研究 21
第三章 全方位食安管理模式 23
3.1 全方位食品安全模型 23
3.2 全方位食品安全架構 24
3.3 智能食安管理模型 27
3.4 食品安全管理流程 31
第四章 智能化食安監控與管理系統架構 43
4.1 智能化食安監控與管理系統模型 43
4.2 智能化食安監控與管理系統架構 49
第五章 資料分析架構與方法 51
5.1 分析架構 51
5.2 分析項目 54
5.3 分析方法 57
第六章 系統驗證 65
6.1 案例描述 65
6.2 系統分析 68
6.2.1 分析工具 68
6.2.2 資料蒐集與前處理 68
6.2.3 影響因子分析與影響因子之影響模式分析 72
6.2.4 影響因子整體趨勢分析 81
6.2.5 變因與問題分析 85
第七章 結論與討論 89
7.1 結論 89
7.2 討論 90
7.3 未來研究方向 90
參考文獻 92
附錄一 99
附錄二 105
附錄三 113
附錄四 121
附錄五 129
附錄六 138

參考文獻 Agarwal, R., &Dhar, V. (2014). Editorial—Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research. Information Systems Research, 25(3), 443–448.
Agrawal, R., Imieliński, T., &Swami, A. (1993). Mining Association Rules Between Sets of Items in Large Databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (pp. 207–216). New York, NY, USA: ACM.
Alfian, G., Rhee, J., Ahn, H., Lee, J., Farooq, U., Ijaz, M. F., &Syaekhoni, M. A. (2017). Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering, 212(Supplement C), 65–75.
ASQ. (2018). PLAN-DO-CHECK-ACT (PDCA) CYCLE. Retrieved from http://asq.org/learn-about-quality/project-planning-tools/overview/pdca-cycle.html
Barton, D., &Court, D. (2012). Making Advanced Analytics Work for You. Harvard Business Review, 90, 78–83,128.
Booton, R. C., &Ramo, S. (1984). The development of systems engineering. IEEE Transactions on Aerospace and Electronic Systems, (4), 306–310.
Breiman, L. (1984). Classification and regression trees. Routledge.
Cambridge dictionary. (2018). Cambridge dictionary-smart. In Cambridge dictionary. Retrieved from http://dictionary.cambridge.org/us/dictionary/english/smart
Carbone, Anna, Jensen, M., &Sato., A.-H. (2016). Challenges in data science: a complex systems perspective. Chaos, Solitons & Fractals, 90, 1–7.
Chen, R.-Y. (2017). An intelligent value stream-based approach to collaboration of food traceability cyber physical system by fog computing. Food Control, 71, 124–136.
DeFries, R. S., Hansen, M., Townshend, J. R. G., &Sohlberg, R. (1998). Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers. International Journal of Remote Sensing, 19(16), 3141–3168.
Dictionary. (2018). Dictionary-smart. In Dictionary. Retrieved from http://www.dictionary.com/
Ding, J., Xu, H., Li, P., &Xie, R. (2017). Design and Implementation of Food Safety Traceability System Based on RFID Technology. In Conference on Complex, Intelligent, and Software Intensive Systems (pp. 657–666). Springer.
Efrim Boritz, J., &Kennedy, D. B. (1995). Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications, 9(4), 503–512.
Eisner, H. (2008). Essentials of project and systems engineering management. John Wiley & Sons.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., &Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115.
Fawcett, F. P. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking.
Goh, A. T. C. (1995). Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9(3), 143–151.
Hall, A. D. (1962). A methodology for systems engineering.
Han, J., Pei, J., &Yin, Y. (2000). Mining Frequent Patterns Without Candidate Generation. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (pp. 1–12). New York, NY, USA: ACM.
Haskins, C., Forsberg, K., Krueger, M., Walden, D., &Hamelin, D. (2006). Systems engineering handbook. In INCOSE.
Hazen, B. T., Boone, C. A., Ezell, J. D., &Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72–80.
Heermann, P. D., &Khazenie, N. (1992). Classification of multispectral remote sensing data using a back-propagation neural network. IEEE Transactions on Geoscience and Remote Sensing, 30(1), 81–88.
Hsieh, W. W. (2007). Nonlinear principal component analysis of noisy data. Neural Networks, 20(4), 434–443.
International Standards Organisation. (2008). ISO/IEC/IEEE Systems and Software Engineering - System Life Cycle Processes. IEEE Std 15288-2008.
International Standards Organisation. Iso 31000:2018 Risk management - Guidelines (2018). Retrieved from https://www.iso.org/obp/ui#iso:std:iso:31000:ed-2:v1:en
Kass, G.V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 119–127.
Khan, J., Wei, J. S., Ringnér, M., Saal, L. H., Ladanyi, M., Westermann, F., …Meltzer, P. S. (2001). Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, 7, 673.
Liu, B., Hsu, W., &Ma, Y. (1999). Mining Association Rules with Multiple Minimum Supports. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 337–341). New York, NY, USA: ACM.
Mattmann, C. A. (2013). A vision for data science. Nature, 493, 473.
McCulloch, W. S., &Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.
Meng, A., Ge, J., Yin, H., &Chen, S. (2016). Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm. Energy Conversion and Management, 114, 75–88.
Mensah, L. D., &Julien, D. (2011). Implementation of food safety management systems in the UK. Food Control, 22(8), 1216–1225.
Merriam-Webster. (2018). Merriam-Webster-smart. In Merriam-Webster. Retrieved from http://www.merriam-webster.com/dictionary/smart
Miyagishima, K., &Bruno, A. M. (2009). CODEX ALIMENTARIUS STANDARDS, ONGOING WORK AND COOPERATION WITH THE OIE.
NASA. (2009). Systems Engineering Handbook (NASA/SP-2007-6105,) Rev. 1, 2007. Garvey, Paul R., Analytical Methods for Risk Management: A Systems Engineering Perspective, Boca Rotan: Chapman & Hall/CRC, 113.
National Environment Agency. (2018). Information on Food Safety Management System. Retrieved from http://www.nea.gov.sg/public-health/food-hygiene/info-on-fsms#Intro
Ning, J., Chen, Z., &Liu, G. (2010). PDCA process application in the continuous improvement of software quality. In 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering (Vol. 1, pp. 61–65).
Oxford Dictionaries. (2018). Oxford Dictionaries-smart. In Oxford Dictionaries. Retrieved from https://www.oxforddictionaries.com/
Park, J. S., Chen, M.-S., &Yu, P. S. (1995). An Effective Hash-based Algorithm for Mining Association Rules. In Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data (pp. 175–186). New York, NY, USA: ACM.
Provost, F., &Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59.
Purdy, G. (2010). ISO 31000: 2009—setting a new standard for risk management. Risk Analysis, 30(6), 881–886.
Qiu, M., &Song, Y. (2016). Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model. PLOS ONE, 11(5), e0155133.
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.
Quinlan, J. R. (1993). C4. 5: Programs for empirical learning. Morgan Kaufmann, San Francisco, CA.
Rokach, L., &Maimon, O. Z. (2008). Data mining with decision trees: theory and applications (Vol. 69). World scientific.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533.
Saeed, M., &Norman, C. (2000). Neural network versus econometric models in forecasting inflation. Journal of Forecasting, 19(3), 201–217.
Savasere, A., Omiecinski, E., &Navathe, S. B. (1995). An Efficient Algorithm for Mining Association Rules in Large Databases. In Proceedings of the 21th International Conference on Very Large Data Bases (pp. 432–444). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
Sekine, S., Grishman, R., &Shinnou, H. (1998). A decision tree method for finding and classifying names in Japanese texts. In Sixth Workshop on Very Large Corpora.
Sokovic, M., Pavletic, D., &Pipan, K. K. (2010). Quality improvement methodologies–PDCA cycle, RADAR matrix, DMAIC and DFSS. Journal of Achievements in Materials and Manufacturing Engineering, 43/1, 476–483.
Thomas H. Davenport, D. J. P. (2012). Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review.
Traill, W. B. (1999). Conference on International Food Trade Beyond 2000: Science-Based Decisions, Harmonization, Equivalence and Mutual Recognition Melbourne, Australia, 11-15 October 1999 Prospects for the Future: Nutritional, Environmental and Sustainable Food Production Co. Prospects, 11, 15.
Vellido, A., Lisboa, P. J. G., &Vaughan, J. (1999). Neural networks in business: a survey of applications (1992–1998). Expert Systems with Applications, 17(1), 51–70.
Waller, M. A., &Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84.
Wang, H. S. (2007). Application of BPN with feature-based models on cost estimation of plastic injection products. Computers & Industrial Engineering, 53(1), 79–94.
Wang, J., &Yue, H. (2017). Food safety pre-warning system based on data mining for a sustainable food supply chain. Food Control, 73, 223–229.
Wang, J., Yue, H., &Zhou, Z. (2017). An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network. Food Control, 79, 363–370.
Weerasooriya, S., &El-Sharkawi, M. A. (1991). Identification and control of a DC motor using back-propagation neural networks. IEEE Transactions on Energy Conversion, 6(4), 663–669.
wikipedia. (2018a). Wekipedia - Food. In Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Food#cite_note-1
wikipedia. (2018b). Wikipedia-System engineering. In wikipedia. Retrieved from https://en.wikipedia.org/wiki/Systems_engineering
Word Central Student Dictionary. (2018). Word Central Student Dictionary-smart. In Word Central Student Dictionary. Retrieved from http://www.wordcentral.com/
Wordnet. (2018). Wordnet-smart. Wordnet. Retrieved from https://wordnet.princeton.edu/
World Health Organization, R. O. for S.-E. A. (2015). Food safety: What you should know. Retrieved from http://www.searo.who.int/entity/world_health_day/2015/whd-what-you-should-know/en/#quality
Yu, Z., Haghighat, F., Fung, B. C. M., &Yoshino, H. (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42(10), 1637–1646.
Zhang, G., Eddy Patuwo, B., &Y. Hu, M. (1998). Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting, 14(1), 35–62.
台北醫學大學. (2014). 食品安全國際研討會. Retrieved from http://event.tmu.edu.tw/actnews/content.php?Sn=1190
行政院食品安全辦公室. (2015). 應用資訊科技強化食品安全管理國際研討會. Retrieved from https://www.ey.gov.tw/ofs/cp.aspx?n=98F272C21B1301B3
行政院食品安全辦公室. (2016). 食安五環扣,幸福安心GO. Retrieved from https://www.ey.gov.tw/ofs/cp.aspx?n=4CD62546A42046EE
行政院食品安全辦公室. (2017a). 106年從食品產業供應鏈看校園午餐之安全管理研討會. Retrieved from https://www.ey.gov.tw/ofs/cp.aspx?n=E9F640ECE968A7E1
行政院食品安全辦公室. (2017b). 106年食品安全管理檢討會議.
行政院新聞傳播處. (2015). 毛揆:強化台歐產官學交流 借重國際經驗精進我國食安管理作為. Retrieved from https://www.ey.gov.tw/Page/9277F759E41CCD91/5cd1c5e5-1e9e-40b7-a208-460d08bbe6db
林玉婷、嚴永龍. (2018). 食安問題層出不窮的台灣 能向香港學什麼?. 食力 FoodNEXT. Retrieved from http://www.foodnext.net/news/newstrack/paper/5470108585
社團法人中華食品安全管制系統發展協會. (2017). 危害分析重要管制點 HACCP. In Wikipedia. Retrieved from https://zh.wikipedia.org/wiki/危害分析重要管制點
金融業的大數據. (2016). 深解讀:什麼是數據科學?如何把數據變成產品? Retrieved May19, 2016, from http://www.bigdatafinance.tw/index.php/finance/fintech/305-2016-05-19-09-18-12
國際標準組織. (2007). ISO 22005:2007 飼料與食品供應鏈的可追溯性. Retrieved from https://www.iso.org/standard/36297.html
許輔. (2014). 提高食品安全-食品登錄與食品追溯. 科技報導, 395(食品安全特輯). Retrieved from http://scitechreports.blogspot.com/2014/11/blog-post_25.html
陳宣毅、謝宜珊. (2016). 資料科學面面觀. Retrieved from https://www.lis.ntu.edu.tw/?p=4731
陳彥良, 趙書榮, &陳禹辰. (2003). 幾個快速挖掘關聯規則的資料探勘方法. 電子商務學報, 5(2), 1–10.
陳政忻. (2011). 全球食品安全發展趨勢. 農業生技產業季刊, (27), 7–10.
陳韻竹. (2016). 過去最先做的傻子 聯華食品安心履歷成為業界標竿. 食力 FoodNEXT力. Retrieved from http://www.foodnext.net/science/scsource/paper/4975350334
智庫百科. (2017a). 戴明迴圈. In MBA智庫百科. Retrieved from http://wiki.mbalib.com/zh-tw/戴明循环
智庫百科. (2017b). 系統工程. In MBA智庫百科. Retrieved from http://wiki.mbalib.com/zh-tw/系统工程
曾淑峰、林志弘、翁玉麟. (2012). 資料採礦應用-以SAS Enterprise Miner為工具.
曾龍. (2016). 大數據與巨量資料分析. 科學發展, 524.
硬塞科技字典. (2016). what is data science. In Inside 科技字典. Retrieved from https://www.inside.com.tw/2016/08/04/what-is-data-science
程明修. (2009). 行政法上之預防原則─食品安全風險管理手段之擴張. 月旦法學雜誌.
黃彥棻. (2014). 臺灣首度舉辦資料科學愛好者年會,傳遞從資料創造價值的理念. IThome. Retrieved from https://www.ithome.com.tw/news/90548
裕○馨. (2018). 經營理念. Retrieved from www.yjs.com.tw
維基百科. (2018a). data science. Retrieved from https://zh.wikipedia.org/wiki/数据科学
維基百科. (2018b). 類神經網路. In Wikipedia. Retrieved from https://zh.wikipedia.org/wiki/人工神经网络
顏真真. (2014). 食安更有保障 台糖食品安全追溯系統上線. 今日新聞NOWNEWS. Retrieved from https://www.nownews.com/news/20140512/1230194
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