商品品質是消費者關注的重點，維護商品品質則是業者重要的責任。食品品質的維護更受到各界極大的重視，因其牽涉到人體的生命安全。為維護供應鏈上各家企業的食品品質與安全，企業須審慎選擇其原物料供應商，並對企業自身內部進行「自主管理」。傳統上，企業內部使用之品質管理分析多使用六標準差方法(Six Sigma Methodology)、管制圖及魚骨圖等方法維持品質並改善問題。然而，這些方法無法即時偵測品質之異常並快速反應，且容易因考量的因素不完全，而導致結果不理想。
隨著物聯網觀念的出現與技術的普及，許多學者利用收集的生產數據，再以人工智慧及機器學習等技術，對生產之品質進行分析。然而，現有之研究多侷限於單一功能，缺乏整合整體品質監控與管理功能之模型，且未提及模型持續更新之方法。資料科學提供了掌握現況、預測預防、診斷處方、及自主不斷優化的系統化循環。食品供應鏈各供應點可視為一系統，因此，可將系統工程「系統監控」與「持續改善」以及風險管理「預防」之概念，與資料科學之系統化分析循環相結合，設計一個具掌握現況、監控變因、異常偵測、趨勢預測、預警、診斷處方、經驗累積與不斷優化循環的「食品安全監控與管理模式」，以維護供應鏈上各家企業的食品品質與安全。
針對食品品質與安全之維護需求，本研究提出一「具持續改善機制之食品品質監控模型」並開發其實現技術。針對此一目標，本研究設計一「食品安全維護模型」，以分析食品供應鏈與企業內部應各自及共同具備之功能。接著，參考資料科學之概念，設計「具持續改善機制之食品品質監控模型」，並依此模型針對食品品質分析之需求，設計「食品品質監控與分析之資料模型」。最後，運用統計與機器學習之技術，實現「食品品質監控、預測及預警之功能」與「持續改善之機制」。
本研究使用公開數據集進行實驗，驗證所提之技術的可行性與有效性。在異常偵測的方法中，使用全資料於具持續改善機制的準確率為95.33%、直接使用全資料的方法準確率為95.18%；使用滑窗方法取資料並用於具持續改善機制的準確率為95.26%、直接使用滑窗方法取資料的準確率為95.06%。在趨勢預測的方法中，使用全資料於具持續改善機制的均方根誤差為6.9、直接使用全資料的方法均方根誤差為9.8。在這兩種功能中，具持續改善機制方法的實驗結果皆優於直接使用增量資料的方法，故具持續改善機制之方法是可行且有效的。

Product quality is the focus of consumers' attention, and maintaining product quality is an important responsibility of the industry. The maintenance of food quality has received great attention from all walks of life because it involves the safety of human life. In order to maintain the food quality and safety of each company in the supply chain, companies must carefully select their raw material suppliers and carry out "self-management" within the company itself. Traditionally, the internal quality management analysis used by enterprises mostly uses Six Sigma Methodology, control charts, and fishbone diagrams to maintain quality and improve problems. However, these methods cannot detect quality abnormalities in real-time and react quickly and are prone to unsatisfactory results due to incomplete considerations.

With the emergence of the concept of the Internet of Things and the popularization of technology, many scholars use the collected production data and then use artificial intelligence and machine learning techniques to analyze the quality of production. However, the existing research is mostly limited to a single function, lacks a model that integrates the overall quality monitoring and management functions, and does not mention the method of continuous updating of the model. Data science provides a systematic cycle of mastering the current situation, predicting and preventing, diagnosing prescriptions, and continuously optimizing independently. Each supply point of the food supply chain can be regarded as a system, therefore, the concepts of system engineering "system monitoring" and "continuous improvement" and risk management "prevention" can be combined with the systematic analysis cycle of data science to design a "food safety monitoring and management model" with current status, monitoring variables, abnormal detection, trend prediction, early warning, diagnosis prescription, experience accumulation, and continuous optimization cycle to maintain the food quality and safety of all companies in the supply chain.

In response to the maintenance needs of food quality and safety, this research proposes a "food quality monitoring model with a continuous improvement mechanism" and develops its implementation technology. Aiming at this goal, this research designs a "food safety maintenance model" to analyze the functions that the food supply chain and the enterprise should have separately and jointly. Then, referring to the concept of data science, design a "food quality monitoring model with a continuous improvement mechanism", and based on this model, design a "data model for food quality monitoring and analysis" in response to the needs of food quality analysis. Finally, the use of statistics and machine learning techniques to achieve "food quality monitoring, prediction, and early warning functions" and "continuous improvement mechanism".

This study uses public data sets to conduct experiments to verify the feasibility and effectiveness of the proposed technology. In the method of anomaly detection, the accuracy rate of using the full data in a continuous improvement mechanism is 95.33%, and the accuracy of the method using the full data directly is 95.18%; the accuracy of using the sliding window method to obtain data and using it with a continuous improvement mechanism is 95.26%, and the accuracy of directly using the sliding window method to obtain data is 95.06%. In the trend prediction method, the root-mean-square error of using all data with a continuous improvement mechanism is 6.9, and the root-mean-square error of using all data directly is 9.8. In these two functions, the experimental results of the method with continuous improvement mechanism are better than the method of directly using incremental data, so the method with a continuous improvement mechanism is feasible and effective.

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