Traditional oil quality measurement is mostly based on chemical indicators such as acid value, peroxide value, and p-anisidine value. This process requires specialized knowledge and involves complex steps. Hence, this study designs and proposes a Sesame Oil Quality Assessment Service Platform, which is composed of an Intelligent Sesame Oil Evaluator (ISO Evaluator) and a Cloud Service Platform. Users can quickly assess the quality of sesame oil using this platform. The ISO Evaluator employs Artificial Intelligence of Things (AIoT) sensors to detect changes in volatile gases and the color of the oil during storage. It utilizes deep learning mechanisms, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to determine and evaluate the quality of the sesame oil. Evaluation results demonstrate that the linear discriminant analysis (LDA) value is 95.13. The MQ2, MQ3, MQ4, MQ7, and MQ8 sensors have a positive correlation. The CNN combined with an ANN model achieves a Mean Absolute Percentage Error (MAPE) of 8.1820% for predicting oil quality, while the LSTM model predicts future variations in oil quality indicators with a MAPE of 0.44%. Finally, the designed Sesame Oil Quality Assessment Service Platform effectively addresses issues related to digitization, quality measurement, supply quality observation, and scalability.

Many food safety incidents have occurred in the world in the past 20 years, causing major threats and harm to human life and health. Each country or region has established different food safety management systems (FSMSs) in response, to increase food safety and to reduce food safety risks. Hence, it is important to develop an FSMS service platform with convenience, consistency, effectiveness, scalability, and lightweight computing. The aim of this study is to design and propose an IOTA Tangle-based intelligent food safety service platform for bubble tea—called IF4BT—which modularizes and integrates hazard analysis and critical control point (HACCP) principles to increase data transparency. The deep learning inference engine is based on long short-term memory and Siamese networks to check and extract significant rare data of high-risk factors, exception factors, and noises, depending on daily check and audit. IF4BT can ensure the correctness of the information of food manufacturers, so as to increase food safety and to reduce food safety issues such as allergen cross-contamination, food expiration, food defense, and food fraud.