In recent years, many large-scale plantings have become refined small-scale or home plantings. The rapid progress of agriculture technologies and information techniques enables people to control the growth of agricultural products well. Hence, this study proposes an Artificial Intelligence of Things (AIoT) based Plant Pot Design for planting edible mint in an office setting, which is called APPD. APPD is composed of intelligent gardens and a cloud-based service platform. An intelligent garden is deployed an Arduino with multiple sensors to monitor and control plant pots of the edible mint, Mentha spicata. The cloud-based service platform provides a Case-Based Reasoning (CBR) inference engine with a database for adjustment influence factors. This study discusses eight growing statuses of Mentha spicata with different illumination, photometric exposure, and moisture content, designed for an office environment. Evaluation results indicate that Mentha spicata with 16 h red–blue lighting and 50% moisture content makes a maximum 5% mint extract of the total weight of the mint leaves. Finally, APPD can be a reference model for researchers and engineers.

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.

Mackerel (Scomber australasicus) steaming juice (MSJ) can be a good source of proteins. However, it is often treated as food waste during the canning process. The objective of this study was to investigate the Angiotensin-I converting enzyme (ACE-I) inhibitory and antioxidant activities from MSJ hydrolysates using in silico and in vitro approaches. Proteins extracted from MSJ were identified by proteomic techniques, followed by sulfate polyacrylamide gel electrophoresis (SDS-PAGE), in-gel digestion, tandem mass spectrometry and on-line Mascot database analysis. Myosin heavy chain (fast skeletal muscle), actin, myosin light chain 1 (skeletal muscle isoform), collagen alpha-2(I) chain, tropomyosin alpha-1 chain, beta-enolase, fructose-bisphosphate aldolase A and glyceraldehyde-3- phosphate dehydrogenase were identified and further analyzed using BIOPEP-UWM database. In silico results indicated that MSJ proteins had potential bioactive peptides of antioxidant and ACE-I inhibitory activities. MSJ was then hydrolyzed using six proteases (papain, pepsin, proteinase k, alcalase, bromelain, thermolysin). In particular, pepsin hydrolysates (5 mg/mL) showed the highest 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity (61.54%) among others. Alcalase hydrolysates (5 mg/mL) exhibited the highest metal chelating activity (89.76%) and proteinase K hydrolysates (5 mg/mL) indicated the highest reducing power activity (1.52 abs). Moreover, pepsin hydrolysates (0.1 mg/mL) possessed the highest ACE inhibitory activity (86.15%). Current findings suggest that MSJ hydrolysates can be a potential material to produce ACE-I inhibitory and antioxidant peptides as nutraceutical or pharmaceutical ingredients/products with added values.

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.