Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms.

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.

Differential Proteomic Analysis of Listeria monocytogenes during High-Pressure Processing.

High-pressure processing (HPP) is a prevailing non-thermal food preservation technology. The inactivation mechanisms of Listeria monocytogenes under HPP at 200 and 400 MPa for 3 min were investigated by label-free quantitative proteomic analysis and functional enrichment analysis in the Kyoto Encyclopedia of Genes and Genomes. HPP treatment at 400 MPa exhibited significant effects on proteins involved in translation, carbon, carbohydrate, lipid and energy metabolism, and peptidoglycan biosynthesis. HPP increased most ribosomal subunits and initiation factors, suggesting it might shift ribosomal biogenesis to translation initiation. However, protein synthesis was impaired by the shortage of proteins responsible for elongation, termination and recycling. HPP stimulated several ATP-dependent Clp proteases, and the global transcriptional regulator Spx, associating with activation of the stress-activated sigma factor Sigma B (σB) and the transcriptional activator positive regulatory factor A (PrfA) regulons. The quantitative proteomics approaches provide fundamental information on L. monocytogenes under different HPP pressures, and provide theoretical support for HPP against Listeriosis illness and for promotion of safer ready-to-eat foods.

In Silico and In Vitro Analyses of Angiotensin-I Converting Enzyme Inhibitory and Antioxidant Activities of Enzymatic Protein Hydrolysates from Taiwan Mackerel (Scomber australasicus) Steaming Juice.

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.

Assessment of potential human health risks in aquatic products based on the heavy metal hazard decision tree.

BACKGROUND: Naturally existing and human-produced heavy metals are released into the environment and cannot be completely decomposed by microorganisms, but they continue to accumulate in water and sediments, causing organisms to be exposed to heavy metals. RESULTS: This study designs and proposes heavy metal hazard decision trees for aquatic products, which are divided into seven categories including pelagic fishes, inshore fishes, other fishes, crustaceans, shellfish, cephalopods, and algae. Based on these classifications, representative fresh and processed seafood products are at the root of the heavy metal hazard decision trees. This study uses 2,107 cases of eating 556 cooked fresh or processed seafood product samples. The constructions of the proposed decision trees consist of 12 heavy metals, which include inorganic arsenic (iAs), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), strontium (Sr), thallium (Tl), and zinc (Zn). The heavy metal concentrations in cooked fresh and processed seafood product samples are subjected to a food safety risk assessment. CONCLUSIONS: The results indicate the relationships among the seven categories of aquatic products, the relationships among 12 heavy metals in aquatic products, and the relationships among potential human health risks. Finally, the proposed heavy metal hazard decision trees for aquatic products can be used as a reference model for researchers and engineers.

Design of a Golf Swing Injury Detection and Evaluation open service platform with Ontology-oriented clustering case-based reasoning mechanism.

Nowadays, people can easily use a smartphone to get wanted information and requested services. Hence, this study designs and proposes a Golf Swing Injury Detection and Evaluation open service platform with Ontology-oritened clustering case-based reasoning mechanism, which is called GoSIDE, based on Arduino and Open Service Gateway initative (OSGi). GoSIDE is a three-tier architecture, which is composed of Mobile Users, Application Servers and a Cloud-based Digital Convergence Server. A mobile user is with a smartphone and Kinect sensors to detect the user's Golf swing actions and to interact with iDTV. An application server is with Intelligent Golf Swing Posture Analysis Model (iGoSPAM) to check a user's Golf swing actions and to alter this user when he is with error actions. Cloud-based Digital Convergence Server is with Ontology-oriented Clustering Case-based Reasoning (CBR) for Quality of Experiences (OCC4QoE), which is designed to provide QoE services by QoE-based Ontology strategies, rules and events for this user. Furthermore, GoSIDE will automatically trigger OCC4QoE and deliver popular rules for a new user. Experiment results illustrate that GoSIDE can provide appropriate detections for Golfers. Finally, GoSIDE can be a reference model for researchers and engineers.

Web2OHS: a Web2.0-based omnibearing homecare system.

This study presents a Web2.0-based Omnibearing Homecare System (Web2OHS) that uses our proposed Application Layer Somecast (ALS) protocol for real-time interactions. Web2OHS provides omnibearing homecare and patientcare services for medical staff and caregivers, which are capable of assisting families, physicians, and nurses to obtain patients' physiological information using healthcare sensors, as well as monitor their behaviors using monitoring-based services. Web2OHS is a three-tier architecture that consists of Web2OHS clients, the Web2.0-based Patientcare Service Platform (WPSP), and the Medicine-based Active Database (MADB). Users can interact with Web2OHS using various appliances and retrieve the latest physiological and monitoring data using really simple syndication (RSS) and the proposed ALS services. The WPSP supports Web2.0-based applications, including blog-like monitoring services, monitoring-based RSS services, a real-time interaction services, and ALS services. The MADB provides a well-designed database, which stores physiological information, clinical information, digital imaging and communications in medicine image files, and monitoring frames. All of the delivered messages are based on extensible markup language and the Health Level 7 protocol. The proposed Web2OHS can support medical informatics and is compatible with related medical information systems.