Data Collection Services

We collect real-time data from IoT sensors and historical data for comprehensive food safety insights.

API Integration Solutions

We connect IoT sensors with big data systems for efficient data storage and processing.

Several stainless steel food containers with green lids are placed on a metallic table, overlooking a body of water. The containers are filled with assorted fruits and snacks, including blueberries, mango slices, and pieces of bread or chips. The scene suggests a leisurely outdoor meal or picnic by the water.

Emerging Issues Monitoring

We gather data on emerging food safety issues from scientific literature, news, and government databases.

Real-Time Data Processing

We ensure efficient processing of real-time data for enhanced food safety management.

Cooks in a professional kitchen prepare and serve food under bright red pendant lights. They wear hairnets and face masks, indicating a focus on hygiene and safety. Steamers and other kitchen utensils are visible, suggesting a busy and efficient culinary environment.

A collection of ingredients and tools arranged on a wooden surface include diced red bell peppers in a small bowl, shredded potatoes in a teal bowl, minced herbs in a small green bowl, and raw ground chicken in a glass bowl. Two orange digital thermometers are also present alongside a blue and white striped cloth.

Data Collection

Collecting real-time data from IoT sensors for food safety.

Fresh green sprouts are spread across a blue conveyor belt, enclosed by metal sides. The scene suggests an industrial farming or food processing environment.

API Integration

Integrating IoT sensors with big data systems for efficient processing.

A sous vide device with a digital display showing 90.5°C is attached to a pot of water. Inside the pot, a vacuum-sealed bag contains a piece of meat, herbs, and spices.

Emerging Issues

Gathering data on emerging food safety issues from various sources.

In the context of enhancing the food system's resilience to food safety risks by integrating AI, big data, and the Internet of Things (IoT) into early - warning and emerging risk identification tools, the following research questions are proposed:How can AI, big data, and IoT be effectively integrated through API to improve the accuracy and timeliness of food safety early - warning systems? The current food safety early - warning systems may face challenges in handling large - scale, diverse data. By using API to combine AI algorithms, big data analytics, and IoT sensor data, we aim to explore how to optimize the data flow and processing, so as to provide more accurate and timely warnings. For example, can we use API to connect IoT devices that collect real - time food quality data (such as temperature, humidity, and chemical composition) with AI models that can predict potential risks?What are the impacts of different AI models integrated via API on the identification of emerging food safety risks? There are various AI models, such as neural networks, decision trees, and support vector machines. Each model has its own characteristics in data processing and pattern recognition. Through API, we can test different AI models in the food safety risk identification scenario. We want to know which model is more suitable for emerging risk identification, considering factors like the complexity of the data, the speed of response, and the accuracy of prediction.