The team led by Prof. Yao-Tung Lin, a tenured distinguished professor at National Chung Hsing University, has developed an intelligent and precise real-time monitoring technology for meat freshness. This technology utilizes agricultural and fishery waste to extract natural pigments, which are then made into meat freshness indicators. Coupled with artificial intelligence technology for real-time recognition of changes in meat pH values, this approach achieves real-time, precise, convenient, low-cost, and high-resolution monitoring. It helps reduce meat waste and greenhouse gas emissions, enhancing food safety and sustainable development.
High-value utilization of agricultural waste: A new opportunity for circular economy
Prof. Lin stated that this technology utilizes agricultural and fishery waste (such as lemon, passion fruit, red dragon fruit, and grapefruit peel) to extract natural pigments such as anthocyanins and betalains, which are then used to produce meat freshness indicators. This not only reduces environmental pollution but also creates new value for agricultural and fishery waste, aligning with the concept of the circular economy.
Breaking the Limits of Traditional Testing, Precisely Monitoring Meat Freshness
Traditional methods of meat freshness testing require sampling and laboratory analysis, which are time-consuming and unable to monitor changes in meat freshness in real-time. The key technology of Prof. Lin's team is the smart freshness indicator, which relies on changes in pH values resulting from meat spoilage. This technology alters the structure of plant pigments, causing changes in the appearance color. For example, the color of passion fruit peel extract changes from red to yellow, green, and brown as the pH value changes. This is because anthocyanins appear red-yellow in acidic conditions and purple-blue in alkaline conditions. Similar color changes occur with extracts from grapefruit, red dragon fruit, lemon peel, and other sources. By directly attaching the indicator to meat packaging, any change in pH value due to meat spoilage results in a corresponding color change, allowing consumers to easily identify meat freshness at a glance.
Enhanced by artificial intelligence, precise recognition of meat pH value changes
To improve the accuracy of meat freshness testing, Professor Lin's team has incorporated artificial intelligence technology. Through machine learning training, they can precisely identify meat pH values based on the microscopic changes in the color of the freshness indicator, achieving a resolution to the first decimal place, far exceeding traditional testing methods.
Technical advantages and future prospects
Prof. Lin highlighted the following advantages of this technology:
This technology is expected to make significant contributions to food safety and environmental sustainability. It can also be applied to the development of smart medical dressings, creating new opportunities for medical care and realizing the untapped potential of agricultural and fishery waste with a wide range of applications.
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