Monitoring Food Freshness With 3D Printing and Neural Networks

Can we rely on our five senses to know if fruits and vegetables are fresh or not? In most cases, it is enough to observe, smell or touch the food to realize that we can eat it. In these cases, there is usually not much risk. However, in the food industry, quality standards can be more demanding and ensuring the freshness of fruits and vegetables during transport and storage can become an even greater challenge. As a solution, a recent study from Jiangnan University in China has developed a new system that combines 3D printing with deep convolutional neural networks (DCNN) to monitor the quality and freshness of foods like fruits and vegetables in real time.

Using 3D printing, labels were made with color indicators that detect changes in CO₂ levels that occur during the decomposition of fruits and vegetables. And by means of deep convolutional neural networks, the results displayed by the labels were interpreted. By combining these two technologies, the perfect method for monitoring the freshness of fruits and vegetables was achieved.

3D printed labels monitor changes in CO₂ levels during storage of vegetables such as green vegetables, cucumbers, okras, plums and jujubes to better understand food freshness (photo credits: Tiantian Tang, Min Zhang, Huijie Jia, Bhesh Bhandari, Zhimei Guo)

How Do 3D Printing and Neural Networks Relate to 3D Printing?

Before going into the details of the study, it is necessary to mention what deep convolutional neural networks (DCNN) are. These are a class of artificial neural networks that are frequently used to analyze different images. Taking advantage of the design flexibility of 3D printing, the labels contain color indicators based on bromothymol in blue and methyl in red. In this way, the labels show the changes in CO₂ levels that occur during the decomposition of fruits and vegetables.