In a collaboration with DIAM Bouchage, the Carnot institute CEA List has developed a technology to detect defective corks. This solution uses artificial intelligence to perform an X-ray analysis of the cork.
An automatic sorting solution for defective corks
As wine connoisseurs know, sometimes a wine can become corked or oxidized when a bottle is opened, even though a cork is considered the ideal choice for preserving it. In order for a wine to be preserved, the cork must be able to let in just enough oxygen to allow it to mature and guarantee its taste quality over time. If this is not the case, it loses its flavor.
However, it is impossible to know with the naked eye if a cork is properly sealed or if it lets too much oxygen into the bottle. It is in this context that the partnership between the Carnot CEA List institute and the company Diam Bouchage intervenes. The company has asked the researchers to develop an automated and reliable method for sorting corks made by tubing cork oak bark.
AI to automate the tool
X-ray tomography is used to produce images of the cork to be analyzed, so the cork can be viewed in its entirety. To perform this analysis, the researchers developed a machine learning algorithm adapted to cork classification.
The solution compares imaging characteristics such as growth lines, cork density or number and distribution of lenticels, with long-term oxygen transfer rate data. Thus, the model automatically assesses the tightness of the cork and classifies it as a good or bad cork.
Initial tests of the platform show that 75% of corks are correctly classified, and this in a matter of seconds, as the tool analyzes only two images per cork. The researchers are continuing their research in order to improve their model with one objective: to reach 100% correct classification, a mandatory criterion for their algorithm to be used to cork great wines.
An automated and reliable method for sorting corks made by tubing cork oak bark
A machine learning algorithm adapted to cork classification in seconds.