Using AI to Fight Food Fraud

Somewhat unexpected application, good details embedded here.  Note the automated tasting sensors involved, which we investigated for coffee taste applications.

Using AI to Fight Food Fraud  By Sandrine Ceurstemont, Commissioned by CACM Staff, July 7, 2022

Food fraud is a global problem that typically involves the dilution or mislabeling of food products, or ingredient substitution. In 2013, horse meat was found in many supermarket meals in Europe that claimed to contain beef, for example, while milk has often been found to be waterd down in India to increase profits.

A 2021 study by the United Nations Food and Agriculture Organization cited a 2018 European Commission finding that estimated " the cost of food fraud for the global food industry is approximately EUR 30 billion" (about $30.5 billion) each year.

While chemical analyses can be carried out in a lab to authenticate food, traditional methods are often expensive, time-consuming, and require technical expertise. That is why researchers are aiming to develop new tools that harness artificial intelligence (AI) to enable rapid, inexpensive screening of food and beverages.

"It would be a very exciting scenario to have AI help us expand the reach and impact of chemical analyses," says Patrick Ruch, a research staff member at IBM Research in Zurich, Switzerland. "All of the intelligence can be on a smartphone or in the cloud."

Ruch and his colleagues have been working on a system to authenticate beverages called HyperTaste that uses a small, portable device called an electronic tongue (e-tongue), combined with machine learning. The e-tongue contains 16 sensors made of conductive polymers that can be thought of as taste buds; when dipped into a drink, the sensors pick up chemical information in the liquid that can be converted into a unique digital fingerprint measured as a time series of voltages. "We know that the signal that we're measuring is a unique indicator of what's inside the liquid because these polymers are interacting with all of the small molecules inside," says Ruch.

Machine learning then is used to make sense of the complex signal detected, for example to identify a specific brand of wine or its origin. In recent work, Ruch and his colleagues focused on wines and juices, training three different machine learning models to perform various recognition tasks using data collected with a sensor-equipped robotic device. The automated system dipped its sensors into nine different types of fruit juices several times, collecting 72 voltage time-series measurements. The process was repeated using 11 different types of Italian red wines to generate 110 measurements. "Nowadays, you can really quickly obtain the data needed for training with automation," says Ruch. "Within half a day to maximum a day, you have all the training data you need."  ... '