Tool combines artificial neural sensors and networks to identify tampering more accurately and lower
An innovation developed at USP’s São Carlos School of Engineering combines microwave sensing and artificial intelligence to quantify up to 5% of adulterants in extra virgin olive oil.
The method is capable of identifying soy, corn, sunflower and canola oils accurately of 99.2%, and is a more accessible and practical solution compared to traditional techniques such as nuclear magnetic resonance imaging. In addition to ensuring lower cost, the system has a potential for large -scale application in industry and the consumer market for allowing the manufacture of portable devices.
“Our goal was to create accessible and efficient technology to detect tampering in olive oil, something that, with traditional methods, requires expensive equipment and specialized personnel. With the planar sensor in microwave, we have been able to identify adulterants such as soybean and canola oil with high precision, as well as quantifying the level of tampering in practical and portable way ”says Júlio Alarcon, author of developed at EESC.
Currently, techniques such as nuclear magnetic resonance imaging and chromatography, also used to identify tampering, are expensive, time consuming and use non -portable equipment.
