Rapid metabolic fingerprinting meets machine learning models to identify authenticity and detect adulteration of...

Essential oils (EOs) are gaining popularity due to their potent antibacterial properties, as well as their applications in food preservation and flavor enhancement, offering growth opportunities for the food industry. However, their widespread use as food preservatives is limited by authenticity challenges, primarily stemming from adulteration with cheaper oils. This study investigated a rapid, cost-effective, and non-destructive method for assessing the authenticity of widely used Mentha and Ocimum EOs. The proposed approach integrates Fourier transform near-infrared (FT-NIR) spectroscopy with machine learning to enable rapid metabolic fingerprinting of EOs. Four Mentha species and three Ocimum species were analysed, and the system was tested on market samples adulterated with vegetable oils. The approach achieved exceptional performance, with Q², R², and accuracy exceeding 0.98, alongside specificity and sensitivity greater than 98 %. These findings demonstrated that FT-NIR, combined with machine learning, offers a highly efficient solution for addressing authenticity and adulteration issues in EOs.