INFORM May 2026

34 INFORM MAY 2026, VOL. 37, NO. 5

RAPID ASSESSMENT OF OLIVE OIL ADULTERATION USING LIF SPECTROSCOPY AND A COMPARATIVE STUDY OF MACHINE LEARNING MODELS Li, L., et al. , Food Analytical Methods , 19, 135, 2026. Laser-induced fluorescence (LIF) provides a rapid, nondestructive tool for detecting adulteration in olive oil. However, severe spectral overlap remains a major obstacle, hindering both qualitative identification and quantitative determination of adulteration levels. In this study, a 405 nm diode laser source was used to excite pure extra virgin olive oil (EVOO), soybean oil, peanut oil, and corn oil, as well as binary blends of these three vegetable oils in EVOO,

precise quantification of adulteration levels, thereby offering an innovative approach to food quality monitoring. INTEGRATING ION MOBILITY SPECTROMETRY AND MACHINE LEARNING FOR GEOGRAPHICAL AUTHENTICATION OF OLIVE OILS Rodríguez-Gutiérrez, A. I., et al. , Food Control , 184, 112022, 2026. Interest in ensuring the authenticity and traceability of the geographical origin of high-value agri-food products, such as olive oil, has grown significantly in recent years. This demand stems from the need to protect Protected Designation of Origin products and to comply with quality standards required in export

and a total of 1,140 sets of fluorescence spectral data were obtained. Convolutional neural network (CNN), long short-term memory network (LSTM), and improved deep convolutional neural network (AlexNet) were respectively employed for the detection and quantitative analysis of adulteration in olive oil. The models achieved 100% classification accuracy, robustly differentiating pure EVOO from adulterated oil, which confirms their complete reliability for detecting olive oil adulteration. In terms of quantitative prediction, AlexNet performs better than LSTM and CNN, with the coefficient of determination (R 2 ) of 0.9930, the mean absolute error (MAE) of 0.0987% and root-mean-square error (RMSE) of 0.1258%. Combining LIF technology with the AlexNet deep learning model enables rapid detection of olive oil adulteration while allowing for

2027–2028 AOCS GOVERNING BOARD! READY FOR YOUR NEXT LEADERSHIP CHALLENGE? Volunteer leaders are the driving force behind nearly every aspect of AOCS. For members who have demonstrated leadership and commitment, serving on the AOCS Governing Board is a possible next step in your AOCS experience! If you are passionate about the success of the Society, we invite you to submit an application.

Please visit aocs.org/governance AOCS to apply today! The deadline to submit is Sunday, June 29, 2026.