OR WAIT null SECS
To kick off the Tuesday afternoon oral symposia on “Chemometrics in Food and Agriculture,” Dr. Barry Lavine of Oklahoma State University talked about the work and that he and his team have done recently on using Fourier transform infrared (FT-IR) spectroscopy to authenticate and differentiate edible oils.
Lavine began his talk by establishing what edible oils are. He defined edible oils in his talk as “food substances that are manufactured from fats and oils” (1). Edible oils are known to contain tocopherol and phenolic compounds. He then talked about previous studies that explored this topic. He explained that chromatographic techniques such as gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–MS (LC–MS) have been used in the past to authenticate edible oils. However, there are limitations with using these techniques for this type of analysis, including its cost and how time-consuming these techniques are (1).
This explanation set the stage for the current study, which Lavine highlighted. A machine learning technique was showcased for distinguishing between two types of edible oils using FT-IR spectroscopy. This method involves generating digital data by blending pure edible oil spectra with adulterant spectra to simulate different levels of adulteration.
By comparing the IR spectra of pure edible oils with digitally blended mixtures, the study established if FT-IR spectroscopy can reliably differentiate between these oils (1). The approach demonstrates the feasibility of authenticating edible oils, such as extra virgin olive oil, using library spectra. Both digital and experimental data are combined to create training and prediction sets for method validation and to determine detection limits in FT-IR spectroscopy for calibrated and uncalibrated adulterants (1).
Lavine’s study showcases a machine learning approach utilizing digitally generated data and FT-IR spectroscopy to determine if two varieties of edible oils can be distinguished, demonstrating its potential for authenticating oils and assessing detection limits in FT-IR spectroscopy. Lavine concluded his talk by saying that although there is more research that can be conducted in this field, the recent study he and his team conducted demonstrated a basic methodology for assessing two edible oils.
This article was written with the help of artificial intelligence and has been edited to ensure accuracy and clarity. You can read more about our policy for using AI here.
(1) Lavine, B. Authentication of Edible Oils Using an Infrared Spectral Library and Digital Sample Sets for Calibrated and Uncalibrated Adulterants. Presented at SciX 2023, in Sparks, Nevada, October 10, 2023.