ATR-FTIR and FORS Fingerprints for Authentication of Commercial Sunflower Oils and Quantification of Their Oleic Acid

TL;DR

This paper uses ATR-FTIR and FORS to authenticate sunflower oils and measure their oleic acid content, showing FORS as a quick tool and ATR-FTIR as more accurate.

Contribution

The study introduces multivariable PLS-R models using ATR FT-IR and FORS for rapid and accurate quantification of oleic acid in sunflower oils.

Findings

FORS is suitable for on-site rapid quality control of sunflower oils.

ATR FT-IR provides highly accurate quantification of oleic acid (RPD = 7.09, RER = 17.82).

PLS-R models effectively differentiate and authenticate MOSFO with a wide oleic acid range.

Abstract

The composition of sunflower oil, rich in fatty acids, largely depends on the seed variety. Commercial sunflower oils are classified as low (SFO), medium (MOSFO), and high (HOSFO) oleic, distinguished by their oleic and linoleic acid content. Higher oleic acid levels enhance health benefits and oxidative stability. Due to their differing market values, ensuring the correct quality and authenticity of these oils is essential. Unsupervised chemometric methods have been applied to visualise the natural behaviour of sunflower oils, while supervised models have been used for authentication based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR) fingerprints obtained from a benchtop spectrometer. Authentication of MOSFO is particularly challenging because of its wider oleic acid range (43.1–74.9%) and production via genetic modification or blending SFO/HOSFO. To address this, two multivariable PLS-R regression models were developed using ATR FT-IR and Fibre Optic Reflectance Spectroscopy (FORS) fingerprints, the latter obtained with a portable, cost-effective device. The results indicate that FORS could be used as a rapid quality control tool for on-site quantification. In contrast, ATR FT-IR is a more accurate tool for confirmation and quantification, achieving excellent results (Residual Predictive Deviation, RPD = 7.09 and Range Error Ratio, RER = 17.82).

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