Authenticity Verification of Commercial Poppy Seed Oil Using FT-IR Spectroscopy and Multivariate Classification

Authenticating poppy seed oil is essential to ensure product quality and prevent economic and health-related fraud. This study developed a non-targeted approach using FT-IR spectroscopy and pattern recognition analysis to verify the authenticity of poppy seed oil. Thirty-nine poppy seed oil samples were sourced from online stores and local markets in Turkiye. Gas chromatography–Flame Ionization Detector (GC-FID) analysis revealed adulteration in 23% of the samples, characterized by unusual fatty acid composition. Spectra of the oil samples were captured with a portable 5-reflection FT-IR sensor. Soft Independent Model of Class Analogies (SIMCA) was used to create class algorithms, successfully detecting all instances of adulteration. Partial least square regression (PLSR) models were then developed to predict the predominant fatty acid composition, achieving strong external validation performance (RCV = 0.96–0.99). The models exhibited low standard errors of prediction (SEP = 0.03–1.40%) and high predictive reliability (RPD = 2.9–6.1; RER = 8.4–13.1). This rapid, non-destructive method offers a reliable solution for authenticating poppy seed oil and predicting its fatty acid composition, presenting valuable applications for producers and regulatory authorities. This approach aids in regulatory compliance, protection of public health, and strengthening of consumer confidence by ensuring the authenticity of the product.