Adulteration detection and quantification in olive oil using excitation-emission matrix fluorescence spectroscopy and chemometrics

This study explores the application of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric techniques for the rapid identification and quantification of adulteration in olive oil, particularly in cases where sample quantities are limited. Soybean oil, peanut oil, and linseed oil are introduced into olive oils to simulate various adulterated samples. Our approach involves the application of parallel factor analysis (PARAFAC) for data decomposition, with a following focus on establishing correlations between the decomposed components and the actual adulterated components. This is accomplished through a thorough comparison of the spectral characteristics and score results of the decomposed components, allowing us to attribute them to the actual adulterated components and thereby ultimately enabling us to quantify the levels of actual adulteration. The results proves that EEMF spectroscopy combined with the proposed analysis methods serves as a powerful tool for the rapid detection and quantification of adulteration in olive oil. We also utilize principal component analysis (PCA) to cluster adulterated samples and identify efficient excitation wavelengths, and conduct a comparative analysis between PCA and PARAFAC methods. This study offers a novel perspective and method for quantitatively analyzing adulterants in olive oil through spectral detection, holding the promise of practical application in real-world detection scenarios.