Fluorescence Spectroscopy Coupled with Artificial Neural Networks to Detect Refined Palm Oil Adulteration in Extra Virgin Olive Oil: A Case Study on Commercialized Oils in Cameroon

Abstract

The adulteration of extra virgin olive oil remains a major problem for the food industry. This practice has both health and economic consequences. In countries which import extra virgin olive oil, the problem is even more serious. The objective of this study was to use a laboratory assembled fluorometer to detect the presence of refined palm oil in extra virgin olive oil commercialized in Cameroon. The fluorescence spectra obtained with an excitation wavelength of 380 nm reveal two principal emission peaks: one at 497.18 nm, attributed to oxidation products in refined palm oil, and another at 673.75 nm, attributed to chlorophylls in extra virgin olive oil. The fluorescence intensities of the adulterated samples increased with the concentration of refined palm oil in extra virgin olive oil. Principal component analysis, employing two principal components that accounted for 95% of the total variance, enabled clear visualization of the clusters of adulterated samples. The risk of misclassification between classes, as indicated by principal component analysis, was mitigated by a self-organizing maps neural network. Finally, a multilayer perceptron neural network demonstrated high efficacy in predicting with a determination coefficient of 0.998, the concentration of refined palm oil in extra virgin olive oil. To the best of our knowledge, this study represents the first application of fluorescence spectroscopy coupled with artificial neural networks to detect the adulteration of extra virgin olive oil with refined palm oil. The results obtained prove the significance of fluorescence and neural network tools for the reliable characterization of vegetable oils, opening promising perspectives for their application in the food industry.