Raman spectroscopy and machine-learning for edible oils evaluation

Abstract

Because of the important advantages as rapidity, cost effectiveness and no sample preparation necessity, encountered in most of the cases, Raman spectroscopy gained more and more attention during the last years with regard to its application in food and beverages authenticity. Vegetable cold-pressed oils obtained from: sesame, hemp, walnut, linseed, pumpkin and sea buckthorn have gained increased attention in consumer interest due to their high nutrient value and health benefits. The high commercial value of these, brought the temptation from some unfair producers and sellers to gain an illegal profit by replacing the raw material of these oils by cheaper ones (i.e. sunflower). Here a new approach based on the rapid processing of Raman spectra using Machine Learning algorithms, for edible oil authentication was developed and successfully tested. Through this approach, not only the adulteration detection was achieved but also an initial estimation of its magnitude.

Keywords: Adulteration; Authenticity control; Edible oils; Machine learning; Raman spectroscopy.