Samples of olive oils (n = 67) from different qualities and samples of other vegetable edible oils (including soybean, sunflower, rapeseed, corn oil etc; n = 79) were used in this study as pure oils. Previous to spectroscopy analysis, a transesterification step was applied to the pure vegetable oil samples and all the different oil blends were then prepared to create in-house blended samples. Spectral acquisition was performed with typical parameters to collect the FTIR and Raman fingerprints. For the olive/non-olive classification model, three classification strategies have been applied: (i) one input-class (1iC) classification; (ii) two input-class (2iC) classification; and (iii) one input-class plus one ‘dummy’ class classification (or pseudo two input-class (p2iC) classification). The multivariate classification methods used were k-nearest neighbours (kNN), partial least squared-discriminant analysis (PLS-DA), one-class partial least squares (OCPLS), support vector machine classification (SVM-C), and soft independent modelling of class analogies (SIMCA). The multivariate quantification method used was partial least square-regression (PLS-R). FTIR fingerprints showed excellent classification ability to distinguish pure olive from non-olive oil. When PLS-DA or SVM-C techniques are applied, 100% of olive oil samples and 92% of other vegetable edible oils are correctly classified. In general FTIR fingerprints were more discriminative than Raman's in both classification and regression scenarios.
|Journal||LEBENSMITTEL-WISSENSCHAFT UND-TECHNOLOGIE-FOOD SCIENCE AND TECHNOLOGY|
|Early online date||29 Jul 2017|
|Publication status||Published - 01 Dec 2017|
- Pattern recognition
- Spectroscopic techniques
- Vegetable oils
Jiménez-Carvelo, A. M., Osorio, M. T., Koidis, A., González-Casado, A., & Cuadros-Rodríguez, L. (2017). Chemometric classification and quantification of olive oil in blends with any edible vegetable oils using FTIR-ATR and Raman spectroscopy. LEBENSMITTEL-WISSENSCHAFT UND-TECHNOLOGIE-FOOD SCIENCE AND TECHNOLOGY, 86, 174-184. https://doi.org/10.1016/j.lwt.2017.07.050