Assessing the varietal origin of extra-virgin olive oil using liquid chromatography fingerprints of phenolic compound, data fusion and chemometrics

• Published in: Food chemistry 2017-01, Vol.215, p.245-255
• Main Authors: Bajoub, Aadil, Medina-Rodríguez, Santiago, Gómez-Romero, María, Ajal, El Amine, Bagur-González, María Gracia, Fernández-Gutiérrez, Alberto, Carrasco-Pancorbo, Alegría
• Format: Article
• Language: English
• Subjects: Chemometrics; Chromatography, High Pressure Liquid - methods; Cluster Analysis; Data fusion; Discriminant Analysis; High performance liquid chromatography; Monovarietal extra-virgin olive oils; Olive Oil - chemistry; Phenolic compounds fingerprints; Phenols - analysis; Principal Component Analysis; Varietal origin
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2016.07.140

•HPLC fingerprinting approach with the use of chemometric pattern recognition tools.•Fingerprints of phenolic compounds were monitored using dual detection (DAD and FLD).•Data fusion applied to HPLC (DAD and FLD) matrices.•Best combinations (LC data+statistical tool) for varietal authentication indicated. High Performance Liquid Chromatography (HPLC) with diode array (DAD) and fluorescence (FLD) detection was used to acquire the fingerprints of the phenolic fraction of monovarietal extra-virgin olive oils (extra-VOOs) collected over three consecutive crop seasons (2011/2012-2013/2014). The chromatographic fingerprints of 140 extra-VOO samples processed from olive fruits of seven olive varieties, were recorded and statistically treated for varietal authentication purposes. First, DAD and FLD chromatographic-fingerprint datasets were separately processed and, subsequently, were joined using “Low-level” and “Mid-Level” data fusion methods. After the preliminary examination by principal component analysis (PCA), three supervised pattern recognition techniques, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogies (SIMCA) and K-Nearest Neighbors (k-NN) were applied to the four chromatographic-fingerprinting matrices. The classification models built were very sensitive and selective, showing considerably good recognition and prediction abilities. The combination “chromatographic dataset+chemometric technique” allowing the most accurate classification for each monovarietal extra-VOO was highlighted.