| Title: | A robustness study of calibration models for olive oil classification: Targeted and non-targeted fingerprint approaches based on GC-IMS |
| Author(s): | Contreras MDM; Jurado-Campos N; Arce L; Arroyo-Manzanares N; |
| Address: | "Department of Analytical Chemistry, Institute of Fine Chemistry and Nanochemistry, University of Cordoba, Campus de Rabanales, Marie Curie Annex Building, E-14071 Cordoba, Spain; Department of Chemical, Environmental and Materials Engineering, Universidad de Jaen, Campus Las Lagunillas, 23071 Jaen, Spain. Department of Analytical Chemistry, Institute of Fine Chemistry and Nanochemistry, University of Cordoba, Campus de Rabanales, Marie Curie Annex Building, E-14071 Cordoba, Spain. Department of Analytical Chemistry, Institute of Fine Chemistry and Nanochemistry, University of Cordoba, Campus de Rabanales, Marie Curie Annex Building, E-14071 Cordoba, Spain; Department of Analytical Chemistry, Faculty of Chemistry, Regional Campus of International Excellence 'Campus Mare-Nostrum', University of Murcia, E-30100 Murcia, Spain. Electronic address: natalia.arroyo@um.es" |
| DOI: | 10.1016/j.foodchem.2019.02.104 |
| ISSN/ISBN: | 1873-7072 (Electronic) 0308-8146 (Linking) |
| Abstract: | "The dual separation in gas chromatography-ion mobility spectrometry generates complex multi-dimensional data, whose interpretation is a challenge. In this work, two chemometric approaches for olive oil classification are compared to get the most robust model over time: i) an non-targeted fingerprinting analysis, in which the overall GC-IMS data was processed and ii) a targeted approach based on peak-region features (markers). A total of 701 olive samples from two harvests (2014-2015 and 2015-2016) were analysed and processed by both approaches. The models built with data samples of 2014-2015 showed that both approaches were suitable for samples classification (success >74%). However, when these models were applied for classifying samples from 2015-2016, better values were obtained using markers. The combination of data from the two harvests to build the chemometric models improved the percentages of success (>90%). These results confirm the potential of GC-IMS based approaches for olive oil classification" |
| Keywords: | "Calibration Discriminant Analysis *Gas Chromatography-Mass Spectrometry/standards Ion Mobility Spectrometry Least-Squares Analysis Models, Chemical Olive Oil/chemistry/*classification/standards Principal Component Analysis Volatile Organic Compounds/analy;" |
| Notes: | "MedlineContreras, Maria Del Mar Jurado-Campos, Natividad Arce, Lourdes Arroyo-Manzanares, Natalia eng England 2019/03/25 Food Chem. 2019 Aug 1; 288:315-324. doi: 10.1016/j.foodchem.2019.02.104. Epub 2019 Mar 1" |
