A robustness study of calibration models for olive oil classification: Targeted and non-targeted fingerprint approaches based on GC-IMS

•701 olive oil samples were differentiated depending on their quality.•GC-IMS data were subjected to two chemometric approaches to classify olive oils.•A non-targeted fingerprinting approach and a targeted one based on markers were used.•In general the markers approach is more suitable for implementation in the industry.•Calibration models from 2014/15 required recalibration to predict oils from 2015/16. 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.