Abstract

There is a lack of official analytical methods to classify olive oil samples according to its organoleptic quality. For that, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was applied and two devices with different technical configurations were compared; a commercial isothermal HS-GC-IMS with a 5 cm drift tube and a new HS-GC-IMS prototype, which works at ramped temperature and presents a 10 cm drift tube. Both devices were compared in terms of resolution, precision and classification rates (CRs) using a 30 m non-polar capillary column. Our results suggested that the resolution in the chromatographic separation was improved using the device in which a ramped temperature can be performed in a shorter analysis time and the resolution in the ion mobility separation was also better. The repeatability values (RSD) for the standards quality control were lower than 10% in both cases. Moreover, as revealed in the olive oil 2D-maps, a higher number of markers were detected by the isothermal HS-GC-IMS (91) than that for the ramped temperature device (76), probably explained by the different sensitivity of both devices. These markers were used to build chemometric models based on orthogonal partial least squares-discriminant analysis. The olive oil samples in the validation set could be classified into extra virgin, virgin and lampante, defective and non-defective, as well as edible and non-edible with CRs ranging from 83 to 100%. In order to improve the resolution, a 60 m non-polar column was used in the ramped temperature HS-GC-IMS and enabled an increase in the visualized markers to 128, but it required a longer analysis time. The repeatability values were also lower than 10% and the CRs were improved (94–100%). Finally, 21, 22 and 25 olive oil aroma compounds were identified using the isothermal (30 m column) and the ramped temperature HS-GC-IMS with 30 and 60 m columns, respectively. Although some of these markers may give clues about the olive oil category, the chemometrics processing approach offers an objective tool for a robust classification and it is easily transferable to the industry.

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