Instrument-agnostic multivariate models from normal phase liquid chromatographic fingerprinting. A case study: Authentication of olive oil Pérez Beltrán, Christian Hazael Jiménez Carvelo, Ana María Martín Torres, Sandra Ortega Gavilán, Fidel Cuadros Rodríguez, Luis Max 6) Instrument-agnostic chromatographic fingerprints Instrument-independent multivariate models Data mining and chemometrics Olive oil authentication Funding for open access charge: University of Granada / CBUA. The application of non-targeted analytical strategies such as instrumental chromatographic fingerprinting is commonly applied in the field of food authentication/food quality. Although the multivariate methods developed to date are able to solve any authenticity problem, they remain dependent on the instrument state where the signals were acquired, which difficult their transfer to other laboratories. The aim of this research is to develop multivariate models independent of both instrument state and time at which the signals were acquired. For this, chromatograms obtained from the polar fraction of different olive oil samples analysed by (NP)UHPLC-UV/Vis are transformed to instrument-agnostic fingerprints. Instrument independence is achieved by transferring the chromatographic behaviour of an ’ad-hoc’ external standards mixture solution analysed throughout an analysis sequence to the remaining analysed samples. The SIMCA models developed from the chromatographic fingerprint matrix before and after instrumentagnostizing showed significant differences in the number of samples classified as "inconclusive", with the after model showing the best results. Furthermore, the PLS-DA and SVM models obtained before and after signal instrument-agnostizing showed similar outcomes. The main conclusion of the work has been to verify that the instrument-agnostizing methodology could allow the building of multivariate classification models which could be transferred among different laboratories as they are not influenced by the signal acquisition time. 2022-05-19T11:15:12Z 2022-05-19T11:15:12Z 2022-03-08 info:eu-repo/semantics/article Christian H. Pérez-Beltrán... [et al.]. Instrument-agnostic multivariate models from normal phase liquid chromatographic fingerprinting. A case study: Authentication of olive oil, Food Control, Volume 137, 2022, 108957, ISSN 0956-7135, [https://doi.org/10.1016/j.foodcont.2022.108957] http://hdl.handle.net/10481/74930 10.1016/j.foodcont.2022.108957 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España Elsevier