| dc.contributor.author | Jiménez Carvelo, Ana María | |
| dc.contributor.author | Arroyo Cerezo, Alejandra | |
| dc.contributor.author | Berzaghi, Paolo | |
| dc.contributor.author | Christian H., Pérez Beltrán | |
| dc.contributor.author | María, Tello Liébana | |
| dc.contributor.author | Cuadros Rodríguez, Luis | |
| dc.date.accessioned | 2025-09-02T06:48:27Z | |
| dc.date.available | 2025-09-02T06:48:27Z | |
| dc.date.issued | 2026 | |
| dc.identifier.citation | Talanta 297 (2026) 128730 | es_ES |
| dc.identifier.uri | https://hdl.handle.net/10481/105953 | |
| dc.description.abstract | Extra virgin olive oil (EVOO) is a high-value commodity traded worldwide; thus, accurate quality control analyses
are essential to ensure its genuineness. In this context, a universal analytical methodology capable of
delivering precise, fast and robust quality assessments would be highly beneficial for stakeholders. This study
extends the instrument-agnostizing methodology from chromatography to low-frequency field nuclear magnetic
resonance spectroscopy (LF-NMR). The proposed approach outlines the required steps to generate instrumentagnostic
fingerprints and demonstrates its application through the development of a global multivariate
model. For this purpose, 93 edible vegetable oils (76 EVOO and 17 non-olive ones) and 9 vegetable oil blends
were analyzed using two different LF-NMR spectrometers operating at 100 MHz and 80 MHz 1H field frequencies.
Each LF-NMR signal was subjected to the instrument-agnostizing methodology to obtain harmonized
LF-NMR fingerprints. To demonstrate the usefulness of this methodology, principal component analysis was
performed to assess the instrument-related variability before and after the instrument-agnostizing methodology.
Finally, partial least squares-discriminant analysis models were developed for each dataset of LF-NMR fingerprints
and evaluated using four main classification performance metrics (sensitivity, specificity, precision and
accuracy). The developed classification model with 100 MHz LF-NMR fingerprints achieved the required values
of quality performance metrics in a conformity scenario, showing for external and cross-instrument validation
sets a sensitivity of 96 % in both cases, and highlighting a precision of 100 % and 96 %, respectively. The
excellent outcome highlights the viability of the instrument-agnostizing methodology, enabling consistent and
accurate quality control across laboratories, regardless of the LF-NMR instrument used. | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | ELSEVIER | es_ES |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.title | Standardized analytical multivariate methods via agnostization of LF-NMR signals – Updating extra virgin olive oil authentication as practical example | es_ES |
| dc.type | journal article | es_ES |
| dc.rights.accessRights | open access | es_ES |
| dc.identifier.doi | https://doi.org/10.1016/j.talanta.2025.128730 | |
| dc.type.hasVersion | AM | es_ES |