@misc{10481/105953,
year = {2026},
url = {https://hdl.handle.net/10481/105953},
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.},
publisher = {ELSEVIER},
title = {Standardized analytical multivariate methods via agnostization of LF-NMR signals – Updating extra virgin olive oil authentication as practical example},
doi = {https://doi.org/10.1016/j.talanta.2025.128730},
author = {Jiménez Carvelo, Ana María and Arroyo Cerezo, Alejandra and Berzaghi, Paolo and Christian H., Pérez Beltrán and María, Tello Liébana and Cuadros Rodríguez, Luis},
}