Standardized analytical multivariate methods via agnostization of LF-NMR signals – Updating extra virgin olive oil authentication as practical example
Year: 2025
Type: article
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 instrument-agnostic 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 1 H 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.
Source: Talanta
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $4,610
Award: G5273101754
OpenAlex ID: W4413365140
