@misc{10481/111483,
year = {2026},
month = {3},
url = {https://hdl.handle.net/10481/111483},
abstract = {The increasing demand for food safety/quality and environmental sustainability requires the development of
rapid, reliable and green analytical screening methods for food quality assessment. Although precise, traditional
reference methods are often time-consuming, resource-intensive and environmentally taxing. To address this
challenge, we developed fast, reliable and sustainable analytical screening methods for assuring the extra virgin
olive oil (EVOO) quality category, a strictly regulated sector.
The potential of two analytical techniques by using miniaturised instrumentation was explored: portable
spatially offset Raman spectrometry (SORS) and benchtop low-field nuclear magnetic resonance (LF-NMR)
spectrometry. 387 olive oil samples of different commercial categories were analysed. Screening methods were
developed to ensure the compliance to EVOO category against three quality characteristics of olive oils: acidity,
peroxide value and fatty acid ethyl esters. The core of the methodology involved building multivariate classification
models based on machine learning. Conservative screening thresholds were established to ensure the
highest probability of correctly classifying compliant samples to the EVOO category. The performance of the
models was evaluated, achieving 100 % precision in almost all cases, outstanding particularly in screening olive
oils based on acidity. Furthermore, whiteness evaluation yielded scores above 80 %, revealing the efficiency and
sustainability of the proposed methods, and the high savings indices showed their potential to reduce the number
of samples requiring conventional reference analyses, which are environmentally unfriendly.
Results demonstrate that both SORS and LF-NMR, coupled with a multivariate approach, offer a viable, green
and cost-effective alternative to support routine quality control of olive oils, significantly optimising analytical
resources.},
organization = {European Union NextGenerationEU/PRTR},
organization = {MICIU/AEI/10.13039/501100011033 CPP2021–008672, RYC2021-031993-I},
organization = {Spanish Ministry of Universities RYC2021-031993-I},
publisher = {Elsevier},
keywords = {Screening analytical methods},
keywords = {Spatially offset Raman spectroscopy},
keywords = {Low field nuclear magnetic resonance},
title = {Reliable spectroscopic screening protocols for the eco-design of analytical workflows in laboratories ensuring EVOO quality categorisation},
doi = {10.1016/j.greeac.2026.100331},
author = {Arroyo Cerezo, Alejandra and Cuadros Rodríguez, Luis and Cabeza Rodríguez, Celia and Roca Nasser, Esteban A and Tello Liébana, María and González Casado, Antonio and Jiménez Carvelo, Ana María},
}