NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil
This study investigates the potential of 1H NMR spectroscopy for predicting key chemical and sensory attributes in olive oil. By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled wi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025001126 |
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| _version_ | 1849704445832593408 |
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| author | Gaia Meoni Leonardo Tenori Francesca Di Cesare Stefano Brizzolara Pietro Tonutti Chiara Cherubini Laura Mazzanti Claudio Luchinat |
| author_facet | Gaia Meoni Leonardo Tenori Francesca Di Cesare Stefano Brizzolara Pietro Tonutti Chiara Cherubini Laura Mazzanti Claudio Luchinat |
| author_sort | Gaia Meoni |
| collection | DOAJ |
| description | This study investigates the potential of 1H NMR spectroscopy for predicting key chemical and sensory attributes in olive oil. By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled with machine learning algorithms for 50 distinct olive oil quality parameters, including physicochemical properties, fatty acid composition, total polyphenols, tocopherols, and sensory attributes. We applied Random Forest regression models to correlate NMR spectra with these parameters, achieving promising results, particularly for predicting major fatty acids, total polyphenols, and tocopherols. We have also found the collected data to be highly effective in classifying olive cultivars and the years of harvest. Our findings highlight the potential of NMR spectroscopy as a rapid, non-destructive, and environmentally friendly tool for olive oil quality assessment. This study introduces a novel approach that combines machine learning with 1H NMR spectral analysis to correlate analytical data for predicting essential qualitative parameters in olive oil. By leveraging 1H NMR spectra as predictive proxies, this methodology offers a promising alternative to traditional assessment techniques, enabling rapid determination of several parameters related to chemical composition, sensory attributes, and geographical origin of olive oil samples. |
| format | Article |
| id | doaj-art-8201a4c062ec47c5bc266d15829e6c6e |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-8201a4c062ec47c5bc266d15829e6c6e2025-08-20T03:16:46ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01271359136910.1016/j.csbj.2025.03.045NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oilGaia Meoni0Leonardo Tenori1Francesca Di Cesare2Stefano Brizzolara3Pietro Tonutti4Chiara Cherubini5Laura Mazzanti6Claudio Luchinat7Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino, Florence 50019, Italy; Corresponding author.Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino, Florence 50019, ItalyDepartment of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino, Florence 50019, ItalyInstitue of Crop Sciences, Scuola Superiore Sant'Anna, Pisa 56127, ItalyInstitue of Crop Sciences, Scuola Superiore Sant'Anna, Pisa 56127, ItalyANALYTICAL Food, Scandicci, Florence 50018, ItalyANALYTICAL Food, Scandicci, Florence 50018, ItalyGiotto Biotech S.r.l., Sesto Fiorentino, Florence 50019, ItalyThis study investigates the potential of 1H NMR spectroscopy for predicting key chemical and sensory attributes in olive oil. By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled with machine learning algorithms for 50 distinct olive oil quality parameters, including physicochemical properties, fatty acid composition, total polyphenols, tocopherols, and sensory attributes. We applied Random Forest regression models to correlate NMR spectra with these parameters, achieving promising results, particularly for predicting major fatty acids, total polyphenols, and tocopherols. We have also found the collected data to be highly effective in classifying olive cultivars and the years of harvest. Our findings highlight the potential of NMR spectroscopy as a rapid, non-destructive, and environmentally friendly tool for olive oil quality assessment. This study introduces a novel approach that combines machine learning with 1H NMR spectral analysis to correlate analytical data for predicting essential qualitative parameters in olive oil. By leveraging 1H NMR spectra as predictive proxies, this methodology offers a promising alternative to traditional assessment techniques, enabling rapid determination of several parameters related to chemical composition, sensory attributes, and geographical origin of olive oil samples.http://www.sciencedirect.com/science/article/pii/S2001037025001126NMREVOOOlive oil qualityChemometricsCultivarHarvesting |
| spellingShingle | Gaia Meoni Leonardo Tenori Francesca Di Cesare Stefano Brizzolara Pietro Tonutti Chiara Cherubini Laura Mazzanti Claudio Luchinat NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil Computational and Structural Biotechnology Journal NMR EVOO Olive oil quality Chemometrics Cultivar Harvesting |
| title | NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil |
| title_full | NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil |
| title_fullStr | NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil |
| title_full_unstemmed | NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil |
| title_short | NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil |
| title_sort | nmr based metabolomic approach to estimate chemical and sensorial profiles of olive oil |
| topic | NMR EVOO Olive oil quality Chemometrics Cultivar Harvesting |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025001126 |
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