Abstract
EVOO is a fundamental ingredient for the Mediterranean diet representing one of the most important foods made in Italy for its quality and organoleptic properties. The purpose of this study was to classify 119 Italian EVOO samples, analysing values from VIS–NIR spectral data and sensory analysis. To obtain this classification, a multivariate metric index has been realized through the Soft Independent Modeling of Class Analogy (SIMCA) analysing the dataset of oils and classifying them into “Superior quality” and “Standard quality”. The result of the SIMCA has shown a correct classification of the samples, highlighting among those of the “Superior” class also top samples from the qualitative point of view, such as that of the Company “Passo della Palomba” and “Castello di Monte Vibiano Vecchio” who won a medal at the EVOO 2021 World Competition. This matrix index has been a useful tool to classify Italian EVOO samples following of their quality, differentiating the most promising oils from commercial ones and of lower quality.
Similar content being viewed by others
References
Marone E, Fiorino P (2012) Oleiculture in progress. Adv Hort Sci 26(3–4):163–175
Violino S, Pallottino F, Sperandio G, Figorilli S, Ortenzi L, Tocci F, Vasta S, Imperi G, Costa C (2020) A full technological traceability system for extra virgin olive oil. Foods 9(5):624
Sicari V, Leporini M, Giuffré AM, Aiello F, Falco T, Pagliuso MT, Ruffolo A, Reitano A, Romeo R, Tundis R, Loizzo MR (2021) Quality parameters, chemical compositions and antioxidant activities of Calabrian (Italy) monovarietal extra virgin olive oils from autochthonous (Ottobratica) and allochthonous (Coratina, Leccino, and Nocellara Del Belice) varieties. J Food Meas Charact 15(1):363–375
Fernandes GD, Ellis AC, Gámbaro A, Barrera-Arellano D (2018) Sensory evaluation of high-quality virgin olive oil: panel analysis versus consumer perception. Curr Opin Food Sci 21:66–71
International Olive Council (2019) IOC/T.15/NC No. 3/Rev. 15/ June 2019. Trade standard applying to olive oils and olive pomace oils
Taiti C, Marone E (2017) EVOO or not EVOO? A new precise and simple analytical 534 tool to discriminate virgin olive oils. Adv Hort Sci 31(4):329–337
International Olive Oil Council (2015) Sensory analysis of olive oil—method for the organoleptic assessment of virgin olive oil. IOC/T.20/Doc. No 15/Rev. 7
Gerhardt N, Schwolow S, Rohn S, Pérez-Cacho PR, Galán-Soldevilla H, Arce L, Weller P (2019) Quality assessment of olive oils based on temperature-ramped HS-GC-IMS and sensory evaluation: comparison of different processing approaches by LDA, kNN, and SVM. Food Chem 278:720–728
International Olive Council (2007) Sensory analysis of olive oil—standard-sensory analysis: general basic vocabulary, IOC/T.20/ Doc. No. 4/5/6/Rev. 1
Taiti C, Marone E, Fiorino P, Mancuso S (2022) The olive oil dilemma: To be or not to be EVOO? chemometric analysis to grade virgin olive oils using 792 fingerprints from PTR-ToF-MS. Food Cont 135:108817
Aparicio-Ruiz R, Morales MT, Aparicio R (2019) L’autenticità della qualità sensoriale dell’olio di oliva vergine richiede l’apporto della chimica? Eur J Lipid Sci Technol 121(12):1900202
Barbieri S, BrkićBubola K, Bendini A, Bučar-Miklavčič M, Lacoste F, Tibet U, Winkelmann O, García-González DL, Gallina Toschi T (2020) Alignment and proficiency of virgin olive oil sensory panels: the OLEUM approach. Foods 9(3):355
Carbone A, Cacchiarelli L, Sabbatini V (2018) Exploring quality and its value in the Italian olive oil market: a panel data analysis. Agric Food Econ 6(1):1–15
Pérez-Castaño E, Medina-Rodríguez S, Bagur-González MG (2019) Discrimination and classification of extra virgin olive oil using a chemometric approach based on TMS-4, 4’-desmetylsterols GC (FID) fingerprints of edible vegetable oils. Food Chem 274:518–525
Del Mar CM, Jurado-Campos N, Arce L, Arroyo-Manzanares N (2019) A robustness study of calibration models for olive oil classification: targeted and non-targeted fingerprint approaches based on GC-IMS. Food Chem 288:315–324
Quintanilla-Casas B, Bustamante J, Guardiola F, García-González DL, Barbieri S, Bendini A, GallinaToschi T, Vichi S, Tres A (2020) Virgin olive oil volatile fingerprint and chemometrics: towards an instrumental screening tool to grade the sensory quality. LWT-Food Sci Technol 121:108936
Liu N, Koot A, Hettinga K, De Jong J, Van Ruth SM (2018) Portraying and tracing the impact of different production systems on the volatile organic compound composition of milk by PTR-(Quad) MS and PTR-(ToF) MS. Food Chem 239:201–207
Violino S, Benincasa C, Taiti C, Ortenzi L, Pallottino F, Marone E, Mancuso S, Costa C (2021) AI-based hyperspectral and VOCs assessment approach to identify adulterated extra virgin olive oil. Eur Food Res Technol 247(4):1013–1022
Violino S, Taiti C, Ortenzi L, Marone E, Pallottino F, Costa C (2022) A ready-to-use portable VIS–NIR spectroscopy device to assess superior EVOO quality. Eur Food Res Technol 248:1–9
Giovenzana V, Beghi R, Romaniello R, Tamborrino A, Guidetti R, Leone A (2018) Use of visible and near infrared spectroscopy with a view to on-line evaluation of oil content during olive processing. Biosyst Eng 172:102–109
Tahir HE, Xiaobo Z, Jianbo X, Mahunu GK, Jiyong S, Xu JL, Sun DW (2019) Recent progress in rapid analyses of vitamins, phenolic, and volatile compounds in foods using vibrational spectros-copy combined with chemometrics: a review. Food Anal Methods 12(10):2361–2382
Vanstone N, Moore A, Martos P, Neethirajan S (2018) Detection of the adulteration of extra virgin olive oil by near infrared spectroscopy and chemometric techniques. Food Qual Saf 2(4):189–198
Jolayemi OS, Tokatli F, Buratti S, Alamprese C (2017) Discriminative capacities of infrared spectroscopy and e-nose on Turkish olive oils. Eur Food Res Technol 243(11):2035–2042
Abu-Khalaf N, Hmidat M (2020) Visible/Near Infrared (VIS/NIR) spectroscopy as an optical sensor for evaluating olive oil quality. Comput Electron Agric 173:105445
Borghi FT, Santos PC, Santos FD, Nascimento MH, Correa T, Cesconetto M, Pires AA, Ribeiro VFN, Lacerda V, Romao V, Filgueiras PR (2020) Quantification and classification of vegetable oils in extra virgin olive oil samples using a portable near-infrared spectrometer associated with chemometrics. Microchem J 159:105544
Cecchini C, Antonucci F, Costa C, Marti A, Menesatti P (2021) Application of near-infrared handheld spectrometers to predict semolina quality. J Sci Food Agric 101(1):151–157
Sinelli N, Casiraghi E, Tura D, Downey G (2008) Characterisation and classification of Italian virgin olive oils by near-and mid-infrared spectroscopy. J Near Infrared Spectrosc 16(3):335–342
Ozcan-Sinir G (2020) Detection of adulteration in extra virgin olive oil by selected ion flow tube mass spectrometry (SIFT-MS) and chemometrics. Food Contr 118:107433
Peña F, Cárdenas S, Gallego M, Valcárcel M (2005) Direct olive oil authentication: detection of adulteration of olive oil with hazelnut oil by direct coupling of headspace and mass spectrometry, and multivariate regression techniques. J Chromatogr A 1074(1–2):215–221
Pagano M, Tomasone R, Cedrola C, Fedrizzi M, Veneziani G, Servili M. (2019) use of ultrasound in the extraction process of virgin olive oil and influence on malaxation time. In: International Mid-Term Conference of the Italian Association of Agricultural Engineering, Springer, Cham pp.703–712
Wold S, Sjostrom M (1977) SIMCA: A method for analyzing chemical data in terms of similarity and analogy. In: BR Kowalski (ed) Chemometrics: Theory and Application. Washington, DC, USA: American Chemical Society Symposium
Zanetti M, Costa C, Greco R, Grigolato S, Ottaviani Aalmo G, Cavalli R (2017) How wood fuels’s quality relates to the standards: a class-modelling approach. Energies 10:1455
Kennard RW, Stone LA (2017) Computer aided design of experiments. Technometrics 11:137–148
Casale M, Casolino C, Oliveri P, Forina M (2010) The potential of coupling information using three analytical techniques for identifying the geographical origin of Liguria extra virgin olive oil. Food Chem 118(1):163–170
Casale M, Casolino C, Ferrari G, Forina M (2008) Near infrared spectroscopy and class modelling techniques for the geographical authentication of Ligurian extra virgin olive oil. J Near Infrared Spectrosc 16(1):39–47
Uluata S, Altuntaş U, Özçelik B (2021) Characterization of Turkish extra virgin olive oils and classification based on their growth regions coupled with multivariate analysis. Food Anal Method 14(8):1682–1694
Gertz C, Matthäus B, Willenberg I (2020) Detection of soft-deodorized olive oil and refined vegetable oils in virgin olive oil using near infrared spectroscopy and traditional analytical parameters. European Eur J Lipid Sci Technol 122(6):1900355
Windarsih A, Arsanti Lestari L, Erwanto Y, Rosiana Putri A, Irnawati, Ahmad Fadzillah, Rahmawati N, Rohman A (2022) Application of Raman spectroscopy and chemometrics for quality controls of fats and oils: a review. Food Rev Int. https://doi.org/10.1080/87559129.2021.2014860
Acknowledgements
The authors would like to acknowledge all the farms which sent free the EVOO to be analyzed. This research was funded by the Italian Ministry of Agriculture (MiPAAF), Grant number D.M. n.12479/2018 of project INFOLIVA.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Compliance with ethics requirements
No ethical issues are applicable to our work.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Violino, S., Taiti, C., Marone, E. et al. A statistical tool to determine the quality of extra virgin olive oil (EVOO). Eur Food Res Technol 248, 2825–2832 (2022). https://doi.org/10.1007/s00217-022-04092-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00217-022-04092-x