Verifying the Geographical Origin and Authenticity of Greek Olive Oils by Means of Optical Spectroscopy and Multivariate Analysis

Kontzedaki, Renate; Orfanakis, Emmanouil; Sofra-Karanti, Georgia; Stamataki, Katerina; Philippidis, Aggelos; Zoumi, Aikaterini; Velegrakis, Michalis

doi:10.3390/molecules25184180

Cited by 15 publications

(16 citation statements)

References 25 publications

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“…A presentation of the techniques used can be found in a recent report by Cecchi, L. et al 22 . Very recently, after the initial submission of the present work, some works have appeared in the literature concerning the geographical discrimination of EVOOs, using squalene, tocopherol, and fatty acid composition measurements by chromatographic techniques 46 , optical spectroscopies 47 and isotopic traceability ( 13 C and 18 O) 48 . All these works report high classification accuracies.…”

Section: Discussionmentioning

confidence: 99%

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Gyftokostas

Stefas

Kokkinos

et al. 2021

Sci Rep

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Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.

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Section: Discussionmentioning

confidence: 99%

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Gyftokostas

Stefas

Kokkinos

et al. 2021

Sci Rep

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“…Specifically, the observed peaks at 454 and 480 nm are assigned to carotenoids contained in the olive oil samples. The peaks at 416, 535, 610 and 670 nm correspond to the presence of chlorophyll in the samples [13,30]. The evaluation of the absorption spectroscopic data was performed via model-based feature selection using Random Forest with 100 trees, minimum split size = 9 and minimum leaf size = 3 and via classification using SVM with linear kernel and regularization parameter C = 1.5.…”

Section: Absorption Spectroscopymentioning

confidence: 99%

“…The EVOO samples from the three different varieties were also analyzed using Raman spectroscopy. Figure 7 depicts a characteristic Raman spectrum of olive oil in the range 1000-1700 cm −1 after the background subtraction, using the Zhangfit technique [31] Table 4 shows the characteristic Raman bands and their corresponding assignments to vibrational modes of the functional groups [13,14,32]. Those bands correspond to the fatty acids contained in the sample, with the peaks located at 1265 and 1657 cm −1 corresponding to the unsaturated fatty acids.…”

Section: Raman Spectroscopymentioning

confidence: 99%

“…Over the last several years, the application of optical spectroscopic techniques combined with machine learning methods has been used to deal with various analytical problems of interest in the olive oil authenticity sector. Specifically, optical spectroscopic techniques were used to distinguish the EVOO samples based on their geographical origin [13]. In addition, they have been used for detection of adulteration in extra virgin olive oil [14] and for the verification of its quality parameters [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Evaluation of Different Targeted and Untargeted Analytical Approaches to Assess Greek Extra Virgin Olive Oil Quality and Authentication

et al. 2022

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Extra virgin olive oil (EVOO) is a key component of the Mediterranean diet, with several health benefits derived from its consumption. Moreover, due to its eminent market position, EVOO has been thoroughly studied over the last several years, aiming at its authentication, but also to reveal the chemical profile inherent to its beneficial properties. In the present work, a comparative study was conducted to assess Greek EVOOs’ quality and authentication utilizing different analytical approaches, both targeted and untargeted. 173 monovarietal EVOOs from three emblematic Greek cultivars (Koroneiki, Kolovi and Adramytiani), obtained during the harvesting years of 2018–2020, were analyzed and quantified as per their fatty acids methyl esters (FAMEs) composition via the official method (EEC) No 2568/91, as well as their bioactive content through liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) methodology. In addition to FAMEs analysis, EVOO samples were also analyzed via HRMS-untargeted metabolomics and optical spectroscopy techniques (visible absorption, fluorescence and Raman). The data retrieved from all applied techniques were analyzed with Machine Learning methods for the authentication of the EVOOs’ variety. The models’ predictive performance was calculated through test samples, while for further evaluation 30 commercially available EVOO samples were also examined in terms of variety. To the best of our knowledge, this is the first study where different techniques from the fields of standard analysis, spectrometry and optical spectroscopy are applied to the same EVOO samples, providing strong insight into EVOOs chemical profile and a comparative evaluation through the different platforms.

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“…18 Visible and Raman spectroscopy in combination with multivariate statistical analysis could distinguish olive oils produced in different Greek geographical regions and classify oils from Crete, Peloponnese and Lesvos. 19 Raman spectroscopy also allowed the differentiation of Andalusian 20 and French PDO oils. 21 Some data fusion approaches have been proposed to assess olive oil's geographical origins by merging the outcomes of NIR and MIR spectroscopy, [22][23][24] headspace mass spectrometry (HS-MS) and visible spectroscopy, 25 multiple electronic sensor devices , 26 as well as visible spectroscopic fingerprints and chemical descriptors.…”

Section: Introductionmentioning

confidence: 99%

Geographical identification of Italian extra virgin olive oil by the combination of near infrared and Raman spectroscopy: A feasibility study

Bragolusi

Massaro

Zacometti

et al. 2021

Journal of Near Infrared Spectroscopy

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The potential of the combination of near infrared (NIR) spectroscopy and Raman spectroscopy to differentiate Italian and Greek extra virgin olive oil (EVOO) by geographical origin was evaluated. Near infrared spectroscopy and Raman fingerprints of both study groups (extra virgin olive oil from the two countries) were pre-processed, merged by low-level and mid-level data fusion strategies and submitted to partial least-squares discriminant analysis. The classification models were cross-validated. After low-level data fusion, the partial least-squares discriminant analysis correctly predicted the geographical origins of extra virgin olive oils in cross-validation with 93.9% accuracy, while sensitivity and specificity were 77.8% and 100%, respectively. After mid-level data fusion, the partial least-squares discriminant analysis correctly predicted the geographical origins of extra virgin olive oils in cross-validation with 97.0% accuracy, while sensitivity and specificity were 88.9% and 100%, respectively. In this preliminary study, improved discrimination of Italian extra virgin olive oils was achieved by the synergism of near infrared spectroscopy and Raman spectroscopy as compared to the discrimination obtained by the separate laboratory techniques. This pilot study shows encouraging results that could open a new avenue for the authentication of Italian extra virgin olive oil.

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Verifying the Geographical Origin and Authenticity of Greek Olive Oils by Means of Optical Spectroscopy and Multivariate Analysis

Cited by 15 publications

References 25 publications

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Comparative Evaluation of Different Targeted and Untargeted Analytical Approaches to Assess Greek Extra Virgin Olive Oil Quality and Authentication

Geographical identification of Italian extra virgin olive oil by the combination of near infrared and Raman spectroscopy: A feasibility study

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