Cultivar and geographical origin authentication of Italian extra virgin olive oil using front-face fluorescence spectroscopy and chemometrics

Riza, Dimas Firmanda Al; Kondo, Naoshi; Rotich, Vincent; Perone, Claudio; Giametta, Ferruccio

doi:10.1016/j.foodcont.2020.107604

Cited by 41 publications

(18 citation statements)

References 26 publications

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“…The second region with an excitation range of 290-360 nm and an intermediate emission range at 350-480 nm is attributed to the oxidation products [15]. Moreover, as reported in the literature, the excitation range from 300 to 400 nm with emission at 450-580 nm is due to the excitation of vitamin E and carotenoids [18]. In the spectral region that is selected to monitor the fluorescence of EVOO the intense emission of pigments such as chlorophyll is absent.…”

Section: Fluorescence Spectroscopymentioning

confidence: 63%

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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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Section: Fluorescence Spectroscopymentioning

confidence: 63%

“…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]. Moreover, these techniques have also been shown to be efficient tools for the identification of EVOOs based on their variety [17,18].…”

Section: Introductionmentioning

confidence: 99%

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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“…The objective of PLS-DA is to find a model that separates classes of observations based on their X variables. [29][30] This multivariate statistical method has already been performed on fluorescence spectra to determine the geographical origin of oils, [31][32] and to monitor the freshness of olive oil, [33] beef samples. [34] In this work, the X matrix consists of the combination of FAI, PV, p-AnV, and TBA parameters for chemical analyses and the intensity for fluorescence spectra.…”

Section: Mathematical Analyses Of Data Tablesmentioning

confidence: 99%

Front Face Fluorescence Spectroscopy Combined with PLS‐DA Allows to Monitor Chemical Changes of Edible Vegetable Oils during Storage at 60 °C

Botosoa

Chèné

Karoui

2021

Euro J Lipid Sci & Tech

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In this work, front‐face fluorescence spectroscopy coupled with chemometric tools are used for the monitoring of rapeseed, sunflower, extra virgin olive, and linseed oil. The oil samples are heated at 60 °C for up to 15 days. The fluorescence excitation spectra are scanned in the range of 270–420 nm and 250–310 nm after emission wavelength set at 450 and 330 nm, respectively. The primary and secondary lipid oxidation products are also determined on the same oil samples. By applying partial least square discriminant analysis (PLS‐DA) to each spectral collection obtained after emission set at 450 and 330 nm, clear discrimination of edible oil samples according to their botanical types, and sometimes aging, is obtained. Practical Applications: This study shows that oxidation of rapeseed, sunflower, extra virgin olive, and linseed oil monitored different trends due to their difference in composition. The presented results demonstrated the capability of the front‐face fluorescence spectroscopy to differentiate four vegetable oils contained in capped and uncapped flasks indicating the evolution of primary and secondary indicators of oxidation during the storage period. The technology can be exploited at the industrial level for monitoring in a non‐destructive manner the level of oxidation of edible oil.

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“…14 Applications include (non-exhaustive list): classification of different quality grades of olive oils, 15,16 adulteration detection, [17][18][19] monitoring of the oxidation processes, 7,20,21 deterioration monitoring, 22 and geographical origin determination. [23][24][25] The complexity in acquiring high-quality data (especially of EEMs) and in data post-processing limit largely the accessibility of these methods. This work presents an easy and non-destructive method to determine the quality of olive oil from a simple single fluorescence spectrum.…”

Section: Introductionmentioning

confidence: 99%

Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil

Arnaud¹,

Michelucci²,

Venturini

et al. 2022

Optical Sensing and Detection VII

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Olive oil is an important commodity in the world, and its demand has grown substantially in recent years. As of today, the determination of olive oil quality is based on both chemical analysis and organoleptic evaluation from specialized laboratories and panels of experts, thus resulting in a complex and time-consuming process. This work presents a new compact and low-cost sensor based on fluorescence spectroscopy and artificial neural networks that can perform olive oil quality assessment. The presented sensor has the advantage of being a portable, easy-to-use, and low-cost device, which works with undiluted samples, and without any pre-processing of data, thus simplifying the analysis to the maximum degree possible. Different artificial neural networks were analyzed and their performance compared. To deal with the heterogeneity in the samples, as producer or harvest year, a novel neural network architecture is presented, called here conditional convolutional neural network (Cond-CNN). The presented technology is demonstrated by analyzing olive oils of different quality levels and from different producers: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). The sensor classifies the oils in the three mentioned classes with an accuracy of 82%. These results indicate that the Cond-CNN applied to the data obtained with the low-cost luminescence sensor, can deal with a set of oils coming from multiple producers, and, therefore, showing quite heterogeneous chemical characteristics.

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Cultivar and geographical origin authentication of Italian extra virgin olive oil using front-face fluorescence spectroscopy and chemometrics

Cited by 41 publications

References 26 publications

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

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

Front Face Fluorescence Spectroscopy Combined with PLS‐DA Allows to Monitor Chemical Changes of Edible Vegetable Oils during Storage at 60 °C

Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil

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