Classification of extra virgin olive oil and verification of adulteration using digital images and discriminant analysis

Milanez, Karla Danielle Tavares de Melo; Pontes, Márcio José Coelho

doi:10.1039/c5ay01765c

Cited by 22 publications

(12 citation statements)

References 35 publications

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“…Thus, different analytical techniques have been developed to detect olive oil adulterations (e.g., MALDI-TOF/MS technique; mid infrared, Raman, fluorescence or visible spectroscopy; DNA-targeted approaches; ion mobility spectrometry; nuclear magnetic resonance; dielectric technique; ultrasounds technique; gas chromatography; etc. ), namely to identify and/or quantify the addition of other vegetable oils like camellia, canola, corn, grapeseed, hazelnut, peanut, rapeseed, soya, sesame, soybean and sunflower oils (De Melo Milanez and Pontes, 2015;Sun et al, 2015;Alouache et al, 2016;Jabeur et al, 2016;Kalaitzis and El-Zein, 2016;Nigri and Oumeddour, 2016;Mu et al, 2016;Rashvand et al, 2016;Srigley et al, 2016;Farley et al, 2017;Georgouli et al, 2017;Jergović et al, 2017;Liu et al, 2017;Ok, 2017;Philippidis et al, 2017;Santos et al, 2017;Uncu et al, 2017) or the admixture of lower quality or refined olive oils (Nigri and Oumeddour, 2016;Jergović et al, 2017). Although EVOO have a long history of economic adulteration, its detection still is a challenging task due to the diverse composition of cultivars and the limitations of existing detection methods (Ou et al, 2015;Srigley et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A taste sensor device for unmasking admixing of rancid or winey-vinegary olive oil to extra virgin olive oil

Harzalli

Rodrigues

Veloso

et al. 2018

Computers and Electronics in Agriculture

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A B S T R A C TElectrochemical sensor devices have gathered great attention in food analysis namely for olive oil evaluation. The adulteration of extra-virgin olive oil with lower-grade olive oil is a common worldwide fraudulent practice, which detection is a challenging task. The potentiometric fingerprints recorded by lipid polymeric sensor membranes of an electronic tongue, together with linear discriminant analysis and simulated annealing metaheuristic algorithm, enabled the detection of extra-virgin olive oil adulterated with olive oil for which an intense sensory defect could be perceived, specifically rancid or winey-vinegary negative sensations. The homemade designed taste device allowed the identification of admixing of extra-virgin olive oil with more than 2.5% or 5% of rancid or winey-vinegary olive oil, respectively. Predictive mean sensitivities of 84 ± 4% or 92 ± 4% and specificities of 79 ± 6% or 93 ± 3% were obtained for rancid or winey-vinegary adulterations, respectively, regarding an internal-validation procedure based on a repeated K-fold cross-validation variant (4 folds × 10 repeats, ensuring that the dataset was forty times randomly split into 4 folds, leaving 25% of the data for validation purposes). This performance was satisfactory since, according to the legal physicochemical and sensory analysis, the intentionally adulterated olive oil with percentages of 2.5-10%, could still be commercialized as virgin olive oil. It could also be concluded that at a 5% significance level, the trained panelists could not distinguish extra-virgin olive oil samples from those adulterated with 2.5% of rancid olive oil or up to 5% of winey-vinegary olive oil. Thus, the electronic tongue proposed in this study can be foreseen as a practical and powerful tool to detect this kind of worldwide common fraudulent practice of high quality olive oil.

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Section: Introductionmentioning

confidence: 99%

A taste sensor device for unmasking admixing of rancid or winey-vinegary olive oil to extra virgin olive oil

Harzalli

Rodrigues

Veloso

et al. 2018

Computers and Electronics in Agriculture

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“…This generates products of acceptable sensory quality but also has economic consequences and is harmful to the final consumer, which is poorly equipped to identify and distinguish the presence of adulterants. 15,28,30,33,40,42 Moreover, depending on the type of adulterant used, these fraudulent practices might represent real risks to consumer health. 15,42 Milanez and Pontes 33 developed a methodology to detect adulteration in extra virgin olive oils (EVOO) with soybean oil in the range of 0.5% to 10% w/w.…”

Section: Identification Of Food Adulterationsmentioning

confidence: 99%

“…1 A holder to conditionate the calibration objects and samples. Glass, quartz or prismatic PTFE cuvettes 17,[29][30][31] and flow cells 32,33 have been used similarly to spectrophotometry involving specific colorimetric reactions, replacing exclusively the detector. Other common holders have included glass, plastic and lab-made polytetrafluorethylene (PTFE) Petri plates 25,27,28,[34][35][36][37][38][39][40][41][42][43][44][45][46] have been the most employed holder type to dispose the samples directly without pretreatment.…”

Section: Instrumentation For Cachasmentioning

confidence: 99%

“…3 An image capturing device to register the images of the chemical system under consideration. Simple commercial devices include digital cameras, 15,25,30,41,45,52,53,59,60 scanners, 21,28,34,35,39,43,46,[54][55][56][57]61 webcams, 26,29,32,33,[36][37][38]40,42,44,[47][48][49] and smartphones, 17,18,27,31,50,51,58 and the choice of one of them shall be according to its availability in each laboratory or specific need for a given application. However, it is important to note that the portability of systems through the use of smartphones has become a remarkable trend in the literature, mainly in applications involving univariate calibration for colorimetric analysis.…”

Section: Instrumentation For Cachasmentioning

confidence: 99%

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Chemometrics‐assisted color histogram‐based analytical systems

Diniz

2020

Journal of Chemometrics

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This review systematizes for the first time the here called "Chemometricsassisted color histogram-based analytical systems" under the acronym CACHAS. A comprehensive discussion of the fundamentals, general characteristics, and applications are presented in order to direct practical aspects for developing methods that use color histograms as input data for the construction of chemometric models. For this, analytical information is rapidly acquired using a simple image acquisition device (such as digital camera, webcam, scanner, or smartphone), without need for any image processing, other than extraction of the histograms, which are constructed by counting the frequency distribution of the different color tones (conveniently specified through a color space) of all pixels delimited by a region of interest in a digital image. As a consequence, CACHAS have been increasingly used for qualitative and quantitative purposes in food, agricultural, forensic, biomedical, and microbiological analysis, reinforcing the principles of green analytical chemistry.

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“…Frequently, therefore, more valuable vegetable oils are adulterated with lower cheap and low-quality oils. Thus, the development of rapid, reliable, and cost-effective analytical methods for detecting adulteration/authentication of edible oils is a focus of great interest (Dourtoglou et al 2013;Mu et al 2014;de Melo Milanez and Pontes 2015).…”

Section: Introductionmentioning

confidence: 99%

Electron Impact–Mass Spectrometry Fingerprinting and Chemometrics for Rapid Assessment of Authenticity of Edible Oils Based on Fatty Acid Profiling

et al. 2019

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A new methodology is described herein that provides an experimentally simple, rapid, and cost-effective mass fingerprinting method for the assessment of edible oil authentication based on their fatty acid methyl ester (FAMEs). This analytical approach is based on the application of electron impact (EI) ionization-mass spectrometry (MS) without chromatographic separation, followed by the treatment of the spectral data via chemometrics analysis, linear discriminant analysis (LDA), principal component analysis (PCA), soft independent modeling of class analogies (SIMCA), and hierarchical cluster analysis (HCA). This fingerprinting analysis was applied by using a gas chromatography-mass spectroscopy instrument, without chromatographic column usage and ion identification; therefore, each measurement lasted about 1 min. All multivariate analyses provided excellent discriminations between the edible oil clusters with low classification error. LDA models constructed with six predictors and a total of 100% of edible oil samples from different brands were correctly classified. Furthermore, no misclassification was reported for the discriminant analysis in supervised SIMCA models with an accuracy of 95%. Thus, the present results pointed to the successful application of such methodology to detect, for the first time, authentication of the edible oils. Keywords Electron impact ionization-mass spectroscopy. Fingerprinting. Chemometrics. Authenticity. Edible oil. Fatty acid methyl ester Abbreviations ATR Attenuated total reflectance BCSO Black cumin seed oil CSO Cottonseed oil EI Electron impact FA Fatty acid FAME Fatty acid methyl ester FL Fluorescence FTIR Fourier transform infrared HCA Hierarchical cluster analysis Electronic supplementary material The online version of this article (

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Classification of extra virgin olive oil and verification of adulteration using digital images and discriminant analysis

Abstract: This work proposes a new methodology based on digital images and supervised pattern recognition methods for the classification of extra virgin olive oil (EVOO) samples with respect to brand (A, B and C) and verification of adulteration with soybean oil.

Cited by 22 publications

References 35 publications

A taste sensor device for unmasking admixing of rancid or winey-vinegary olive oil to extra virgin olive oil

A taste sensor device for unmasking admixing of rancid or winey-vinegary olive oil to extra virgin olive oil

Chemometrics‐assisted color histogram‐based analytical systems

Electron Impact–Mass Spectrometry Fingerprinting and Chemometrics for Rapid Assessment of Authenticity of Edible Oils Based on Fatty Acid Profiling

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