Consumers' preferences for geographical origin labels: evidence from the Canadian olive oil market

2011 ◽  
Vol 38 (2) ◽  
pp. 193-212 ◽  
Author(s):  
L. Menapace ◽  
G. Colson ◽  
C. Grebitus ◽  
M. Facendola
Keyword(s):  
Olive Oil ◽  
Geographical Origin ◽  
Oil Market

scholarly journals Authentication of the Botanical and Geographical Origin and Detection of Adulteration of Olive Oil Using Gas Chromatography, Infrared and Raman Spectroscopy Techniques: A Review

Foods ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1565
Author(s):  
Eleni Kakouri ◽  
Panagiota-Kyriaki Revelou ◽  
Charalabos Kanakis ◽  
Dimitra Daferera ◽  
Christos S. Pappas ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Raman Spectroscopy ◽  
Olive Oil ◽  
Nutritional Value ◽  
Review Paper ◽  
Geographical Origin ◽  
Analytical Techniques ◽  
Volatile Fraction ◽  
Olive Oils ◽  
Infrared And Raman Spectroscopy

Olive oil is among the most popular supplements of the Mediterranean diet due to its high nutritional value. However, at the same time, because of economical purposes, it is also one of the products most subjected to adulteration. As a result, authenticity is an important issue of concern among authorities. Many analytical techniques, able to detect adulteration of olive oil, to identify its geographical and botanical origin and consequently guarantee its quality and authenticity, have been developed. This review paper discusses the use of infrared and Raman spectroscopy as candidate tools to examine the authenticity of olive oils. It also considers the volatile fraction as a marker to distinguish between different varieties and adulterated olive oils, using SPME combined with gas chromatography technique.

A Strategic Model for Intervention on the Portuguese Olive-Oil Market

1980 ◽  
Vol 31 (9) ◽  
pp. 813
Author(s):  
L. Valadares Tavares ◽  
J. Assis Lopes
Keyword(s):  
Olive Oil ◽  
Oil Market ◽  
Strategic Model

scholarly journals An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer

Foods ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 834 ◽  
Author(s):  
Simona Violino ◽  
Luciano Ortenzi ◽  
Francesca Antonucci ◽  
Federico Pallottino ◽  
Cinzia Benincasa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Olive Oil ◽  
Open Source ◽  
Geographical Origin ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Ann Model ◽  
First Choice ◽  
Phenolic Components ◽  
Artificial Neural Network Ann

Extra virgin olive oil (EVOO) represents a crucial ingredient of the Mediterranean diet. Being a first-choice product, consumers should be guaranteed its quality and geographical origin, justifying the high purchasing cost. For this reason, it is important to have new reliable tools able to classify products according to their geographical origin. The aim of this work was to demonstrate the efficiency of an open source visible and near infra-red (VIS-NIR) spectrophotometer, relying on a specific app, in assessing olive oil geographical origin. Thus, 67 Italian and 25 foreign EVOO samples were analyzed and their spectral data were processed through an artificial intelligence algorithm. The multivariate analysis of variance (MANOVA) results reported significant differences (p < 0.001) between the Italian and foreign EVOO VIS-NIR matrices. The artificial neural network (ANN) model with an external test showed a correct classification percentage equal to 94.6%. Both the MANOVA and ANN tested methods showed the most important spectral wavelengths ranges for origin determination to be 308–373 nm and 594–605 nm. These are related to the absorption of phenolic components, carotenoids, chlorophylls, and anthocyanins. The proposed tool allows the assessment of EVOO samples’ origin and thus could help to preserve the “Made in Italy” from fraud and sophistication related to its commerce.

scholarly journals Evaluation of the Capability of Horizontal ATR-FTMIR and UV-Visible Spectroscopy in the Discrimination of Virgin Olive Oils from the Moroccan Region of Beni Mellal-Khenifra

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Aimen El Orche ◽  
Mustapha Bouatia ◽  
Siham Yanisse ◽  
Houda Labjar ◽  
Mouad Mouhsin ◽  
...  
Keyword(s):  
Olive Oil ◽  
Spectral Data ◽  
Geographical Origin ◽  
Virgin Olive Oil ◽  
Correct Classification ◽  
Virgin Olive Oils ◽  
Olive Oils ◽  
Uv Visible ◽  
Beni Mellal ◽  
Analytical Approaches

One of the most important challenges in the authentication of olive oil is the determination of the geographical origin of virgin olive oil. In this work, we evaluated the capacity of two spectroscopic techniques, UV-Visible and ATR-FTMIR, coupled with chemometric tools to determine the geographical origin of olive oils. These analytical approaches have been applied to samples that have been collected during the period of olive oil production, in the Moroccan region of Beni Mellal-Khenifra. To develop a rapid analysis tool capable of authenticating the geographical origin of virgin olive oils from five geographical areas of the Moroccan region of Beni Mellal-Khenifra, UV-Visible and ATR-FTMIR spectral data were processed by chemometric algorithms. PCA was applied on the spectral data set to represent the data in a very small space, and then discrimination methods were applied on the principal components synthesized by the PCA. The application of the PCA-LDA method on the spectral data of UV-Visible and ATR-FTMIR shows a good ability to classify olive oils according to their geographical origin with a percentage of correct classification that represents 90.24% and 85.87%, respectively, and the processing of the spectral data of UV-Visible and ATR-FTMIR by PCA-SVM allows differentiating correctly between five olive oils with a correct classification rate of 100% and 97.56, respectively. This study demonstrated the feasibility of UV-Visible and ATR-FTMIR fingerprinting (routine technique) for the geographical classification of olive oils in the Moroccan region of Beni Mellal-Khenifra. Such developed methods can be proposed as alternative and complementary methods to authenticate the geographical origin of virgin olive oil.

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