scholarly journals Discrimination and identification of geographical origin virgin olive oil by an e-nose based on MOS sensors and pattern recognition techniques

2011 ◽  
Vol 25 ◽  
pp. 1137-1140 ◽  
Author(s):  
Z. Haddi ◽  
A. Amari ◽  
A. Ould. Ali ◽  
N. El Bari ◽  
H. Barhoumi ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Olive Oil ◽  
Geographical Origin ◽  
Virgin Olive Oil ◽  
Pattern Recognition Techniques

Tracing the geographical origin of honeys based on volatile compounds profiles assessment using pattern recognition techniques

2010 ◽  
Vol 118 (1) ◽  
pp. 171-176 ◽  
Author(s):  
I. Stanimirova ◽  
B. Üstün ◽  
T. Cajka ◽  
K. Riddelova ◽  
J. Hajslova ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Volatile Compounds ◽  
Geographical Origin ◽  
Pattern Recognition Techniques

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

2015 ◽  
Vol 7 (20) ◽  
pp. 8839-8846 ◽  
Author(s):  
Karla Danielle Tavares de Melo Milanez ◽  
Márcio José Coelho Pontes
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Soybean Oil ◽  
Olive Oil ◽  
Digital Images ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Pattern Recognition Methods

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.

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.

scholarly journals 1H–NMR fingerprinting and supervised pattern recognition to evaluate the stability of virgin olive oil during storage

Food Control ◽  
2021 ◽  
Vol 123 ◽  
pp. 107831
Author(s):  
Rosa María Alonso-Salces ◽  
Blanca Gallo ◽  
María Isabel Collado ◽  
Andrea Sasía-Arriba ◽  
Gabriela Elena Viacava ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Olive Oil ◽  
1H Nmr ◽  
Virgin Olive Oil ◽  
The Stability

Characterization of olive oil classes using a Chemsensor and pattern recognition techniques

2002 ◽  
Vol 79 (11) ◽  
pp. 1103-1108 ◽  
Author(s):  
F. Peña ◽  
S. Cárdenas ◽  
M. Gallego ◽  
M. Valcárcel
Keyword(s):  
Pattern Recognition ◽  
Olive Oil ◽  
Pattern Recognition Techniques

Authentication of the geographical origin of extra-virgin olive oil of the Arbequina cultivar by chromatographic fingerprinting and chemometrics

Talanta ◽  
2019 ◽  
Vol 203 ◽  
pp. 194-202 ◽  
Author(s):  
Dainis N. Vera ◽  
Ana M. Jiménez-Carvelo ◽  
Luis Cuadros-Rodríguez ◽  
Itziar Ruisánchez ◽  
M. Pilar Callao
Keyword(s):  
Olive Oil ◽  
Geographical Origin ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Arbequina Cultivar ◽  
Chromatographic Fingerprinting

Influence of Olive Maturity Stage and Geographical Origin on Some Minor Components in Virgin Olive Oil of the Chemlali Variety

2008 ◽  
Vol 56 (3) ◽  
pp. 982-988 ◽  
Author(s):  
Aida Lazzez ◽  
Enzo Perri ◽  
Maria Anna Caravita ◽  
Mohcen Khlif ◽  
Moncef Cossentini
Keyword(s):  
Olive Oil ◽  
Geographical Origin ◽  
Virgin Olive Oil ◽  
Maturity Stage ◽  
Minor Components
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