Classification of extra virgin olive oil and verification of adulteration using digital images and discriminant analysis
- 8 September 2015
- journal article
- research article
- Published by Royal Society of Chemistry (RSC) in Analytical Methods
- Vol. 7 (20) , 8839-8846
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.Keywords
Funding Information
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (162930/2013-5, 304530/2012-3, 475087/2012-7)
This publication has 34 references indexed in Scilit:
- Thermal oxidation process accelerates degradation of the olive oil mixed with sunflower oil and enables its discrimination using synchronous fluorescence spectroscopy and chemometric analysisSpectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2015
- Discrimination and characterisation of extra virgin olive oils from three cultivars in different maturation stages using Fourier transform infrared spectroscopy in tandem with chemometricsFood Chemistry, 2015
- Evaluation of the overall quality of olive oil using fluorescence spectroscopyFood Chemistry, 2015
- Detection of sunflower oil in extra virgin olive oil by fast differential scanning calorimetryThermochimica Acta, 2015
- A likelihood ratio model for the determination of the geographical origin of olive oilAnalytica Chimica Acta, 2015
- Detection of virgin olive oil adulteration using a voltammetric e-tongueComputers and Electronics in Agriculture, 2014
- Self-organizing maps based on chaotic parameters to detect adulterations of extra virgin olive oil with inferior edible oilsJournal of Food Engineering, 2013
- Authentication of extra virgin olive oils by Fourier-transform infrared spectroscopyFood Chemistry, 2010
- High-power gradient diffusion NMR spectroscopy for the rapid assessment of extra-virgin olive oil adulterationFood Chemistry, 2010
- Colour and image texture analysis in classification of commercial potato chipsFood Research International, 2007