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Title: An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer
Authors: Violino, Simona
Ortenzi, Luciano 
Antonucci, Francesca
Pallottino, Federico
Benincasa, Cinzia
Figorilli, Simone
Costa, Corrado
Journal: FOODS 
Issue Date: 2020
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.
ISSN: 2304-8158
DOI: 10.3390/foods9060834
Rights: Attribution 4.0 International
Appears in Collections:A1. Articolo in rivista

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