Cultivar classification of Apulian olive oils: use of artificial neural networks for comparing NMR, NIR and merceological data
| Autor: | Rosa Ragone, David Naso, Francesco Paolo Schena, Enzo Perri, Giulio Binetti, Francesco Paolo Fanizzi, Laura Del Coco, Raffaele Valentini, Cinzia Montemurro, Samanta Zelasco |
|---|---|
| Přispěvatelé: | Binetti, Giulio, Coco, Laura Del, Ragone, Rosa, Zelasco, Samanta, Perri, Enzo, Montemurro, Cinzia, Valentini, Raffaele, Naso, David, Fanizzi, Francesco Paolo, Schena, Francesco Paolo |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Near infra red spectroscopy
Artificial neural network Magnetic Resonance Spectroscopy Near-infra red spectroscopy Artificial neural networks Cultivar classification Merceological analysis Nuclear magnetic resonance spectroscopy Olive oil Olive Oil Neural Networks (Computer) Analytical Chemistry Food Science 01 natural sciences 0404 agricultural biotechnology Cultivar Mathematics business.industry 010401 analytical chemistry Merceological analysi 04 agricultural and veterinary sciences General Medicine 040401 food science Nmr data 0104 chemical sciences Biotechnology Horticulture Neural Networks Computer business |
| Popis: | The development of an efficient and accurate method for extra-virgin olive oils cultivar and origin authentication is complicated by the broad range of variables (e.g., multiplicity of varieties, pedo-climatic aspects, production and storage conditions) influencing their properties. In this study, artificial neural networks (ANNs) were applied on several analytical datasets, namely standard merceological parameters, near-infra red data and 1H nuclear magnetic resonance (NMR) fingerprints, obtained on mono-cultivar olive oils of four representative Apulian varieties (Coratina, Ogliarola, Cima di Mola, Peranzana). We analyzed 888 samples produced at a laboratory-scale during two crop years from 444 plants, whose variety was genetically ascertained, and on 17 industrially produced samples. ANN models based on NMR data showed the highest capability to classify cultivars (in some cases, accuracy>99%), independently on the olive oil production process and year; hence, the NMR data resulted to be the most informative variables about the cultivars. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|