Traceability of olive oil based on volatiles pattern and multivariate analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22330%2F10%3A00024324" target="_blank" >RIV/60461373:22330/10:00024324 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Traceability of olive oil based on volatiles pattern and multivariate analysis

Popis výsledku v původním jazyce

An automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography?ion trap mass spectrometry (GC?ITMS), was developed and employed for fast characterisation of olive oil volatiles. In total, 914 sampleswere collected, over three production seasons, in north-western Italy?Liguria (n = 210) and other regions?in addition to the rest of Italy, Spain, France, Greece, Cyprus, and Turkey (n = 704) with the aim to distinguish, based on analytical (profiling)data, the olive oils labelled as ??Ligurian? (protected denomination of origin region, PDO) from all the others (??non-Ligurian?). For the chemometric analysis, linear discriminant analysis (LDA) and artificial neural networks with multilayer perceptrons(ANN-MLP) were tested. Employing LDA, somewhat lower recognition (81.4%) and prediction (61.7%) abilities were obtained. The classification model was significantly improved using ANN-MLP. Under these conditions, the recognition (90.1%) a

Název v anglickém jazyce

Traceability of olive oil based on volatiles pattern and multivariate analysis

Popis výsledku anglicky

An automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography?ion trap mass spectrometry (GC?ITMS), was developed and employed for fast characterisation of olive oil volatiles. In total, 914 sampleswere collected, over three production seasons, in north-western Italy?Liguria (n = 210) and other regions?in addition to the rest of Italy, Spain, France, Greece, Cyprus, and Turkey (n = 704) with the aim to distinguish, based on analytical (profiling)data, the olive oils labelled as ??Ligurian? (protected denomination of origin region, PDO) from all the others (??non-Ligurian?). For the chemometric analysis, linear discriminant analysis (LDA) and artificial neural networks with multilayer perceptrons(ANN-MLP) were tested. Employing LDA, somewhat lower recognition (81.4%) and prediction (61.7%) abilities were obtained. The classification model was significantly improved using ANN-MLP. Under these conditions, the recognition (90.1%) a

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

GM - Potravinářství

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)<br>R - Projekt Ramcoveho programu EK

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Food Chemistry

ISSN

0308-8146

e-ISSN

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Svazek periodika

121

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

BE - Belgické království

Počet stran výsledku

8

Strana od-do

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Kód UT WoS článku

000275360200042

EID výsledku v databázi Scopus

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