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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22330%2F09%3A00022386" target="_blank" >RIV/60461373:22330/09:00022386 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60461373:22330/10:00024326

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

The goal of this study was to examine the possibility of verifying the geographical origin of honeys based on the profiles of volatile compounds. A head-space solid phase microextraction (SPME) combined with comprehensive two-dimensional gas chromatography?time-of-flight mass spectrometry (GCxGC--TOFMS) was used to analyze the volatiles in honeys with various geographical and floral origins. Once the analytical data were collected, supervised pattern recognition techniques were applied to construct classification/discrimination rules to predict the origin of samples on the basis of their profiles of volatile compounds. Specifically, linear discriminant analysis (LDA), soft independent modeling of class analogies (SIMCA), discriminant partial least squares (DPLS) and support vector machines (SVM) with the recently proposed Pearson VII universal kernel (PUK) were used in our study to discriminate between Corsican and non-Corsican honeys. Although DPLS and LDA provided models with high se

Název v anglickém jazyce

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

Popis výsledku anglicky

The goal of this study was to examine the possibility of verifying the geographical origin of honeys based on the profiles of volatile compounds. A head-space solid phase microextraction (SPME) combined with comprehensive two-dimensional gas chromatography?time-of-flight mass spectrometry (GCxGC--TOFMS) was used to analyze the volatiles in honeys with various geographical and floral origins. Once the analytical data were collected, supervised pattern recognition techniques were applied to construct classification/discrimination rules to predict the origin of samples on the basis of their profiles of volatile compounds. Specifically, linear discriminant analysis (LDA), soft independent modeling of class analogies (SIMCA), discriminant partial least squares (DPLS) and support vector machines (SVM) with the recently proposed Pearson VII universal kernel (PUK) were used in our study to discriminate between Corsican and non-Corsican honeys. Although DPLS and LDA provided models with high se

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)

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

118

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

BE - Belgické království

Počet stran výsledku

6

Strana od-do

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Kód UT WoS článku

000270492500027

EID výsledku v databázi Scopus

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