Botanical discrimination of Greek unifloral honeys with physico-chemical and chemometric analyses

被引:95
作者
Karabagias, Ioannis K. [1 ]
Badeka, Anastasia V. [1 ]
Kontakos, Stavros [2 ]
Karabournioti, Sofia [3 ]
Kontominas, Michael G. [1 ,4 ]
机构
[1] Univ Ioannina, Dept Chem, Food Chem Lab, GR-45110 Ioannina, Greece
[2] Democritus Univ Thrace, Dept Social Adm & Polit Sci, Komotini 69100, Greece
[3] Attiki Honey SA, Krioneri Attikis 14568, Greece
[4] Amer Univ Cairo, Dept Chem, New Cairo 11835, Egypt
关键词
Unifloral honeys; Classification; Chemical markers; Multi element analysis; Chemometrics; VOLATILE COMPOSITION; GEOGRAPHICAL ORIGIN; THYME HONEYS; CITRUS; DIFFERENTIATION; CLASSIFICATION; PARAMETERS; MARKERS; SAMPLES;
D O I
10.1016/j.foodchem.2014.05.033
中图分类号
O69 [应用化学];
学科分类号
081704 ;
摘要
The aim of the present study was to investigate the possibility of characterisation and classification of Greek unifloral honeys (pine, thyme, fir and orange blossom) according to botanical origin using volatile compounds, conventional physico-chemical parameters and chemometric analyses (MANOVA and Linear Discriminant Analysis). For this purpose, 119 honey samples were collected during the harvesting period 2011 from 14 different regions in Greece known to produce unifloral honey of good quality. Physicochemical analysis included the identification and semi quantification of fifty five volatile compounds performed by Headspace Solid Phase Microextraction coupled to gas chromatography! mass spectroscopy and the determination of conventional quality parameters such as pH, free, lactonic, total acidity, electrical conductivity, moisture, ash, lactonic/free acidity ratio and colour parameters L*, a*, b*. Results showed that using 40 diverse variables (30 volatile compounds of different classes and 10 physico-chemical parameters) the honey samples were satisfactorily classified according to botanical origin using volatile compounds (84.0% correct prediction), physicochemical parameters (97.5% correct prediction), and the combination of both (95.8% correct prediction) indicating that multi element analysis comprises a powerful tool for honey discrimination purposes. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:181 / 190
页数:10
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