Baroni María Verónica, Nores María Laura, Díaz María del Pilar, Chiabrando Gustavo Alberto, Fassano Juan Pablo, Costa Cristina, Wunderlin Daniel Alberto
Universidad Nacional de Córdoba-CONICET, Facultad de Ciencias Químicas, Dto. Bioquímica Clínica-CIBICI, Medina Allende y Haya de la Torre, Ciudad Universitaria, 5000 Córdoba, Argentina.
J Agric Food Chem. 2006 Sep 20;54(19):7235-41. doi: 10.1021/jf061080e.
We report the evaluation of the floral origin of honey by analysis of its volatile organic compounds (VOCs) profile, joined with the use of combined pattern recognition techniques. Honey samples, from five floral origins, were analyzed by headspace solid-phase microextraction-gas chromatography-mass spectrometry, selecting 35 VOCs out of the entire profiles, which were analyzed by hierarchical cluster analysis (HCA), stepwise discriminant analysis (SDA), and K-nearest-neighbor (KNN). Both HCA and SDA were used as exploratory tools to select a group of VOCs representing similitude and differences among studied origins. Thus, six out of 35 VOCs were selected, verifying their discriminating power by KNN, which afforded 93% correct classification. Therefore, we drastically reduced the amount of compounds under consideration but kept a good differentiation between floral origins. Selected compounds were identified as octanal, benzeneacetaldehyde, 1-octanol, 2-methoxyphenol, nonanal, and 2-H-1-benzopyran-2-one. The analysis of VOC profiles, coupled to HCA, SDA, and KNN, provides a feasible alternative to evaluate the botanical source of honey.
我们报告了通过分析蜂蜜的挥发性有机化合物(VOCs)谱,并结合使用组合模式识别技术来评估蜂蜜的花源。对来自五个花源的蜂蜜样品进行顶空固相微萃取-气相色谱-质谱分析,从整个谱图中选择35种VOCs,通过层次聚类分析(HCA)、逐步判别分析(SDA)和K近邻(KNN)进行分析。HCA和SDA均用作探索性工具,以选择一组代表所研究花源之间异同的VOCs。因此,从35种VOCs中选择了6种,通过KNN验证它们的判别能力,其正确分类率为93%。因此,我们大幅减少了所考虑的化合物数量,但仍能很好地区分花源。所选化合物被鉴定为辛醛、苯乙醛、1-辛醇、2-甲氧基苯酚、壬醛和2-H-1-苯并吡喃-2-酮。对VOC谱的分析,结合HCA、SDA和KNN,为评估蜂蜜的植物来源提供了一种可行的替代方法。