Identification of components of Brazilian honey by 1H NMR and classification of its botanical origin by chemometric methods

被引:117
作者
Boffo, Elisangela F. [1 ]
Tavares, Leila A. [2 ]
Tobias, Antonio C. T. [3 ]
Ferreira, Marcia M. C. [4 ]
Ferreira, Antonio G. [1 ]
机构
[1] Univ Fed Sao Carlos, Dept Quim, BR-13565905 Sao Carlos, SP, Brazil
[2] Univ Fed Espirito Santo, Dept Ciencias Matemat & Nat, BR-29932540 Sao Mateus, ES, Brazil
[3] Ctr Reg Univ Espirito Santo Pinhal UNIPINHAL, Curso Agron & Med Vet, BR-13990000 Espirito Santo Do Pinhal, SP, Brazil
[4] Univ Estadual Campinas, Inst Quim, BR-13084971 Campinas, SP, Brazil
基金
巴西圣保罗研究基金会;
关键词
Honey; H-1 nuclear magnetic resonance; K-nearest neighbor; Soft independent modeling of class analogies; Partial least squares discriminant analysis; NUCLEAR-MAGNETIC-RESONANCE; QUALITY; SPECTROSCOPY; ADULTERATION; EXTRACTION; VINEGARS; SPECTRA;
D O I
10.1016/j.lwt.2012.04.024
中图分类号
TS2 [食品工业];
学科分类号
100403 [营养与食品卫生学];
摘要
The potential of NMR spectroscopy to differentiate honeys concerning to the nectar employed in its production was evaluated. The application of chemometric methods to H-1 NMR spectra has allowed to discriminate the honeys produced in the state of Sao Paulo, being identified the signals of responsible substances for the discrimination. Application of PCA and HCA methods to H-1 NMR data have resulted in the natural clustering of the samples. Wildflower honeys were characterized by higher concentration of phenylalanine and tyrosine. Citrus honeys showed higher amounts of sucrose than other compounds, while eucalyptus honeys had higher amount of lactic acid than the others. Assa-peixe honeys showed spectra similar to eucalyptus and citrus. Sugar-cane honeys showed some signals similar to eucalyptus and citrus honeys, but also showed the tyrosine and phenylalanine signals. Adulterated honeys showed 5-hydroxymethylfurfural, citric acid and ethanol signals. KNN, SIMCA and PLS-DA methods were used to build predictive models for honey classification. In the commercial honeys prediction KNN, SIMCA and PLS-DA models correctly classified 66.7; 22.2 and 72.2% of the samples, respectively. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:55 / 63
页数:9
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