Attempted Confirmation of the Provenance of Corsican PDO Honey Using FT-IR Spectroscopy and Multivariate Data Analysis

Teagasc, Ashtown Food Research Centre, Ashtown, Dublin 15, Ireland.
Journal of Agricultural and Food Chemistry (Impact Factor: 3.11). 09/2010; 58(17):9401-6. DOI: 10.1021/jf101500n
Source: PubMed

ABSTRACT This study investigated the potential of Fourier-transform infrared (FT-IR) spectroscopy and chemometric techniques to produce a mathematical model that would confirm or refute the provenance of honeys claiming to be Corsican. Authentic honey samples from two harvest seasons (2004/2005 and 2005/2006) were collected from Ireland (n=2), Italy (n=30), Austria (n=40), Germany (n=36), mainland France (n=46), and Corsica (n=219). Prior to scanning, samples were diluted with distilled water to a standard solids content (70 degrees Brix). Spectra (2500-12500 nm) were recorded at room temperature using a FT-IR spectrometer equipped with a germanium attenuated total reflectance (ATR) accessory. Standard normal variate (SNV) and first- and second-derivative data pretreatments were applied to the recorded spectra, which were processed using factorial discriminant analysis (FDA) and partial least-squares (PLS) regression analysis. Overall correct classification figures of 82% (FDA) and 87% (PLS) were obtained for a separate validation set comprising samples from both harvests.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Characterization of the botanical origin and quality of honeys is of great importance and interest in agriculture. In this study, an electronic nose (e-nose) was applied for identifying the botanical origin of honey as well as determining their main quality components such as glucose, fructose, hydroxymethylfurfural (HMF), amylase activity (AA), and acidity. Principal component analysis (PCA) and discriminant factor analysis (DFA) were employed to generate scatter plots of honey samples from 14 botanical origins. Origin discrimination models with 100 % overall accuracy were established by least squares support vector machines (LS-SVM). LS-SVM outperformed the linear regression method of partial least squares regression (PLSR) for quality prediction, showing that the non-linear correlations between e-nose responses were important for the analysis of honey. Moreover, three sensor selection algorithms, namely, uninformation variable elimination (UVE), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) were applied for the first time to analyze e-nose fingerprints of honey. After the calculation of the above three algorithms and the comparison of their results, from a total of 18 sensors, the important ones were selected for glucose (three), fructose (five), HMF (three), AA (five), and acidity (four) prediction, respectively. The results of sensor selection show the advantages of reducing redundancy of e-nose data, optimizing the sensor array of an e-nose, and improving the performance of models in terms of robustness. The overall results show that the laborious, time-consuming, and destructive analytical methods like high-performance liquid chromatography (HPLC), acid-base titration, and spectrophotometry could be replaced by e-nose to provide a rapid and non-invasive determination of the botanical origin and quality of honey.
    Food and Bioprocess Technology 02/2014; 8(2):359-370. DOI:10.1007/s11947-014-1407-6 · 3.13 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The efficiency of ATR FT-IR spectrometry was compared with recommended methodologies for physicochemical parameters of eighteen samples of Melipona subnitida honey. Significant differences were found between the values obtained using those techniques for hydroxymethylfurfural, ash and electrical conductivity. The results for the other parameters did not differ significantly, suggesting that this rapid and nondestructive methodology may predict parameters usually used to assess honeys’ quality. The effects of different storage conditions (room temperature, fridge and freezer) on the quality parameters of the product stored during 12 months were studied. Darkening of the honey was observed, particularly in the fridge and freezer. However, the changes occurring in the honey kept on the fridge were not statistically different from those occurring in the product kept on the freezer, except for free acidity. The results obtained for the honey stored at room temperature, best way to preserve, differed significantly from those obtained for the honey kept under the other conditions.
    International Journal of Food Science & Technology 01/2014; 49(1). DOI:10.1111/ijfs.12297 · 1.35 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: NMR can be used in food analysis for origin discrimination and biomarker discovery using a metabolomic approach. Here, we present an example of this strategy to discriminate honey samples of different botanical origins. The NMR spectra of 353 chloroform extracts of selected honey samples were analyzed to detect possible markers of their floral origin. Six monofloral Italian honey types (acacia, linden, orange, eucalyptus, chestnut, and honeydew) were analyzed together with polyfloral samples. Specific markers were identified for each monofloral origin: two markers for acacia (chrysin and pinocembrin), one for chestnut (γ-LACT-3-PKA), two for orange (8-hydroxylinalool and caffeine), one for eucalyptus (dehydrovomifoliol), one for honeydew (a diacylglycerilether) and two for linden (4-(1-hydroxy-1-methylethyl)cyclohexa-1,3-diene-carboxylic acid and 4-(1-methylethenyl)cyclohexa-1,3-diene-carboxylic acid). An NMR-based metabolomic approach that used O2PLS-DA multivariate data analysis allowed us to discriminate the different types of honey. Two different classifiers were built based on different multivariate techniques. The high precision of the classification obtained suggests that this approach could be useful to develop generally applicable metabolomic tools to discriminate the origin of honey samples.
    Metabolomics 08/2011; 8(4):679-690. DOI:10.1007/s11306-011-0362-8 · 3.97 Impact Factor