Attempted Confirmation of the Provenance of Corsican PDO Honey Using FT-IR Spectroscopy and Multivariate Data Analysis
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.
Available from: Leticia M. Estevinho
- "The crystal surface was cleaned between the samples with Triton X-100 solution (1% w/w) and rinsed with distilled water. The spectral baseline recorded by the spectrometer was examined visually to ensure that the crystal did not have residues from previous samples, as recommended by Hennessy et al. (2010). For the development of the chemometric PLS regression model, the IR spectra of honey samples with known composition were used to calculate a calibration function (mathematical model), which can be used for the analysis of future unknown samples, after evaluation of its capacity of prediction by internal and external validation. "
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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 · 1.38 Impact Factor
Available from: Dirk W Lachenmeier
- "Various novel, fast, and accurate chromatographic methods such as high-performance liquid chromatography (HPLC) [4–7], gas chromatography (GC) [8–10], liquid chromatography with electrochemical detector , and matrix- assisted-laser-desorption/ionization-time-of-flight-mass-spectrometry (MALDI TOF MS)   have been used to obtain the chemical composition and detect possible adulteration of honey. Vibration spectroscopic methods such as FT-Raman  , NIR   , and FT-IR    could be additionally used as a screening technique for checking the honey authenticity and for quantifying its major compounds. Apart from these analytical methods, the application of multivariate data analysis and, in particular, principal "
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ABSTRACT: 400 MHz nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis techniques were used in the context of food surveillance to measure 328 honey samples with
C NMR. Using principal component analysis (PCA), clusters of honeys from the same botanical origin were observed. The chemical shifts of the principal monosaccharides (glucose and fructose) were found to be mostly responsible for this differentiation. Furthermore, soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) could be used to automatically classify spectra according to their botanical origin with 95–100% accuracy. Direct quantification of 13 compounds (carbohydrates, aldehydes, aliphatic and aromatic acids) was additionally possible using external calibration curves and applying TSP as internal standard. Hence, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of botanical origin and quantification of selected constituents of honeys.
01/2013; 2013(4):825318. DOI:10.1155/2013/825318
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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 · 3.86 Impact Factor
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