Attempted confirmation of the provenance of Corsican PDO honey using FT-IR spectroscopy and multivariate data analysis.
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.
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ABSTRACT: A thorough characterisation of PDO food products generally involves the determination of many analytical features which therefore requires the deployment of many different analytical techniques. Such features should be considered together – because their inter-correlations play a crucial role in characterisation– to provide what is called an analytical fingerprint. For this reason, a multivariate pattern recognition approach is usually required in order to obtain successful models to verify PDO claims. The output of these models must be binary, qualitative answers, such as: “the product under examination is compatible with the PDO requirements” or “the product under examination is not compatible with the PDO requirements”. This issue is exactly the same as for quality control and it therefore follows that the same type of data analysis tools should be employed. Multivariate quality control began with the work of Harold Hotelling , who extended to multivariate problems the univariate Student’s t test for verifying the agreement of an experimental value with a specified one. As a matter of fact, the Hotelling’s T2 variable can be considered as a sum of n independent Student’s t variables. While the Student’s – univariate – confidence interval is a segment delimited by two points, the corresponding Hotelling’s acceptance space is enclosed within an ellipse, an ellipsoid or a hyperellipsoid, depending on the number of variables considered. Ellipse width is a function of the dispersion (variance) of the variables while the angle with respect to axes depends on their degree of correlation. Furthermore, the acceptance space is exclusively determined by the application of statistics to the in-specification samples. This means that no information from out-of-specification samples is used to determine the acceptance boundaries: in fact, it would be difficult and unnecessary to include such information. Difficult, because it would apply to the inclusion of all possible factors of variation that might be responsible for rendering samples non-conforming. Not necessary, because a good set of compliant samples – which is usually less difficult to find – is sufficient to produce a verification model. Arguably, the most appropriate family of chemometric methods for this type of problem goes by the name of class-modelling [2, 3] or one-class classifiers . These methods verify compliance of samples with a specification by defining a multivariate enclosed class space at a predetermined confidence level. Such a class space is determined on the basis of a representative set of authentic samples of the class under investigation. Another important group of pattern recognition tools is represented by the discriminant classification techniques  – also known as two-class or multiclass classifiers – which have been applied more frequently than class-modelling for both historical and convenience reasons. For historical reasons, because they were the first multivariate techniques to be introduced and applied for qualitative pattern recognition; for convenience reasons, because the relevant software is much more available commercially and because the results are in many cases – apparently – better. All discriminant methods look for a delimiter between two – or more – classes. This delimiter defines a decision rule, on the basis of which a test sample is always assigned to one of the classes studied.07/2013: pages 317-338; , ISBN: 978-0-444-59562-1
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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 1 H and 13 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.ISRN Analytical Chemistry. 01/2013; 2013:825318.
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ABSTRACT: Honey is a carbohydrate rich syrup and viscous fluid produced by honeybees (Apis mellifera) from the nectar of flowers that, by definition, does not include any other substances. Honey is produced primarily from floral nectars where fructose and glucose are the major components. Overall, the chemical composition of honey varies depending on plant source, season, production methods and storage conditions. Analytical methods applied to honey generally deal with different topics such as determination of botanical or geographical origin, quality control according to the current standards and detection of adulteration or residues. Traditional chemical composition analysis and physical properties assessment are routinely performed in commercial trading of honey using time consuming analytical methods that require considerable sample preparation and analytical skills. Spectroscopic techniques in the infrared (IR) wavelength region of the electromagnetic spectrum have been used in the food industry to monitor and evaluate the composition of foods, becoming one of the most attractive and used methods for analysis. This review discusses the use, with advantages and limitations, of IR spectroscopy technologies to evaluate and monitor the composition of honey.Applied Spectroscopy Reviews 10/2011; 46(7):523-538. · 2.92 Impact Factor