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.
- SourceAvailable from: Paolo Oliveri[Show abstract] [Hide abstract]
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.Discriminant and Class-Modelling Chemometric Techniques for Food PDO Verification, Edited by Miguel de la Guardia and Ana Gonzálvez, 07/2013: chapter 13: pages 317-338; Elsevier., ISBN: 978-0-444-59562-1
- [Show abstract] [Hide abstract]
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.
- [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). · 1.24 Impact Factor