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.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
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ABSTRACT: This study proposed a methodology to evaluate the potential of mid-infrared (MIR) spectroscopy as a process analytical technology (PAT) tool for in situ (in-line) monitoring of cell culture media constituents, paving the way for on-line bioprocess monitoring and control of mammalian cell cultures. The methodology included a limit of detection (LOD) analysis and external influence investigation in addition to the calibration model development. The LOD analysis in the initial step provided a detailed procedure by which to evaluate the monitoring potential of the instrument of choice, for the application in question. The external influence study highlighted the potential difficulties when applying this technique to a typical mammalian cell culture. A comparative investigation between a fixed conduit immersion probe and flexible fiber-optic immersion probe was also carried out. Limitations associated with the use of MIR spectroscopy in the cell culture environment were also examined. A preliminary investigation, on components typically found in mammalian cell cultures, involving spectral characterization and limit of detection analysis was completed. It was evident at this initial stage that glutamine, could not be accurately detected at levels typically found in a mammalian cell culture medium. Results for glucose and ammonia, however, proved promising. A seven-concentration-level experimental design was used, and partial least squares regression employed, to develop calibration models. Optimized model results echoed the results of the preliminary analysis with the percentage error of prediction for glucose as low as 6.03% with the fixed conduit probe and glutamine having a higher error of 63.06% for the same probe. Comparison of the model results obtained from both probes supported the fixed conduit as the more accurate of the two probes for this experimental setup. The effect of external influences on the MIR spectra and hence the concentrations predicted by the model were also examined. These were subjected to statistical analysis to determine the significance of the effect. This study demonstrates that MIR spectroscopy as a PAT tool has limited potential for mammalian cell culture monitoring due to low concentrations of analytes present and outlines a method to allow the system to be evaluated.Applied Spectroscopy 12/2011; 66(1):33-39. · 2.01 Impact Factor
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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). · 1.35 Impact Factor