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1.
J Agric Food Chem ; 55(4): 1071-6, 2007 Feb 21.
Article in English | MEDLINE | ID: mdl-17300145

ABSTRACT

An innovative covalent microsphere immunoassay, based on the usage of fluorescent beads coupled to a specific antibody, was developed for the quantification of the endotoxin Cry1Ab present in MON810 and Bt11 genetically modified (GM) maize lines. In particular, a specific protocol was developed to assess the presence of Cry1Ab in a very broad range of GM maize concentrations, from 0.1 to 100% [weight of genetically modified organism (GMO)/weight]. Test linearity was achieved in the range of values from 0.1 to 3%, whereas fluorescence signal increased following a nonlinear model, reaching a plateau at 25%. The limits of detection and quantification were equal to 0.018 and 0.054%, respectively. The present study describes the first application of quantitative high-throughput immunoassays in GMO analysis.


Subject(s)
Bacterial Proteins/analysis , Bacterial Toxins/analysis , Endotoxins/analysis , Fluorescent Dyes , Hemolysin Proteins/analysis , Immunoassay/methods , Microspheres , Plants, Genetically Modified/chemistry , Zea mays/chemistry , Bacillus thuringiensis Toxins , Bacterial Proteins/genetics , Bacterial Toxins/genetics , Endotoxins/genetics , Hemolysin Proteins/genetics , Plants, Genetically Modified/genetics , Seeds/chemistry , Zea mays/genetics
2.
J Biochem Biophys Methods ; 61(1-2): 95-106, 2004 Oct 29.
Article in English | MEDLINE | ID: mdl-15560925

ABSTRACT

The main outcome of this work is elaboration of classification models for edible oil samples representing the most widespread brands of Austrian pumpkin seed oil. A complete spectral characterisation of the pumpkin seed oil samples by UV-Vis, NIR and FTIR spectra was obtained together with their basic sensorial classification. Chemometrical processing of the measured data enabled the detection of the most important spectral features, which are crucial for categorising the oils into two or three classes according to their sensory quality evaluated by a panel of experts. The elaborated models thus make it possible to predict the category into which a hitherto unclassified oil sample belongs--considering classification into either two categories, containing oils with overall acceptable scores or oils that were not accepted, or three categories, involving oils fulfilling all quality criteria, oils with good scores and not accepted oils. This will perspectively facilitate the determination of chemical substances responsible for bad taste, odour and colour of the respective oil brands, as well as finding substances contributing to the excellent sensorial perception of some tested products.


Subject(s)
Algorithms , Cucurbita/chemistry , Food Analysis/methods , Plant Oils/analysis , Plant Oils/classification , Seeds/chemistry , Spectrum Analysis/methods , Combinatorial Chemistry Techniques/methods , Food Analysis/standards , Multivariate Analysis , Pattern Recognition, Automated/methods , Plant Oils/standards , Seeds/classification
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