Determination of aflatoxins and ochratoxin A in high-sugar-content traditional Turkish foods by affinity column cleanup and LC fluorescence detection.

The effectiveness of an affinity column cleanup procedure followed by LC with fluorescence detection was established for the determination of aflatoxins and ochratoxin A in high-sugar-content traditional Turkish foods. Traditional foods, such as baklava (finely layered pastry filled with nuts and steeped in syrup), halvah (containing sesame paste and pistachios), cevizli sucuk (a confection made of grape juice boiled and dried on strings of nuts), Turkish delight (containing hazelnuts, pistachios, or walnuts), and pismaniye (candy made of sugar, butter, and flour), were tested, and the performance of the method was established with spiked samples. To examine the robustness of the methodology, baklava was prepared from raw materials and spiked at the initial stage of dry ingredients and through subsequent stages of preparation of dough, after cooking, and after addition of syrup and nuts. For all products, the analytical method required grinding the composite foodstuff under liquid nitrogen to form a fine powder, which was then thoroughly mixed before subsampling. After vortex extraction into methanol-water (aflatoxins) and aqueous sodium bicarbonate (ochratoxin A), the sample was filtered, diluted with phosphate-buffered saline, and then passed through either an aflatoxin or ochratoxin A affinity column before HPLC analysis with fluorescence detection (using post-column bromination for the aflatoxins). In all the traditional Turkish products, the recovery of aflatoxin B1 ranged from 77 to 98%, and LODs were <0.1 microg/kg. For ochratoxin A, the recoveries were from 88 to 93% and LODs were similarly <0.1 microLg/kg. Despite the complex nature of these traditional Turkish foods, which frequently contain products from sugar caramelization, there was no evidence of any interfering co-extractives, and the method has proved to be robust enough to be used for food control purposes.

Determination of fumonisins B1 and B2 in corn by liquid chromatography/mass spectrometry with immunoaffinity column cleanup: single-laboratory method validation.

A single-laboratory method validation was conducted to establish the effectiveness of an immunoaffinity column cleanup procedure followed by liquid chromatography/mass spectrometry (LCIMS) for the determination of fumonisins B1 and B2 (FBI + FB2) in corn. The test portion is extracted with acetonitrile-methanol-water (25 + 25 + 50). The extract is filtered, diluted with phosphate-buffered saline solution, and applied to an immunoaffinity column. FB1 + FB2 are removed with methanol and directly determined by reversed-phase LC with MS detection using selected-ion monitoring of 2 characteristic ions in each case. Test portions of blank corn samples were spiked with a mixture of FB1 + FB2 to give total levels of 200 and 500 ng/g, respectively. Recoveries of both FB1 and FB2 from spiked samples averaged 90.4-101%. Based on results for spiked raw corn (triplicates at 2 levels), the relative standard deviation for repeatability ranged from 2.8 to 7.1%. The accuracy of the method was demonstrated by analysis of Food Analysis Performance Assessment Scheme (FAPAS) test material. The method was also applied to a small survey of processed corn products such as corn chips, cornflakes, and popcorn.

Profiling Turkish honeys to determine authenticity using physical and chemical characteristics.

Seventy authentic honey samples of 9 different floral types (rhododendron, chestnut, honeydew, Anzer (thymus spp.), eucalyptus, gossypium, citrus, sunflower, and multifloral) from 15 different geographical regions of Turkey were analyzed for their chemical composition and for indicators of botanical and geographical origin. The profiles of free amino acids, oligosaccharides, and volatile components together with water activity were determined to characterize chemical composition. The microscopic analysis of honey sediment (mellissopalynology) was carried out to identify and count the pollen to provide qualitative indicators to confirm botanical origin. Statistical analysis was undertaken using a bespoke toolbox for Matlab called Metabolab. Discriminant analysis was undertaken using partial least-squares (PLS) regression followed by linear discriminant analysis (LDA). Four data models were constructed and validated. Model 1 used 51 variables to predict the floral origin of the honey samples. This model was also used to identify the top 5 variable important of projection (VIP) scores, selecting those variables that most significantly affected the PLS-LDA calculation. These data related to the phthalic acid, 2-methylheptanoic acid, raffinose, maltose, and sucrose. Data from these compounds were remodeled using PLS-LDA. Model 2 used only the volatiles data, model 3 the sugars data, and model 4 the amino acids data. The combined data set allowed the floral origin of Turkish honey to be accurately predicted and thus provides a useful tool for authentication purposes. However, using variable selection techniques a smaller subset of analytes have been identified that have the capability of classifying Turkish honey according to floral type with a similar level of accuracy.