Computer vision based analysis of potato chips - A tool for rapid detection of acrylamide level

In this study, analysis of digital color images of fried potato chips were combined with parallel LCMS based analysis of acrylamide in order to develop a rapid tool for the estimation of acrylamide during processing. Pixels of the fried potato image were classified into three sets based on their Euclidian distances to the representative mean values of typical bright yellow, yellowish brown, and dark brown regions using a semiautomatic segmentation algorithm. The featuring parameter extracted from the segmented image was NA2 value which was defined as the number of pixels in Set-2 divided by the total number of pixels of the entire fried potato image. Using training images of potato chips, it was shown that there was a strong linear correlation (r = 0.989) between acrylamide level and NA2 value. Images of a number of test samples were analyzed to predict their acrylamide level by means of this correlation data. The results confirmed that computer vision system described here provided explicit and meaningful description from the viewpoint of inspection and evaluation purpose for potato chips. Assuming a provisional threshold limit of 1000 ng/g for acrylamide, test samples could be successfully inspected with only one failure out of 60 potato chips. © 2006 Wiley-VCH Verlag GmbH & Co. KGaA

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. © 2009 American Chemical Society

Survey of sulfites in wine and various Turkish food and food products intended for export, 2007–2010

Surveys were carried out between 2007 and 2010 to determine the total levels of sulfites in 1245 samples of wines, dried apricots, dried vegetables, nuts, juices and purees, frozen foods and cereals containing dried fruit supplied by food inspectors and by food producers for testing or for export certification. Sulfite analysis of wine was carried out using the Ripper method with an LOQ of 5 mg l(-1) and for dried and other foods the Monier-Williams distillation procedure was employed with an LOQ of 10 mg kg(-1). In the survey all wines contained measurable sulfites, but with the exception of one sample of white wine they were otherwise below Turkish Food Codex limits of 160 mg kg(-1) for red wine, 210 mg kg(-1) to white wine and 235 mg kg(-1) for sparkling wine. None of the cereal products, frozen foods, juices or purees contained sulfites above 10 mg kg(-1). However, all dried apricot samples contained significant levels of sulfite with around 40% having levels exceeding the Turkish limit of 2000 mg kg(-1). Significant levels of sulfite were found in other samples of dried fruit with even a fruit and nut bar containing 1395 mg kg(-1) of sulfite, suggesting the dried fruit ingredients contained levels above regulatory limits