Browsing by Author "Gökmen, V."
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Item Open Access Computer vision based analysis of potato chips-A tool for rapid detection of acrylamide level(Wiley - VCH Verlag GmbH & Co. KGaA, 2006) Gökmen, V.; Senyuva, H. Z.; Dülek, B.; Çetin, E.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.Item Open Access Computer vision-based image analysis for the estimation of acrylamide concentrations of potato chips and french fries(Elsevier BV, 2007) Gökmen, V.; Şenyuva, H. Z.; Dülek, B.; Çetin, A. EnisIn this study, digital colour images of fried potato chips and french fries were analyzed to estimate acrylamide levels based on the correlation with analyses using liquid chromatography-mass spectrometry. In fried potato images, bright yellow (Region 1), yellowish brown (Region 2) and darker brown (Region 3) regions were clearly visible, having different kinds of image pixels with characteristic mean values of red, green and blue components. Pixels of the fried potato image were classified into three sets (Set 1, Set 2 and Set 3) by means of semi-automatic and automatic segmentation. There was a strong correlation between acrylamide concentration and NA2 value, which is defined as the number of pixels in Set 2 divided by the total number of pixels of the entire fried potato image. To verify the applicability of this approach, a linear regression equation was used to estimate the acrylamide concentrations of a number of commercial potato chips and home-made french fries. Mean differences between the measured and predicted acrylamide concentrations were found to be +4 ± 14% and 14 ± 24% for commercial potato chips and home-made french fries, respectively