Browsing by Subject "Acrylamide"

Now showing 1 - 4 of 4

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.
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. Enis
In 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
Open Access
Image processing methods for food inspection
(2012) Yorulmaz, Onur
With the advances in computer technology, signal processing techniques are widely applied to many food safety applications. In this thesis, new methods are developed to solve two food safety problems using image processing techniques. First problem is the detection of fungal infection on popcorn kernel images. This is a damage called blue-eye caused by a fungus. A cepstrum based feature extraction method is applied to the kernel images for classification purposes. The results of this technique are compared with the results of a covariance based feature extraction method, and previous solutions to the problem. The tests are made on two different databases; reflectance and transmittance mode image databases, in which the method of the image acquisition differs. Support Vector Machine (SVM) is used for image feature classification. It is experimentally observed that an overall success rate of 96% is possible with the covariance matrix based feature extraction method over transmittance database and 94% is achieved for the reflectance database. The second food inspection problem is the detection of acrylamide on cookies that is generated by cooking at high temperatures. Acrylamide is a neurotoxin
Open Access
Signal and image processing algorithms for agricultural applications
(2006) Dülek, Berkan
Medical studies indicate that acrylamide causes cancer in animals and certain doses of acrylamide are toxic to the nervous system of both animals and humans. Acrylamide is produced in carbohydrate foods prepared at high temperatures such as fried potatoes. For this reason, it is crucial for human health to quantitatively measure the amount of acrylamide formed as a result of prolonged cooking at high temperatures. In this thesis, a correlation is demonstrated between measured acrylamide concentrations and NABY (Normalized Area of Brownish Yellow regions) values estimated from surface color properties of fried potato images using a modified form of the k-means algorithm. Same method is used to estimate acrylamide levels of roasted coffee beans. The proposed method seems to be a promising approach for the estimation of acrylamide levels and can find applications in industrial systems. The quality and price of hazelnuts are mainly determined by the ratio of shell weight to kernel weight. Due to a number of physiological and physical disorders, hazelnuts may grow without fully developed kernels. We previously proposed a prototype system which detects empty hazelnuts by dropping them onto a steel plate and processing the acoustic signal generated when kernels hit the plate. In that study, feature vectors describing time and frequency nature of the impact sound were extracted from the acoustic signal and classified using Support Vector Machines. In the second part of this thesis, a feature domain post-processing method based on vector median/mean filtering is shown to further increase these classification results.