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Keywords:Toxic substancesAcrylamideneurotoxinon analysis and classification of the discriminatory features of the image in spatial domain. The potatochips are automatically segmented from the image followed by statistical and texture features extractionitemsparts ofinConventional methods based on chemical analysis of toxic sub-stances like acrylamide in food items may be time consuming,expensive and involve specialized manpower. Other methods likespectroscopic methods are destructive methods where the foodmethod. Thisimage to measureencouraging.to identifyof the potato chips for acrylamide identification. Pedreschi et al.(2006) proposed a computer vision based method to measure thecolor and shape of heterogeneous food material. They also studied(Pedreschi et al., 2005) the kinetics of color changes during fryingof blanched and unblanched potato chips. In this work a relation-ship was developed between the color change in food image andformation of acrylamide in food sample. They used potato chipimages for experimental purpose and reported experimentalCorresponding author.E-mail addresses: (M.K. Dutta), (A. Singh), (S. Ghosal).Computers and Electronics in Agriculture 119 (2015) 4050Contents lists availableComputers and Electrofood items of fried potato and there is a serious need to counterthis serious problem.presence of acrylamide in fried potato chips. In this work a linearregression equation was established from the color informationformation in potatoes and cereals. Many other researches(Pedreschi et al., 2010; Rosen and Hellens, 2002) has been carriedout in this direction and the reports are quite alarming which indi-cates that there is presence of this carcinogenic toxic substances inand french fries using image processing basedmethod was based on the Color analysis of foodthe acrylamide content and reported results areet al. (2013) also studied the color of food image/10.1016/pag.2015.10.0070168-1699/C211 2015 Elsevier B.V. All rights reserved.Hethestarchy food like potato is fried in high temperature. Acrylamideis one such carcinogenic toxic substance that is formed whenpotato is fried in high temperatures. Mottram et al. (2002) hadshown that acrylamide is formed in starchy food like potato duringheating because of the Maillard reaction between amino acids.They reported that asparagines were the main cause of acrylamidenate convenient non-destructive method which can be used forquick identification of toxic substances from food items in batchesafter batches while producing commercially.With regards to image analysis for identification of toxic sub-stances, some related work is summarized here. Gokmen et al.(2007) estimated the concentration of acrylamide in potato chipsImage analysisFeature extraction from imagesClassificationSupport vector machine1. IntroductionFried/baked potato based foodpotato chips are very common in allfound that toxic substances are formedfrom the segmented image in spatial domain. These statistical features are then analyzed for identifica-tion of acrylamide content using support vector machine (SVM) classifier. The discriminatory variation inthe features of the image is strategically related to the presence of acrylamide using image processingtechniques. The experimental results have shown accuracy over than 94% and sensitivity of 96% indicat-ing that this method could be explored for viable commercial use.C211 2015 Elsevier B.V. All rights reserved.like French fries andthe world. It has beenthe food items whensample under test is destructed. Hence there is a need of fast andinexpensive methods which could be applied on food samples foraccurate results in real time. Image processing based methods suitswith these requirements and are being investigated quite exten-sively in recent time. Image processing can be considered an alter-Available online 24 October 2015sive and may need specialized manpower. The proposed work presents a computer vision based non-destructive method to identify the presence of acrylamide in potato chips. The proposed method is basedOriginal papersA computer vision based technique for identificationpotato chipsMalay Kishore Duttaa, Anushikha Singha, Sabari GhosalaDepartment of Electronics & Communication Engineering, Amity University, Noida, IndiabAmity Institute of Biotechnology, Amity University, Noida, Indiaarticle infoArticle history:Received 18 July 2015Received in revised form 12 October 2015Accepted 13 October 2015abstractAcrylamide is a well-knownpotato chips, cookies, biscuitsimportance. Conventional methodsjournal homepage: of acrylamide inbsubstance commonly found in fried and baked food items such as& French fries. Identification of such toxic chemicals in fried food is of greatof acrylamide identification in food items are time consuming, expen-at ScienceDirectnics in Agriculture/locate/compagencouraging.These encouraging results have created immense interest inimprove the accuracy and efficiency of acrylamide identification,statistical and texture features are extracted from segmented2.3. Image acquisition of potato chips for image processingImages of potato chips were captured using a self developedimage acquisition system. Samples were illuminated using fourfluorescent lamps (length of 2 feet) of white light and four CFL of25 watt. The four lamps and four CFL were arranged as a square35 cm above the sample and at an angle of 45C176 with the sampleplane to give a uniform light intensity over the food sample.Images were captured using a color digital camera located verti-cally from the sample at a distance approx. 25 cm. The digital cam-era of 8 mega pixel with auto focus is used for image acquisitionwhich provides images in JPEG format. The angle between thecamera lens axis and the lighting sources was around 45C176. Sampleilluminators and camera were inside a box whose internal wallswere painted white to avoid the light and reflection from the room.Images were captured with the mentioned camera at its maximumresolution (3104 C2 1746 pixels) and connected to the USB port of acomputer with Intel core i3 processor. Images were stored in theimage was preprocessed and area of region of interest was seg-Samples were prepared by the procedures as described byin Agriculture 119 (2015) 4050 41potato chip images and strategically compressed using principalcomponent analysis (PCA) method. To improve the performanceof classifier only the discriminatory features are selected after fea-ture normalization and feature reduction and only these featuresare subjected to classification. The proposed method achievesaccuracy over 94% and sensitivity of 96% when the SVM is appliedwith a Polynomial Kernel with order 3 and rbf kernel withgamma 3. This proposed image processing prototype is a nondestructive method and is suitable for real time applications foracrylamide identification in fried/baked potato chips.The rest of the paper is organized as follows. Section 2 of thepaper comprises Material and methods followed in this work.While, the Section 3 describes the measurement of acrylamideusing LCMS analysis. Section 4 includes the computer visionbased analysis of potato chips images. The next section describesthe feature analysis of potato chips images for acrylamide identifi-cation. Section 6 includes the proposed methodology of acrylamideidentification including ROI segmentation, feature extraction, fea-ture normalization followed by feature reduction and acrylamideidentification using classification. Section 7 includes experimentalresults obtained using proposed method. The next section high-lights the final remarks on experimental results. Finally Section 9provides conclusion to the paper.2. Materials and methods2.1. MaterialsPotatoes of variety of Kufri Anand and vegetable oil (canola orsunflower oil) were the raw materials used for sample preparation.Potato stored at 4 C176C and 90% of relative humidity were thoroughlywashed in water and gently peeled before cutting. Potato slices ofthickness 2.0 mm were cut using a Potato Chip Slicer Machine.2.2. Pre-treatments and sample preparationPotato slices were rinsed immediately after cutting for 1 min indistilled water to remove any excess starch adhering to the surfaceprior to frying. Potato slices were cooked corn, canola and/or sun-flower oil to make chipy crunch potato chips using an electricalthis area which needs to be explored further to find out suitablecost effective and efficient methods. This work proposes a non-destructive computer vision based to identify the presence of acry-lamide content in fried potato chips.The main contribution of this paper is an efficient and accuratenon destructive image processing based method for identificationof acrylamide from potato chips using support vector machine(SVM) classifier. For accurate discriminatory feature extraction,area of potato chips is segmented from the image and then strate-gic feature extraction is done from this segmented image. Toresults indicate that it was successful to identify acrylamide inpotato chips using this image processing method. Pedreschi et al.(2007) proposed computer vision based method for quality evalu-ation of potato chips for acrylamide identification. In this work dis-crete color categories obtained from all the possible combinationsof gray levels for the segmented region and results wereM.K. Dutta et al./Computers and Electronicsfryer at the different frying condition (temperature C0120 C176Cto180 C176C) (Pedreschi et al., 2006) and then slices were drained afterfrying.Gokmen et al. (2007). In brief, 1 g of finely grounded potato chipsobtained after various degree of frying under controlled conditionswere suspended in 5 mL of methanol and13C3labeled acrylamidemented out from the input image. Statistical and texture featureswere considered to explore the discrimination possibility betweennormal images and acrylamide contained images. Dimensionreduction technique was employed for feature reduction to reducethe time complexity and improve the performance. Supervisedclassifier was used to classify the normal potato chip image andacrylamide contained potato chip image using these imagefeatures.3. Measurement of acrylamide using LCMS analysis3.1. Sample preparationcomputer directly via USB port in JPEG format. Fig. 1 shows theimage acquisition setup used to capture image for imageprocessing.2.4. Image analysis for acrylamide identificationIn the proposed work, identification of acrylamide in potatochips was based of image analysis of potato chips. Input sampleFig. 1. Image acquisition setup.three channel of RGB image, hence the discriminatory features ofof image for analysis.(1000 ng/g) into a 10 mL of glass centrifuge tube. The mixture, afterhomogenization of 2 min was centrifuged at 5000 rpm for 10 min.The clear supernatant was treated with Carrez I and Carrez II solu-tions (25lL each) to precipitate the co-extractives and centrifuga-tion was performed at 5000 rpm for 5 min. Quantitatively, 1 mL ofthe supernatant was concentrated to ca.50lL followed by imme-diate reconstitution to a total volume of 1 mL with water. For SPEclean up waters HLB cartridge was preconditioned with 1 mL ofmethanol and 1 mL of water. Subsequently, 1 mL of the extractwas passed through the preconditioned cartridge using a syringe.First 500lL of the eluent was discarded and the forthcoming dropswere collected and passed through a syringe filter (0.45lm).Twenty lL of the final test solution was injected into LC columnof LCMS analysis.3.2. LCMS analysisLCMS analysis of acrylamide in food samples (Gokmen et al.,2007) was performed. Agilent 1100 HPLC system consisting of abinary pimp, an auto sampler, a temperature controlled columnoven coupled to a detector (Agilent 1100 MS) equipped with atmo-spheric pressure chemical ionization interface was used for theanalysis. The analytical separation was carried out on Waters C-18 column (250 C2 4.6 mm, 5 lm) using an isocratic mixture of0.01 mM acetic acid in 0.2% aqueous solution of formic acid at aflow rate of 0.6 mL/min at 25 C176C. The interface parameters were:drying gas (N2, 100 psig), flow rate 4 L/min, nebulizer pressure60 psig, drying gas temperature of 325 C176C, vaporizer temperatureof 425 C176C, capillary voltage of 4 kV, corona current of 4 lA, andfragmentor voltage of 55 eV. Ions monitored were m/z 72 and 55for acrylamide and m/z 75 and 58 for13C labeled acrylamide.All the samples whose images are experimented in the workhave been subjected to this LCMS method for labeling them asnormal or acrylamide content sample.4. Computer vision based analysis of potato chips imagesColor of potato chips is an important parameter to be controlledduring processing together with chipness, oil and acrylamide con-tent (Pedreschi et al., 2007; Rosen and Hellens, 2002; Scanlonet al., 1994). Fried potato color is the result of the Maillard reactionthat depends on the content of reducing sugars, amino acids orproteins at the surface, temperature and time of frying (Smith,1975; Mrquez and An, 1986). Among the different classes ofphysical properties of fried items, color is considered the mostimportant visual attribute in the perception of product quality.Hence the color of potato chips sample image can be a measureof acrylamide content in potato chips. Fig. 2(a) shows the colorcomponents present in the sample image of potato chips in whichacrylamide is not present. Similarly Fig. 2(b) shows the color com-ponent for the sample image in which acrylamide content is pre-sent. It can be seen in Fig. 2 that presence of acrylamidediscriminate the color components present in the image of a foodsample from a normal food sample image.The presence of toxic substance like acrylamide can be evalu-ated by analyzing the color of potato chips image (Pedreschiet al., 2006). In terms of image processing color image (RGB image)can be considered as the combination of three different channelsred, green & blue. Since these three primary color makes a colorRGB image hence these three channels of RGB image will have dis-criminatory variation in the pixels intensity values for normal sam-ple and acrylamide content sample images and the presentedresults in Gokmen et al. (2007) are encouraging.42 M.K. Dutta et al./Computers and ElectronicsSince color RGB image is 3-dimensional image, it may be diffi-cult and more complex to process the heavy RGB image directlyfor acrylamide identification. A 2-Dimensional (2D) image will pro-Statistical features like mean, standard deviation, and varianceare calculated from segmented image which is the region of inter-est (ROI) in spatial domain and it is observed that these featureshave discrimination between normal and acrylamide samples.Fig. 5 shows the plot of mean for normal & acrylamide contentsamples. It can be clearly seen in the figures that mean value showsdiscriminatory variation between normal sample image and acry-lamide sample image. Similarly Fig. 6 indicates discriminatory pat-tern variation for the value of variance. On the basis of theseobservations there is a motivation to explore various statisticaland textures features from the gray images for identification ofacrylamide in potato chip images.6. The proposed methodThe proposed work presents a non destructive compute visionbased method for acrylamide identification in fried/baked potatochips. Statistical and texture features from segmented gray imageof potato chips were used to find out discrimination between nor-mal potato chip image and acrylamide contained potato chipimage. Accuracy of acrylamide identification may be improved ifprominent distinct features are considered for analysis and classi-fication. Significant features from the image will be extracted ifonly the informative area of image is considered for featureacrylamide will be retained in the 2-dimensional gray scale image.Accordingly if the gray scale image contains the discriminatory fea-tures then processing this 2-D image for acrylamide identificationwill be computationally cheap increasing the efficiency of themethod.Fig. 4 shows the plot of number of pixels with gray levels pre-sents in the gray scale version of normal image & acrylamide con-tent image respectively. The gray level intensity distributionindicates that normal and acrylamide sample image have discrim-inatory behavior in the pattern distribution. Hence processing of2-dimensional gray image seems to a feasible approach foracrylamide identification using image processing.5. Feature analysis of potato chips image for identification ofacrylamideIt was observed from the histogram distributions that gray scaleimage of potato chips have discrimination in the pixels intensityvalues for normal and acrylamide sample images. Based on theseobservations gray scale image are used in this work to identifyacrylamide presence from the samples.Since number of pixels in the gray image is very high dependingon the size of image so processing of pixel values for acrylamideidentification may be a time taking approach. Hence, for efficientcomputation some statistical parameters may act as representativevide faster processing with less computational cost. This 3-dimensional (3D) RGB image can be converted into 2D gray scaleimage using the following image processing operation:Gray scale Image I R G B31where R, G & B represents red, green and blue channels of RGBimage respectively.Fig. 3 represents RGB image of potato chips, Red, Green & Bluechannels of RGB image and gray scale image of potato chips. Sincethis 2-dimensional gray scale image is the intensity average of allin Agriculture 119 (2015) 4050extraction. In this case the potato chip area needs to be segmentedfrom the background image so that only informative features areconsidered and redundant information from the background isM.K. Dutta et al./Computers and Electronicsremoved. The

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