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Adulteration Detection in Basmati Rice When Mixed with Low Premium Rice Brand

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Adulteration Detection in Basmati Rice When Mixed with Low Premium Rice Brand ADULTERATION DETECTION IN BASMATI RICE WHEN MIXED WITH LOW PREMIUM RICE BRAND 1PRIYANKARAN TANCK, 2BIPAN KAUSHAL 1,2Electronics and Electrical Communication, Department, PEC University of Technology, Chandigarh, India Abstract- Cost of basmati rice is two to three times the price of other rice varieties available in the market. There is widespread adulteration in order to increase the margin of profit. The methods currently employed to determine adulteration include manual inspection of rice. This proves to be an improper way and outcomes that results are inaccurate. This paper aims to develop a digital technique of inspection using computer vision to detect the extent of adulteration. Here, a more appropriate non destructive method employing image processing technique is proposed. Image processing technique is used to study rice grain varieties of different types. It provides an automated, cost-effective and non-destructive alternative way of inspection. Two mixed rice varieties were taken for finding adulteration. Aspect ratio and perimeter are the two parameters that are employed for the detection of adulteration. Key words- binary image, feature extraction, grayscale, natural aroma, parmal I. INTRODUCTION rice with low quality basmati rice or other cheaper varieties of rice. It could also be due to lack in proper Sanskrit meaning of Basmati is “fragrant". hygienic conditions of storing, processing, marketing 2-acetyl-1-pyrroline compound is chiefly responsible and transporting or carelessness on the part of the for a typical pandan like flavor in Basmati rice. This handlers. variety of rice is in cultivation in India for many years. It is considered as the crop that has origin in India. Parmal rice, which looks like basmati rice, is found to Trading community was instrumental in taking be mixed with the basmati rice grain by the traders. basmati to other parts of the world. It remains an Rice adulteration may also be carried out to make it important part of various cuisines in India, Middle available for common people. In order to maintain the East and south Asia. It is considered to be the authenticity of this product, adulteration detection benchmark in many parts of the world for all types of methods in basmati rice are desirable to differentiate rice. genuine basmati from other low premium rice. A variety of analytical and enzymic analyses along with India is among the top rice producing countries of the sensory techniques for the analysis of adulterated world. Rice is the pre-eminent crop and chief grains of basmati rice were applied for the determination of India. It is the most important staple food grain for deliberate or non-deliberate mixing of different rice people in India, particularly in the southern and varieties that are not very successful in some cases. eastern regions of the country. Large area in this country is under rice cultivation as it is the chief food II. PROBLEM IDENTIFICATION crop. It is grown on a majority of the rain fed areas where annual rainfall is aplenty. It is also grown in The adulteration of premium brands of rice with areas where irrigation facilities are available. cheaper brands is abundantly prevalent in the food industry. As a consequence to this general public is the There are many varieties of rice that are produced in prime sufferer. India i.e. basmati (Oryza Sativa L), parmal, pusa rice, kamini, jyothi, sona masuri etc. Basmati commands A. Major problem premium price in the market. It is because of the fact Adulteration in food has become a major burning that the basmati grain is long and slender in shape. On problem these days. The adulterants chosen for cooking it elongates by 1.5 to two times and possesses mixing are so similar to the naturally existing food a strong specific natural aroma. items that it becomes nearly impossible to detect these by employing traditional methods. Basmati rice is sold at a price which is two to three times the prices of some other varieties such as parmal B. Manual inspection method and selli. The significant difference in the price The visual inspection method is mostly used in the between them is the main reason that encourages the grain markets to ascertain the extent of adulteration by fraudulent traders to adulterate the premium basmati experienced technicians. Manually it is very difficult Proceedings of 4th SARC International Conference, 30th March-2014, Nagpur, India, ISBN: 978-93-82702-70-2 53 Adulteration Detection in Basmati Rice When Mixed with Low Premium Rice Brand to ascertain the extent of adulteration. It is prone to human sensory errors. C. High quality standards People are much aware as compared with earlier times about the necessity of high quality of food products to remain healthy. High degree of accuracy in food adulteration is required to satisfy customer demands these days. D. Non destructive accurate method Accurate, less time consuming and non destructive Fig 3 shows the block diagram employed for the method that could detect the presence of unwanted extraction of features of the adulterated sample of material is needed with higher levels of accuracy. basmati rice using MATLAB as a tool. III. METHODOLOGY Pictures of the rice samples are taken against a clear and neat background with high quality camera. The images are then stored in the computer. The analysis of these images is then carried out in accordance with the methodology depicted in the following steps. A. Block diagram The block diagram depicting the steps for capturing and saving the image for characteristics parameters analysis process is as given in Fig 1. IV. RESULTS AND DISCUSSION In Table I, various characteristics parameters observed for one of the basmati samples are given. Perimeter and aspect ratio for each of the grain present in the sample are obtained. Based upon these values, Fig1.Basic Block for Image Capturing histograms for the perimeter and aspect ratio are B. Perimeter and aspect ratio plotted. From the histograms, number of grains These images are then analyzed using perimeter and having values below or above certain value is found. aspect ratio as two characteristics parameters in In Table II, the number of basmati grains as well as MATLAB. In order to process the image of the number of foreign grains detected is shown. The adulterated basmati rice, the algorithm used is as: number in the brackets mentions the number of grains which are taken for a particular sample. The 1. Input the high quality image of basmati rice percentage in error for a given sample is given in the sample taken with the help of high quality last column. camera \ scanner as shown in Fig.2a. 2. Convert the colour image into greyscale as Table I shown in Fig.2b. Grai Perimete Major Minor axis Aspect n No. r axis ratio 3. Now subtract the background image from the 1. 68.87 25.052 16.2499 1.541 original image 01 4. Adjust the contrast of the image as per 2. 149.0 66.275 17.8353 3.714 requirement. 538 5 5. Convert the image into binary image as shown 3. 154.1 67.512 20.582 3.28 in Fig.2c. 665 1 6. Compute desired parameters using region 4. 145.3 65.362 19.0023 3.4397 props. 381 9 7. Create histogram of various parameters. 5. 142.9 60.611 21.37 2.8362 533 7 C. Conversion to binary image 6. 138.1 61.472 19.3831 3.171 838 9 In Fig 2 are shown the image when it is being 7. 80.11 31.666 16.5975 1.9079 converted to binary form. Proceedings of 4th SARC International Conference, 30th March-2014, Nagpur, India, ISBN: 978-93-82702-70-2 54 Adulteration Detection in Basmati Rice When Mixed with Low Premium Rice Brand 76 7 . 371 4 8. 69.02 25.509 16.7795 1.52 39 84.18 31.182 17.67 1.764 2 6 . 38 6 9. 147.6 66.248 17.733 3.735 40 149.3 65.746 21.5273 3.054 396 . 381 3 10 61.35 22.366 14.3344 1.56 . 53 8 As can be verified from table II, which has been 11 53.11 20.234 11.4778 1.762 constructed with the help of the Fig 4 and Fig 5, the . 27 1 results obtained have an accuracy of well over 90%. 12 149.4 66.710 16.506 4.041 This validates the efficacy of the proposed . 386 3 methodology used for adulteration detection. 13 145.7 65.525 17.0374 3.845 . 401 4 14 63.94 21.479 17.1383 1.2532 . 11 3 15 155.5 69.174 18.5998 3.719 . 391 1 16 83.59 33.329 15.3118 2.176 . 6 17 141.6 63.152 18.5419 3.405 . 812 18 74.91 29.454 15.2098 1.936 . 17 2 19 149.0 66.461 20.1276 3.302 . 122 4 20 71.01 25.832 17.7656 1.454 Fig 4 Perimeter histogram . 22 2 21 142.0 63.88 18.7161 3.413 . 244 22 155.1 63.402 20.1793 3.141 . 96 9 23 154.9 68.474 18.0528 3.793 . 533 9 24 146.7 64.568 17.9272 3.601 . 107 1 25 143.9 63.172 19.749 3.198 . 828 7 26 66.66 23.941 16.6771 1.435 . 9 8 Fig 5 Aspect ratio histogram 27 81.11 30.771 17.4838 1.7599 . 27 Table II 28 69.59 25.298 16.1962 1.562 . 8 7 Sampl Total no. No. of Amount of % 29 66.18 23.104 16.1445 1.431 e No.
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