Influence of Cold Plasma on Sesame Paste and the Nano Sesame Paste Based on Co-occurrence Matrix
Main Article Content
Abstract
The aim of the research is to investigate the effect of cold plasma on the bacteria grown on texture of sesame paste in its normal particle and nano particle size. Starting by using the image segmentation process depending on the threshold method, it is used to get rid of the reflection of the glass slides on which the sesame samples are placed. The classification process implemented to separate the sesame paste texture from normal and abnormal texture. The abnormal texture appears when the bacteria has been grown on the sesame paste after being left for two days in the air, unsupervised k-mean classification process used to classify the infected region, the normal region and the treated region. The bacteria treated with cold plasma, the time exposure is two minutes. The textural features related to gray level co-occurrence matrix are calculated for the normal, abnormal and the treated texture, it is obvious that the treated texture class has the best features compared with the other classes. The result shows the sesame paste treated with plasma has good result compared with nano sesame paste treated with plasma. This is because the plasma provides the sesame paste with heat and makes the sesame nano particle congregate together.
Received 4/9/2020
Accepted 20/4/2021
Published Online First 20/1/2022
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Langham D R, Smith G, Wiemers T, Riney J, Sesame J. Production Information; Sesame Coordinators, Southwest Sesame Grower’s Pamphlet; SESACO: Austin, TX, 2008.
Heuzé V, Tran G, Bastianelli D, Lebas F. Sesame (Sesamum indicum) seeds and oil meal. Feedipedia, a programme by INRA, CIRAD, AFZ and FAO. 2017.
Anilakumar K R, Pal A, Khanum F, Bawa A S. Nutritional, medicinal and industrial uses of sesame (Sesamum indicum L.) Seeds—An Overview. Agric. Cons. Sci. 2010; 75: 159–168.
Sharma K, Kalsh A, Saini K. GLCM and its Features, (IJARECE). 2015; 4(8):2180-2182.
Chunhui Y, Haitao Y. Research on K-Value Selection Method of K-Means Clustering Algorithm. M.J. 2019; 2(16):226-235. doi:10.3390/j2020016.
Suganya S, Meyyappan T, Santhosh Kumar S. Performance Analysis of K Means and K Mediods Algorithms in Air Pollution Prediction. IJRTE. 2020;8(5):3573-3577
Mazhir S N, Ali A H, Harb N H, Hadi F W. The Effect of Dielectric Barrier Discharge Plasma on Smear of Leukemia Blood Cells by Texture Anaysia Images. J. Appl. S. R. 2017;13(3);35-42
Mazhir S N, Hadi F W, Mazher A N . Alobaidy L H. Texture Analysis of smear of Leukemia Blood Cells after Exposing to Cold Plasma. Baghdad Sci. J. 2017; 14(2): 403-410.
Muryoush A Q, Ali A H, Al-Ahmed H I, Mazhir S N. Study Effect of cold Plasma on Rabbits bones infected with Osteoporosis using biological and digital image processing. REVISTA AUS. 2019; 26-2:1-10.
Ali A H, Mazhir S N, Majeed N F, Mazhir A N. Studying the Effect of Diabetic on Panaceas using textural analysis for Histopathology Images. REVISTA AUS. 2019;26(2):59-64.
Mazhir SN, Ali AH, Abdalameer NK, Hadi FW. Studying the effect of Cold Plasma on the Blood Using Digital Image Processing and Images Texture analysis. IEEE Xplore Digital Library. 2016; 904-914.
Muryoush AQ, Ali AH, Al-Ahmed H, Mazhir SN. Effect of cold plasma on histological compositions of the rabbit's fracture bone tissue. IJS. 2019; 60(9):1997-2002.
Ravindra R, Rathod RDG. Design of electricity tariff plans using gap statistic for K-means clustering based on consumer's monthly electricity consumption data. Int. J. Energ. Sect. Manag. 2017; 2:295–310.
Ali A H, Al-Ahmed H, Mazhir S N, Noori A S. Using Texture Analysis Image Processing Technique to Study the Effect of microwave Plasma on the Living Tissue. Baghdad Sci. J. 2018; 15(1):87-97.
Falih E, Mazhe N A. Using FREAK descriptor to classify plasma influence in Mice sperm. KIJOMS. 2020; 6(1):36-43. DOI: 10.33640/2405-609X.1352.
Li X, Yu L, Hang L, Tang X. The parallel implementation and application of an improved k-means algorithm. J. Uni. Electron. Sci. Technol. China. 2017; 46:61–68
Turkel E. Segmentation, Introduction The Thresholding. Lect. Notes. 2012;1-23.
Ain Q U, Arfan M, Jaffar M, Latif G. Classification and Segmentation of Brain Tumor Using Texture Analysis. RAAIKEDB. 2010;147-155.
Suraksha R, Singh M. A Survey Paper on Image Retrieval Based on Colour Feature. IJRITCC. 2015; 3(2):96-98.
Turtinen M. Learning and Recognizing Texture Characteristics Using Local Binary Patterns. Academic dissertation, University of Oulu, Finland. 2007; C 278.
Aggarwal N, Agrawal R K. First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images. J. Signal Inf. Process. 2012; 3(2): 146–153.
Celebi M E. Improving the performance of k-means for color quantization. Image Vis Comput. 2011; 29 (4):260–271.
Shi X Q, Sallstrom P, Welander U. A Color Coding Method for Radiographic Images. Image Vis Comput. 2002; 20: 761-767.
Yahya K A, Rasheed B F. Effects of Discharge Current and Target Thickness in Dc -Magnetron Sputtering on Grain Size of Copper Deposited Samples. Baghdad Sci. J. 2019;16(1) :84-87.
Lebedev Y A. Microwave discharges: Generation and diagnostics. J. Phys. Conf. Ser. 2010;25:161.