Influence of Cold Plasma on Sesame Paste and the Nano Sesame Paste Based on Co-occurrence Matrix

: 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.


Introduction:
Sesame is a plant with several features and properties for food, industry and medicine applications. The by-product that remains after oil extraction from sesame seeds, also known as sesame oil meal contains high amount of protein (35-50 %) and is used as poultry and livestock feed 1,2,3 . Nanotechnology is a general term related to all nano-scale technology and research. The term refers to the scientific concepts with new properties which can be discovered and perfected while working within this context. Nanotechnology operates at nanometer-scale dimensions (1-100 nm) and can be used for a wide variety of applications and development of various types of nano materials and nano devices. A statistical method of examining texture that considers the spatial relationship of pixels is the gray-level co-occurrence matrix (GLCM), also known as the gray-level spatial dependence matrix. The GLCM functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, creating a GLCM, and then extracting statistical measures from this matrix. Harlick in 1970 proposed four features that can be calculated from the matrix, energy, homogeneity, contras, and correlation 4 .
The textural features used to investigate the effect of cold plasma on bacteria grown on the sesame paste and nano sesame paste. The unsupervised K-mean classification method is used to classify paste sesame images. K-means is usually used to decide the natural pixel groupings present in an image. In practice, it's attractive because it's straightforward, and generally very quick. The input dataset is partitioned into clusters 5,6 .
In 2017 Mazhir et. al. used the textural features from the co-occurrence to study the effect of plasma on smear of leukemia blood cells, the gray level matrix is used to ensure the effect of cold plasma on the leukemia cells 7 . In 2017 Mazhir et. al. used the co-occurrence matrix to find the effect of the plasma on the Leukemia Blood Cells 8 . In 2019 Muryoush et. al. used the co-occurrence matrix to study the effect of cold plasma on bone infected by osteoporosis 9 . In 2019 Ali et. al. used the co-occurence matrix to find the effect of Nanocurcumin which has been enhanced by using microwave plasma on the mice infected with diabetes, the result depends on calculating the textural features contrast, correlation, homogenous and energy 10 . The aim of the search: The novelty of this search is studying the effect of cold plasma on sesame particle and nano sesame particle using image processing technique which based on co-occurrence matrix and its textural feature a combination between the nano technology, the cold plasma and the digital image processing.

Methodology:
The methodology based on analyze the effect of cold plasma on the nano particle and normal particle of the sesame paste through the cooccurrence matrix starting from leaving the sesame paste in its both form nano and normal particles two days in air, kinds of bacteria growing on it, then treating them with plasma and taking the microscope images. The threshoding technique is applied as first step in image segmentation, coloring the images using the Hot color preparing them for image classification, using k-mean clustering method to classify the infected region and normal region, and using the co-occurrence matrix to study the texture of the sesame paste Thresholding: Thresholding is one of the easiest and simplest ways of segmentation. It depends on grouping the intensity according to the threshold value, it can remove the object from the context. The resulting image for one threshold as calculated by 11 Where T is value for threshold. This can produce a binary image. The thresholding can be used to transform the image with a gray scale into a binary image. Gray Level Co-occurrence Matrix: The cooccurrence matrices are constructed by considering that every pixel have eight neighbors (horizontally, vertically and diagonally at 45 degrees). It is also assumed that the matrix of relative frequencies of gray levels co-occurrence can specify the texture-context information. Some of the texture features can be obtained from these matrices 12,13 .
 Contrast: It gives the local variations in the gray level co-occurrence matrix. It determines the intensity difference between a pixel and its neighborhood 12,13 Contrast = ∑ ( − ) 2  Energy: it is the sum of squared elements. Its range is from 0 to 1. It represents the number of occurrence for the gray level in the image, the high energy value the gray level is small otherwise it is high. The energy equation can be represented as follows 15 :

……… (3)
 Homogeneity: It gives the distribution value of the closeness of elements of the gray level cooccurrence matrix. It gives the value between the range of 0 and 1 16 .
 Correlation: It measures how a pixel is correlated to its neighborhood pixels. Its value lies between (-1 and +1). Its value is (-1) for perfectly negatively correlated image and (+1) for positively correlated image 16 .
, : mean value in the x and y direction. , : variance of x,y

Classification by K-means
The most common clustering based partitioning technique is the K-means algorithm. It is an unsupervised algorithm used in clustering. K-mean clusters are formed by identifying the data points closest to the clusters. The K-means steps are 17,18 : 1. Randomly select k number of points, and make them initial centroids 19 . 2. Select a data point from the array, compare it with each centroid, then assign it to the closest centroid cluster if the data point is found to be identical with the centroid 20 . 3. When assigning each data point to one of the clusters, determine the Centroid value for each cluster k-number. 4. Repeat phases 2 and 3, until no data point transfers to any other cluster from its previous cluster Color map "Hot" also known as "warm color" refers to the smooth color changes from black to white, through shades of yellow, orange and red. The adjacent color in this map is of equal distance; as an extension of the 16-step color scale, a 256 color scale is implemented 21,22 . Let 'Ri, Gi, Bi' and 'Ri+1, Gi+1, Bi+1' represent any two adjacent base colors and li and li+1 denote their corresponding gray leve l1 (li > l > l i+1 for 1 ≤ i ≤ 15), the associated with the color (R,G,B) which represented by the following equation 23 .

Microwave Plasma:
Microwave plasma is operated by electromagnetic wave at frequencies greater than 300 MHZ and wavelength at (mm to cm), generating non-equilibrium (non-thermal) plasma with continuous energy wave (watt to kilowatt) and operating pressure range (10 -5 torn up to atmospheric pressure) which used in many medical applications without damaging the surrounding tissue 24,25 . In the search, the microwave plasma is used as a treatment tool to treat the bacteria that was created on the past sesame after leaving it in the air for two days, the duration of time exposing was two mints.

Results and Discussion:
The present search deals with18 images (sesame paste, nano sesame paste, sesame paste treated with plasma and nano sesame paste treated with plasma).
Bacteria grows on sesame past which has been left in air for two days, a microscopic image with 100x power magnification. Figure 2 displays sesame paste images with their threshold before left them in the air for two days. The threshold is used to discard from the glass reflection of the slide that the sesame placed on it and some noise that the microscopic image creates. The texture features are shown in Table 1 and image 3 has low contrast and higher energy.   Figure 3. Image No.4 for sesame paste after two days with their classification region.           The sesame paste was converted into nano sesame using Vibra-Cell Ultrasonic Liquid Processors device 750 Watt and 20 kHz, its main object is to convert the sesame into nano particles. Figure 9 shows the microscope images for the nano sesame paste and their threshold. Table 8  Th<90 Th<95 Th<80 Figure 9. The microscope images for the nano sesame paste and their threshold class 4sesame with Bactria Figure 10. Image No.13 for sesame paste after two days with their classification region.  Figure 11. Image No.14 for sesame paste after two days with their classification region.  Figure 12. Image No.15 for sesame paste after two days with their classification region.  Figure 13. Image No.16 for sesame paste after treating with plasma with their classification region.      Tables 15,16 and 17 the value of the contrast for the sesame with bacteria after leaving it two days in air has highest value, because the contrast indicates that there is a high difference between the adjacent pixels, high random intensity this comes from the bacteria which grows on the sesame. The sesame paste treated with plasma after removing the (bacteria) has the lowest value of the contrast because plasma regularized the pixels and the intensity of adjacent pixel will approach nearly the same value. The correlation for sesame paste treated with plasma is higher than that for sesame paste after two days in the air, the lower value is that when the sesame treated with plasma and the bacteria is removed, the correlation has highest value if the neighboring pixels are highly correlated and the correlation approach its maximum value. The energy of the sesame paste treated with plasma, has the maximum value as the sesame without treating it with plasma. The energy represents the gray levels number in the image, the high energy value with the gray level is low, when the gray level is low the texture is more regular, the homogeneity indicated the regularity of the texture the maximum value is for the sesame paste treated with plasma (this result is based on average value of table 17  comparing it with average value of Tables 15 and  16. Tables 18,19 and 20 show the contrast value for the nano-sesame paste with bacteria after leaving it in air for two days is of the highest value, since the contrast indicates that there is a high difference between the adjacent pixels, high random intensity. This comes from the bacteria that that grows on the sesame. Plasma-treated, nano-sesame paste gives lowest contrast value, because plasma gives heat to the particles the pixels of the neighboring pixels' intensity approach each other. The correlation for plasma-treated nano sesame paste is higher than that for nano sesame paste with bacteria, the correlation has high value if the adjacent pixels are highly correlated. The energy of the plasma-treated nano sesame paste has the max value comparing with the sesame without treating with plasma, since the energy indicates the amount of gray levels in the image. The less gray level in the texture indicate that its pure from bacteria. The homogeneity which shows the regularity of the texture has maximum value for the plasma-treated nano sesame paste (this result conclude as comparing Tables 18, 19 and 20.

Conclosion:
The texture features which are calculated from co-occurrence matrix are energy, correlation, contrast and homogeneity, they describe the sesame through the textural features. The present search contains hybrid technique in using image processing (textural analysis) with the nano particles and plasma application, the texture feature used to study the effect of cold plasma on the sesame paste in both form nano particle and normal particle. The low contrast value indicated that the sesame paste texture is more regular. This is an indication for less or no bacteria presents in the texture, the energy, correlation and homogeneity provide us with information about the sesame texture, the result indicate the high value of these features is for the sesame paste treated with plasma comparing with the other cases used. This result is good and means that the plasma treated the bacteria. The time exposure to plasma is two minutes its suitable time to treated the sesame texture. The same result could be concluded with nano sesame. Comparing the two results, the sesame paste with plasma and nano sesame paste with plasma the result is best for the sesame paste with plasma than the nano sesame paste with plasma, because the plasma provides the paste with heat and makes the sesame nano particle congregate together.