International Journal of Research In Vocational Studies (IJRVOCAS)
Vol. 2 No. 4 (2023): IJRVOCAS – Special Issues – INCOSTIG – PP. 48~55 Print ISSN 2777-0168| Online ISSN 2777-0141| DOI prefix: 10.53893 https://journal.gpp.or.id/index.php/ijrvocas/index
48
Indentification of Beef in Beef and Chicken Experiments using Conducting Polymer Sensor Series
and Kohonen Algorithm Method
Benrad Edwin Simanjuntak1, Marhaposan Situmorang2, Syahrul Humaidi3, Marzuki Sinambela4
1Student of Doctoral Program in Physics Department, Faculty of Mathematics and Natural Sciences, University of North Sumatra, Indonesia
1Department of Electro Engineering, Politeknik Negeri Medan, Indonesia
2,3,4 Lecturer of Physics Department, Faculty of Mathematics and Natural Sciences, University of North Sumatra, Indonesia
ABSTRACT
Chicken, and beef each have a distinctive aroma. Identification of Chicken and beef based on the aroma of the meat using an electronic nose. This electronic nose uses a series of sensors consisting of 6 (six) pieces and uses a Conducting Polymer. This polymer has a high resistance so it is widely used as an insulator.
However, this resistance has a certain limit where the polymer surface will turn into carbon and conduct electric current if exposed to excessive electric charge.
This research was conducted by taking samples of chicken and beef as test samples where these meats were placed in a closed container at room temperature. Data is taken alternately every day to find out the odor of each meat where on the first day data is taken from the odor of chicken, and on the second day data is taken from the odor of beef. This condition is done to ensure the freshness of each meat. This study uses a Neural Network (NN) as pattern recognition and ATMega16 microcontroller as data acquisition. Neural Network is trained using Kohonen. The sensor used is a Conducting Polymer sensor because of the nature of the Conducting Polymer where the output is a voltage generated due to changes in the polymer resistance resistance.
A two-layer neural network consisting of six input nodes and three output neurons is trained using the Kohonen algorithm with the training process completed in 31 iterations. The test was carried out 30 times for each exposure to steam from the odor of chicken and beef which was carried out alternately.
The percentage of success of the system is 100%.
Keywords:
Conducting Polymer Sensor Kohonen Algorithm Odor
Artificial Neural Network
Corresponding Author:
Benrad Edwin Simanjuntak, Department of Electro Engineering, Politeknik Negeri Medan,
Almamater Road No 1, Padang Bulan, Medan, North Sumatera, Indonesia.
Email: [email protected]
1. INTRODUCTION
Consumption of meat is needed by society. The meat needed is of course fresh and quality meat. Meat is defined as all animal tissues and all products resulting from the processing of these tissues are suitable for consumption and do not cause health problems for those who eat them, Soeparno (1994). Meat is one type of meat whose role is very important in meeting the nutritional needs of the community, because it contains protein and other substances such as fat, minerals, vitamins which are important for smooth metabolic
Polymers are materials that have high resistance. If the polymer is exposed to steam from the odor of meat, then the vapor molecules can diffuse to the polymer surface, causing a change in the polymer surface size.
To obtain a pattern of changes in voltage from exposure to several vapors from odors, eight sensors are used which are formed into a series of sensors. The polymer materials used are silicon DC-200, PEG-20M, 0V-101, 0V-17, DEGA, PEG-200, PEG-1540, and PEG-6000. To get a pattern of changes in voltage which is a result of changes in resistance, the sensor is connected in series with a variable resistor to form a voltage divider circuit.
The stress change pattern of the normalized meat odor was used to train the Kohonen network. After the training process is complete, it is hoped that the resulting weight can make the Kohonen network classify the voltage pattern according to the type of steam exposed to the sensor series so that the type of steam exposed to the sensor series can be identified.
In a previous research journal (Bagus & Widiartha, 2021) a classification system for meat types and their level of freshness was built, where this system was built to distinguish types of meat such as beef, chicken or lamb and can determine the freshness level with a digital image processing approach. The feature extraction method. The methods used include the Statistical method for warm feature extraction, Gray-Level Co- occurrence Matrix (GLCM) for the texture feature extraction process, and the Hu's Invariant Moment method for moment features, where the extraction results will be classified using Linear Discriminant Analysis (LDA).
In this research, the first stage is that the image that has been cropped and resized manually outside the system is entered for training. Next is the preprocessing stage, at this stage the image will be converted from RGB to grayscale and HSI color space. HSI images are used for color feature extraction while grayscale images are used for texture and moment invariant feature extraction. As for the feature extraction stage, statistical methods are used for color feature extraction, GLCM methods are used for texture features and HU methods are used for invariant moment features. These features are then trained using LDA. The results of the training are data projections that are stored and used in the classification process.
In a previous research journal (Prabowo, A., Erwanto, D., & Rahayu, P. N., 2021) it was done to classify the types of fresh, inapan and rotten beef using 120 beef samples taken directly by the researchers.
Before classifying the type of beef, the texture of the beef image was extracted using the GLCM method so as to produce texture parameters in the form of contrast, correlation, homogeneity and energy. The texture parameters were classified using the KNN method. The results of this study indicate that the extraction of beef image texture using the GLCM method can produce various values for the 4 GLCM texture parameters. The results of the classification of beef freshness using the KNN method to determine from 3 types of meat quality, namely fresh, inapan and rotten beef, obtained an evaluation of classification performance using the Confusion Matrix table with an Accuracy value of 0.82, Precision of 0.83, Recall of 0.82 and F-Measure of 0.82. So that the texture parameters of beef images using the GLCM method can be classified properly using the KNN method.
2. RESEARCH METHOD
In general, the system consists of a series of sensors, a voltage divider circuit, an ATMega16 Microcontroller in which there is an ADC and serial communication, as well as an artificial neural network that runs on a computer/laptop. The block diagram of the system is shown in Figure 1 and the overall research tool is shown in Figure 2.
Figure 1. Overall System Block Diagram.
Figure 2. Photo of Research Tools
2.1. Sensor Room Cleaning Process (Chamber)
The process of cleaning the sensor room (chamber) is carried out in the following way: Before the steam from the meat odor is entered into the sensor room, the sensor room is fed with Nitrogen (N2) which is passed into the sensor room to clean the remaining steam from odors that previously entered the sensor room.
N2 is given before entering the sensor room with the aim of reducing humidity. This is done because some sensor elements are sensitive to humidity. The sensor chamber is cleaned with N2 after being given steam from the odor until the sensor series voltage becomes the same or close to the reference voltage value when there is no steam in the sensor chamber.
The schematic of the air flow during the sensor room cleaning process is in Figure 3
Figure 3. Schematic of Air Flow during Sensor Room Cleaning Process
cleaned with N2 according to Figure 3.
The data retrieval process was carried out as follows: The experiment consisted of chicken samples, beef samples and lamb samples. These 3 meat samples were confirmed 1 day after being cut. It was first done by giving N2 which was flowed into the sensor room to neutralize polymer sensors and gas particles that were still attached, then giving chicken meat odor steam and waiting for a response from the sensor until the condition was stable, after a while it was neutralized again with N2 until the response back to its original state.
Then proceed with giving steam from the smell of chicken, beef and lamb.
3. RESULTS AND ANALYSIS
3.1. Testing on Chicken Meat
During the process of exposure to a series of sensors with steam from the odor of chicken, responses were obtained as shown in Figures 4 (a) and 4 (b). From the voltage response graph, it can be seen that the change in voltage becomes relatively constant on average starting at the 21st second after giving steam, so that data retrieval for each steam is taken at the 21st second and above.
(a). Chicken 1
(b). Chicken 2 -0,010000
0,000000 0,010000 0,020000 0,030000 0,040000 0,050000 0,060000 0,070000
1 4 7 101316192225283134374043464952555861646770
V o lt ag e ( V o lt)
Data
Chicken 1
Sensor-1 Sensor-2 Sensor-3 Sensor-4 Sensor-5 Sensor-6
-0,020000 0,000000 0,020000 0,040000 0,060000 0,080000
1 4 7 101316192225283134374043464952555861646770
Voltage (Volt)
Data
Chicken 2
Sensor-1 Sensor-2 Sensor-3 Sensor-4 Sensor-5 Sensor-6
During the process of exposure to a series of sensors with steam from the odor of beef, a response is obtained as shown in Figures 5 (a) and 5 (b). From the voltage response graph, it can be seen that the change in voltage becomes relatively constant on average starting at the 21st second after giving steam, so that data retrieval for each steam is taken at the 21st second and above.
(a). Beef 1
(b). Beef 2
Figure 5. Voltage Response of 6 (six) Sensors to Beef Odor Steam
Based on the stability of the graph from Figures 4 until 5, the input data for the artificial neural net is taken at the 21st second for chicken, beef and lamb with 10 data each and averaged. Table 1. shows the input data for the artificial neural net.
Table 1. Training Data for Artificial Neural Networks
Data Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 Sensor 6 Noted 1 0,018768 0,009482 0,034262 0,007135 0,00611 0,044526 Chicken 1 2 0,018231 -0,00894 0,034115 0,019208 0,013099 0,043696 Chicken 2 3 -0,00064 0,017302 0,033725 0,01173 0,024438 0,068915 Beef 1 4 0,010703 0,025611 0,043695 0,014614 0,009336 0,068768 Beef 2
-0,020000 0,000000 0,020000 0,040000 0,060000 0,080000 0,100000
1 4 7 101316192225283134374043464952555861646770
Voltage (Volt)
Data Beef 1
Sensor-1 Sensor-2 Sensor-3 Sensor-4 Sensor-5 Sensor-6
-0,020000 0,000000 0,020000 0,040000 0,060000 0,080000 0,100000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
Voltage (Volt)
Data
Beef 2
Sensor-1 Sensor-2 Sensor-3 Sensor-4 Sensor-5 Sensor-6
Figure 6. Normalized Sensor Series Voltage Pattern against the Odor of Chicken
Figure 7. Normalized Sensor Series Voltage Pattern against the Odor of Beef
4. CONCLUSION
Based on the results and discussion in the previous chapter, conclusions can be drawn as follows:
1. Indentification System for Determining the Type of Meat using a series of sensors consisting of 6 conducting polymers and an artificial neural net with a kohonen algorithm with 6 inputs and 3 outputs trained to recognize the sensor response pattern to vapors from the odor of chicken and beef.
2. Testing and identification of vapor from odors has been carried out and successfully identified chicken and beef are passed to the sensor. The overall success rate of identification is 100%.
3. The sensor series voltage change response will start to stabilize after exposure to vapor for 10 to 20 seconds, if you want to use it again, the sensor chamber must first be cleaned using N2 as purge gas for 155 to 181 seconds or until the sensor series voltage is close to the rated voltage when there is no vapor exposure.
Provide a statement that what is expected, as stated in the "Introduction" chapter can ultimately result in "Results and Discussion" chapter, so there is compatibility. Moreover, it can also be added the prospect of the development of research results and application prospects of further studies into the next (based on result
0 0,01 0,02 0,03 0,04 0,05
Voltage (Volt)
Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 Sensor 6
0 0,01 0,02 0,03 0,04 0,05 0,06 0,07 0,08
Voltage (Volt)
Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 Sensor 6
The author would like to thank the Medan State Polytechnic in Indonesia
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How to Cite
Simanjuntak, B. E., Situmorang, M., Humaidi, S., & Sinambela, M. (2023). Indentification of Beef in Beef and Chicken Experiments using Conducting Polymer Sensor Series and Kohonen Algorithm Method. International Journal of Research in Vocational Studies (IJRVOCAS), 2(4), 48–55. https://doi.org/10.53893/ijrvocas.v2i4.162
