Application of k-nearest neighbors method for detection of beef authenticity based on beef image
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Published:
Jun 18, 2024
Keywords:
Authenticity detection, Beef, HOG feature extraction, Image classification, K-Nearest Neighbors
Section
Computer Science
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Abstract
Beef authenticity detection is a significant concern in today's food industry. This study proposes the K-Nearest Neighbors (K-NN) method based on the extraction of the Histogram of Oriented Gradients (HOG) feature to detect the authenticity of beef based on images. A dataset of 40 images of real and fake beef was collected and aggregated into 240 images to increase the variety of data. The imagery is changed to grayscale, and the HOG feature is extracted to capture texture and shape information. The K-NN model is built with optimized parameters using Grid Search and cross-validation techniques. The model was evaluated by measuring accuracy, precision, recall, and F1-score on the test data. The results show that the K-NN model with HOG feature extraction can achieve an accuracy of 80.56%, precision of 87.10%, recall of 72.97%, and F1-score of 72.97% in classifying real and fake beef. These findings confirm the effectiveness of the proposed method for the rapid and accurate detection of beef authenticity. This research contributes to developing image-based food authenticity detection methods that can be applied to increase consumer confidence in the food industry
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How to Cite
gunawan, G. ., Moonap, D. A. and Fadhilah, N. . (2024) “Application of k-nearest neighbors method for detection of beef authenticity based on beef image ”, Jurnal Mantik, 8(1), pp. 818-827. doi: 10.35335/mantik.v8i1.5281.
References
Abbasianjahromi, H., & Aghakarimi, M. (2023). Safety performance prediction and modification strategies for construction projects via machine learning techniques. Engineering, Construction and Architectural Management, 30(3), 1146–1164. https://doi.org/10.1108/ECAM-04-2021-0303
Artavia, G., Cortés-Herrera, C., Granados-Chinchilla, F., & Pinto, A. Di. (2021). Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis. Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/foods
Betgeri, S. N., Vadyala, S. R., Matthews, J. C., Madadi, M., & Vladeanu, G. (2023). Wastewater pipe condition rating model using K-nearest neighbors. Tunnelling and Underground Space Technology, 132, 104921. https://doi.org/10.1016/j.tust.2022.104921
Chen, X., Lu, L., Xiong, X., Xiong, X., & Liu, Y. (2020). Development of a real-time PCR assay for the identification and quantification of bovine ingredient in processed meat products. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-59010-6
Cronje, J. C. (2020). Designing questions for research design and design research in e-learning. Electronic Journal of E-Learning, 18(1), 13–24. https://doi.org/10.34190/EJEL.20.18.1.002
Gallego, A. J., Rico-Juan, J. R., & Valero-Mas, J. J. (2022). Efficient k-nearest neighbor search based on clustering and adaptive k values. Pattern Recognition, 122, 108356. https://doi.org/10.1016/j.patcog.2021.108356
Hamed, A., Sobhy, A., & Nassar, H. (2021). Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm. Arabian Journal for Science and Engineering, 46(9), 8261–8272. https://doi.org/10.1007/s13369-020-05212-z
Hosu, V., Lin, H., Sziranyi, T., & Saupe, D. (2020). KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment. IEEE Transactions on Image Processing, 29, 4041–4056. https://doi.org/10.1109/TIP.2020.2967829
Kaufmann, K., & Peil, C. (2020). The mobile instant messaging interview (MIMI): Using WhatsApp to enhance self-reporting and explore media usage in situ. Mobile Media and Communication, 8(2), 229–246. https://doi.org/10.1177/2050157919852392
Khaled, A. Y., Parrish, C. A., & Adedeji, A. (2021). Emerging nondestructive approaches for meat quality and safety evaluation—A review. Comprehensive Reviews in Food Science and Food Safety, 20(4), 3438–3463. https://doi.org/10.1111/1541-4337.12781
Laghrissi, F. E., Douzi, S., Douzi, K., & Hssina, B. (2021). Intrusion detection systems using long short-term memory (LSTM). Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00448-4
Lin, X., Chang, S.-C., Chou, T.-H., Chen, S.-C., & Ruangkanjanases, A. (2021). Consumers’ intention to adopt blockchain food traceability technology towards organic food products. International Journal of Environmental Research and Public Health, 18(3), 912. https://doi.org/10.3390/ijerph18030912
Li, Q., Diao, Y., Chen, Q., & He, B. (2022). Federated learning on non-iid data silos: An experimental study. 2022 IEEE 38th International Conference on Data Engineering (ICDE), 965–978. https://doi.org/10.1109/ICDE53745.2022.00077
Liu, J., Ellies-Oury, M.-P., Stoyanchev, T., & Hocquette, J.-F. (2022). Consumer perception of beef quality and how to control, improve and predict it? Focus on eating quality. Foods, 11(12), 1732. https://doi.org/10.3390/foods11121732
Li, Y., Liu, S., Meng, F., Liu, D., Zhang, Y., Wang, W., & Zhang, J. (2020). Comparative review and the recent progress in detection technologies of meat product adulteration. Comprehensive Reviews in Food Science and Food Safety, 19(4), 2256–2296. https://doi.org/10.1111/1541-4337.12579
Momtaz, M., Bubli, S. Y., & Khan, M. S. (2023). Mechanisms and health aspects of food adulteration: A comprehensive review. Foods, 12(1), 199. https://doi.org/10.3390/foods12010199
Pinto, D. L., Selli, A., Tulpan, D., Andrietta, L. T., Garbossa, P. L. M., Vander Voort, G., Munro, J., McMorris, M., Alves, A. A. C., & Carvalheiro, R. (2023). Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms. Livestock Science, 267, 105152. https://doi.org/10.1016/j.livsci.2022.105152
Sarno, R., Triyana, K., Sabilla, S. I., Wijaya, D. R., Sunaryono, D., & Fatichah, C. (2020). Detecting Pork Adulteration in Beef for Halal Authentication Using an Optimized Electronic Nose System. IEEE Access, 8, 221700–221711. https://doi.org/10.1109/ACCESS.2020.3043394
Shaban, W. M., Rabie, A. H., Saleh, A. I., & Abo-Elsoud, M. A. (2020). A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowledge-Based Systems, 205, 106270. https://doi.org/10.1016/j.knosys.2020.106270
Smaoui, S., Tarapoulouzi, M., Agriopoulou, S., D’Amore, T., & Varzakas, T. (2023). Current state of milk, dairy products, meat and meat products, eggs, fish and fishery products authentication and chemometrics. Foods, 12(23), 4254. https://doi.org/10.3390/foods12234254
Uddin, S., Haque, I., Lu, H., Moni, M. A., & Gide, E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-10358-x
Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making, 19(1). https://doi.org/10.1186/s12911-019-1004-8
Wijaya, D. R., Syarwan, N. F., Nugraha, M. A., Ananda, D., Fahrudin, T., & Handayani, R. (2023). Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization. IEEE Access, 11, 62484–62495. https://doi.org/10.1109/ACCESS.2023.3286980
Yousefi, D. B. M., Rafie, A. S. M., Al-Haddad, S. A. R., & Azrad, S. (2022). A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles. Ieee Access, 10, 80071–80091. https://doi.org/10.1109/ACCESS.2022.3194507
Zhou, J., Brereton, P., & Campbell, K. (2024). Progress towards achieving intelligent food assurance systems. Food Control, 110548. https://doi.org/10.1016/j.foodcont.2024.110548
Artavia, G., Cortés-Herrera, C., Granados-Chinchilla, F., & Pinto, A. Di. (2021). Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis. Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/foods
Betgeri, S. N., Vadyala, S. R., Matthews, J. C., Madadi, M., & Vladeanu, G. (2023). Wastewater pipe condition rating model using K-nearest neighbors. Tunnelling and Underground Space Technology, 132, 104921. https://doi.org/10.1016/j.tust.2022.104921
Chen, X., Lu, L., Xiong, X., Xiong, X., & Liu, Y. (2020). Development of a real-time PCR assay for the identification and quantification of bovine ingredient in processed meat products. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-59010-6
Cronje, J. C. (2020). Designing questions for research design and design research in e-learning. Electronic Journal of E-Learning, 18(1), 13–24. https://doi.org/10.34190/EJEL.20.18.1.002
Gallego, A. J., Rico-Juan, J. R., & Valero-Mas, J. J. (2022). Efficient k-nearest neighbor search based on clustering and adaptive k values. Pattern Recognition, 122, 108356. https://doi.org/10.1016/j.patcog.2021.108356
Hamed, A., Sobhy, A., & Nassar, H. (2021). Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm. Arabian Journal for Science and Engineering, 46(9), 8261–8272. https://doi.org/10.1007/s13369-020-05212-z
Hosu, V., Lin, H., Sziranyi, T., & Saupe, D. (2020). KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment. IEEE Transactions on Image Processing, 29, 4041–4056. https://doi.org/10.1109/TIP.2020.2967829
Kaufmann, K., & Peil, C. (2020). The mobile instant messaging interview (MIMI): Using WhatsApp to enhance self-reporting and explore media usage in situ. Mobile Media and Communication, 8(2), 229–246. https://doi.org/10.1177/2050157919852392
Khaled, A. Y., Parrish, C. A., & Adedeji, A. (2021). Emerging nondestructive approaches for meat quality and safety evaluation—A review. Comprehensive Reviews in Food Science and Food Safety, 20(4), 3438–3463. https://doi.org/10.1111/1541-4337.12781
Laghrissi, F. E., Douzi, S., Douzi, K., & Hssina, B. (2021). Intrusion detection systems using long short-term memory (LSTM). Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00448-4
Lin, X., Chang, S.-C., Chou, T.-H., Chen, S.-C., & Ruangkanjanases, A. (2021). Consumers’ intention to adopt blockchain food traceability technology towards organic food products. International Journal of Environmental Research and Public Health, 18(3), 912. https://doi.org/10.3390/ijerph18030912
Li, Q., Diao, Y., Chen, Q., & He, B. (2022). Federated learning on non-iid data silos: An experimental study. 2022 IEEE 38th International Conference on Data Engineering (ICDE), 965–978. https://doi.org/10.1109/ICDE53745.2022.00077
Liu, J., Ellies-Oury, M.-P., Stoyanchev, T., & Hocquette, J.-F. (2022). Consumer perception of beef quality and how to control, improve and predict it? Focus on eating quality. Foods, 11(12), 1732. https://doi.org/10.3390/foods11121732
Li, Y., Liu, S., Meng, F., Liu, D., Zhang, Y., Wang, W., & Zhang, J. (2020). Comparative review and the recent progress in detection technologies of meat product adulteration. Comprehensive Reviews in Food Science and Food Safety, 19(4), 2256–2296. https://doi.org/10.1111/1541-4337.12579
Momtaz, M., Bubli, S. Y., & Khan, M. S. (2023). Mechanisms and health aspects of food adulteration: A comprehensive review. Foods, 12(1), 199. https://doi.org/10.3390/foods12010199
Pinto, D. L., Selli, A., Tulpan, D., Andrietta, L. T., Garbossa, P. L. M., Vander Voort, G., Munro, J., McMorris, M., Alves, A. A. C., & Carvalheiro, R. (2023). Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms. Livestock Science, 267, 105152. https://doi.org/10.1016/j.livsci.2022.105152
Sarno, R., Triyana, K., Sabilla, S. I., Wijaya, D. R., Sunaryono, D., & Fatichah, C. (2020). Detecting Pork Adulteration in Beef for Halal Authentication Using an Optimized Electronic Nose System. IEEE Access, 8, 221700–221711. https://doi.org/10.1109/ACCESS.2020.3043394
Shaban, W. M., Rabie, A. H., Saleh, A. I., & Abo-Elsoud, M. A. (2020). A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowledge-Based Systems, 205, 106270. https://doi.org/10.1016/j.knosys.2020.106270
Smaoui, S., Tarapoulouzi, M., Agriopoulou, S., D’Amore, T., & Varzakas, T. (2023). Current state of milk, dairy products, meat and meat products, eggs, fish and fishery products authentication and chemometrics. Foods, 12(23), 4254. https://doi.org/10.3390/foods12234254
Uddin, S., Haque, I., Lu, H., Moni, M. A., & Gide, E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-10358-x
Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making, 19(1). https://doi.org/10.1186/s12911-019-1004-8
Wijaya, D. R., Syarwan, N. F., Nugraha, M. A., Ananda, D., Fahrudin, T., & Handayani, R. (2023). Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization. IEEE Access, 11, 62484–62495. https://doi.org/10.1109/ACCESS.2023.3286980
Yousefi, D. B. M., Rafie, A. S. M., Al-Haddad, S. A. R., & Azrad, S. (2022). A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles. Ieee Access, 10, 80071–80091. https://doi.org/10.1109/ACCESS.2022.3194507
Zhou, J., Brereton, P., & Campbell, K. (2024). Progress towards achieving intelligent food assurance systems. Food Control, 110548. https://doi.org/10.1016/j.foodcont.2024.110548
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