Using orange data mining for meat classification: The preliminary application of machine learning
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Abstract
Orange Data Mining study on the classification of buffalo, beef, and goat meats, Machine Learning (ML) classifiers including Support Vector Machine (SVM), Neural Network (NN), and Naïve Bayes (NB) are well performed to achieve 100% accuracy across all features. Random Forest (RF) demonstrated the best performance more than 97% in AUC, CA, F1, and MCC. Other models such as Gradient Boosting (GB), AdaBoost, CN2 Rule Induction (CN2), Decision Tree (DT), and k-Nearest Neighbors (KNN) are performed better but there were less efficient. In the application of specific classifiers for species-based meat quality attributes, SVM, NN and NB should be considered as the best options
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References
Alqasmi, D. U., Alzuhair, A. and Bama’bad, A. (2020). Enhanced system for computer-aided detection of mri brain tumors. Signal & Image Processing: An International Journal, 11:25-31.
AMSA (2012). Meat Color Measurement Guidelines. American Meat Science Association, IL, 124 p. Retrieved from: https://meatscience.org/publications-resources/printed-publica tions/amsa-meat-color-measurement-guidelines
AMSA (2016). Research Guidelines for Cookery, Sensory Evaluation, and Instrumental Tenderness Measurements of Meat, 2nd ed. American Meat Science Association, IL, 105 p. Retrieved from: https://meatscience.org/publications-resources/printed-publications/ sensory-and-tenderness-evaluation-guidelines
Arora, S. (2018). Mathematics of machine learning: An introduction. Proceedings of the International Congress of Mathematicians, 1:377-390.
Association of Official Analytical Chemists (AOAC). (2012). Official Method of Analysis (Association of Official Analytical Chemist (ed.); 19th ed.).
Ayaz, H., Ahmad, M., Sohaib, A., Yasir, M. N., Zaidan, M. A., Ali, M., Khan, M. H. and Saleem, Z. (2020). Myoglobin-based classification of minced meat using hyperspectral imaging. Applied Sciences, 10:6862.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer New York.
Bourne, M. C. (1978). Texture profile analysis. Food Technology, 32:62-67.
Buza, K., Nanopoulos, A. and Nagy, G. (2015). Nearest neighbor regression in the presence of bad hubs. Knowledge-Based Systems, 86:250-260.
Dayal, M., Gupta, M., Gupta, M., Bara, A. R. and Chaubey, C. (2023). Introduction to machine learning methods with application in agriculture. In Applying Drone Technologies and Robotics for Agricultural Sustainability. Retrieved from: https://doi.org/10.4018/978-1-6684-6413-7.ch012
Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M. and Zupan, B. (2013). Orange: Data Mining Toolbox in Python. Journal of Machine Learning Research, 14:2349-2353.
Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55:78-87.
Duarte, D. and Ståhl, N. (2019). Machine learning: A concise overview. Studies in Big Data, 46:27-58.
Elsaadouny, M., Barowski, J. and Rolfes, I. (2021). Unsupervised learning implementation for SAR Images Clustering. International Conference on Electromagnetics in Advanced Applications, ICEAA 2021, 104. Retrieved from: https://doi.org/10.1109/ICEAA52647. 2021.9539661
Evidently, A. I. (2024). A complete guide to classification metrics in machine learning. Retrieved from: https://www.evidentlyai.com/classification-metrics
Forsyth, D. (2018). Probability and Statistics for Computer Science. In: Probability and Statistics for Computer Science. Springer International Publishing.
Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning. MIT Press.
Gupta, V., Mishra, V. K., Singhal, P. and Kumar, A. (2022). An overview of supervised machine learning algorithm. Proceedings of the 2022 11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022, pp.87-92.
Hamidi, O., Poorolajal, J., Sadeghifar, M., Abbasi, H., Maryanaji, Z., Faridi, H. R. and Tapak, L. (2015). A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran. Theoretical and Applied Climatology, 119:723-731.
Han, H, and Jiang, X. (2014). Overcome Support Vector Machine diagnosis overfitting. Cancer Informatics, 13:145-158.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning. Springer New York.
Head, M. and Aloqaily, W. (2022). 9 - Machine learning approach for seismic assessment. In: Seismic Evaluation, Damage, and Mitigation in Structures. Woodhead Publishing, pp.237-247.
Honikel, K. O. (1998). Reference methods for the assessment of physical characteristics of meat. Meat Science, 49:447-457.
Huesmann, K., Klemm, S., Linsen, L. and Risse, B. (2020). Exploiting the full capacity of deep neural networks while avoiding overfitting by targeted sparsity regularization. Retrieved from: https://doi.org/10.48550/arxiv.2002.09237
Ibrahim, Z., Isa, D., Rajkumar, R. and Kendall, G. (2009). Document zone content classification for technical document images using Artificial Neural Networks and Support Vector Machines. 2nd International Conference on the Applications of Digital Information and Web Technologies, 345-350. Retrieved from: https://doi.org/10.1109/ICADIWT.2009. 5273957
Ioannou, C. and Vassiliou, V. (2021). Network attack classification in IOT using support vector machines. Journal of Sensor and Actuator Networks, 10:58.
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer.
Jordan, M. I., and Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349:255-260.
Khorramifar, A., Rasekh, M., Karami, H., Covington, J. A., Derakhshani, S. M., Ramos, J. and Gancarz, M. (2022). Application of MOS gas sensors coupled with chemometrics methods to predict the amount of sugar and carbohydrates in potatoes. Molecules, 27: 3508.
Kumar, H. and Hasija, Y. (2021). Machine Learning in Medical Image Processing. Smart Innovation, Systems and Technologies, 195:377-383.
Kumar, R., Aggarwal, Y. and Kumar Nigam, V. (2022). Heart rate dynamics in the prediction of coronary artery disease and myocardial infarction using artificial neural network and support vector machine. Journal of Applied Biomedicine, 20:70-79.
Li, A., Li, C., Gao, M., Yang, S., Liu, R., Chen, W. and Xu, K. (2021a). Beef cut classification using multispectral imaging and machine learning method. Frontiers in Nutrition, 8: 755007.
Li, Q., Yan, M. and Xu, J. (2021b). Optimizing convolutional neural network performance by mitigating underfitting and overfitting. 19th International Conference on Computer and Information Science (ICIS), pp. 126-131.
Mahmud, M. S., Huang, J. Z. and Fu, X. (2020). Variational autoencoder-based dimensionality reduction for high-dimensional small-sample data classification. International Journal of Computational Intelligence and Applications, 19:2050002.
Malikhah, M., Sarno, R. and Sabilla, S. (2021). Ensemble learning for optimizing classification of pork adulteration in beef based on electronic nose dataset. International Journal of Intelligent Engineering and Systems, 14:44-55.
Medeiros, E. C., Almeida, L. M. and Filho, J. G. de A. T. (2021). Computer vision and machine learning for tuna and salmon meat classification. Informatics, 8:70.
Nainggolan, S. P. and Sinaga, A. (2023). Comparative analysis of accuracy of random forest and gradient boosting classifier algorithm for diabetes classification. Sebatik, 27:97-102.
Onyezewe, A., Kana, A. F., Abdullahi, F. B. and Abdulsalami, A. O. (2021). An enhanced adaptive k-Nearest Neighbor classifier using simulated annealing. International Journal of Intelligent Systems and Applications, 13:34-44.
Pandurang Adi, S., Bettadapura Adishesha, V., Vaidyanathan Bharadwaj, K. and Narayan, A. (2020). Earthquake damage prediction using random forest and gradient boosting classifier. American Journal of Biological and Environmental Statistics, 6:58.
Peng, G. C. Y., Alber, M., Tepole, A. B., Cannon, W. R., De, S., Dura-Bernal, S., Garikipati, K., Karniadakis, G., Lytton, W. W., Perdikaris, P., Petzold, L. and Kuhl, E. (2021). Multiscale modeling meets machine learning: What can we learn? Archives of Computational Methods in Engineering: State of the Art Reviews, 28:1017-1037.
Phoemchalard, C., Tathong, T., Pornanek, P., Uriyapongson, S. and Cherdthong, A. (2021). Quality attributes of buffalo meat using precision sous vide cooking device. ARPN Journal of Engineering and Applied Sciences, 16:2117-2125.
Phoemchalard, C., Uriyapongson, S., Tathong, T. and Pornanek, P. (2022). 1H NMR metabolic
profiling and meat quality in three beef cattle breeds from northeastern Thailand. Foods, 11:3821.
Prieditis, A. and Sapp, S. (2013). Lazy Overfitting Control. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, pp.481-491.
Qiu, J., Lin, Y., Wu, J., Xiao, Y., Cai, H. and Ni, H. (2024). Rapid beef quality detection using spectra pre-processing methods in electrical impedance spectroscopy and machine learning. International Journal of Food Science and Technology, 59:1624-1634.
Rani, P. (2017). A Review of various KNN techniques. International Journal for Research in Applied Science and Engineering Technology, 5:1174-1179.
Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P. and Green, R. (2019). Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Academic Pathology, 6:2374289519873088.
Reddy, M. N., Latchmana Kumar, M. R., Kumar, P. B. S., Thirumalai, S. and Nirmala Devi, M. (2022). Performance enhancement by tuning hyperparameters of random forest classifier for hardware trojan detection. In M. Saraswat, S. Roy, C. Chowdhury, and A. H. Gandomi (Eds.), Proceedings of International Conference on Data Science and Applications, pp.177-191.
Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall.
Sathya, D., Sudha, V. and Jagadeesan, D. (2022). Application of machine learning techniques in healthcare. In: Research Anthology on Machine Learning Techniques, Methods, and Applications. https://doi.org/10.4018/978-1-6684-6291-1.ch067
Sen, R. and Das, S. (2023). Unsupervised Learning. Indian Statistical Institute Series, 305-318. https://doi.org/10.1007/978-981-19-2008-0_21
Setiadi, I. C., Hatta, A. M., Koentjoro, S., Stendafity, S., Azizah, N. N. and Wijaya, W. Y. (2022). Adulteration detection in minced beef using low-cost color imaging system coupled with deep neural network. Frontiers in Sustainable Food Systems, 6:1073969
Sharma, R. and Bharti, V. (2021). A Review Towards Machine Learning Spectrum for Bioinformatics. Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, 199-202.
Sopiyan, M., Fauziah, F. and Wijaya, Y. F. (2022). Fraud detection using random forest classifier, logistic regression, and gradient boosting classifier algorithms on credit cards. JUITA: Jurnal Informatika, 10:77.
Tagliaferri, F., Viola, I. M. and Flay, R. G. J. (2015). Wind direction forecasting with artificial neural networks and support vector machines. Ocean Engineering, 97:65-73.
Tantuğ, A. C. and Eryiğit, G. (2006). Performance analysis of naϊve bayes classification, support vector machines and neural networks for spam categorization. In Applied Soft Computing Technologies: The Challenge of Complexity, Springer-Verlag, pp.495-504.
Thakur, U. K. (2023). The role of machine learning in customer experience. In: Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics. Retrieved from: https://doi.org/10.4018/978-1-6684-7105-0.ch005
Torres, J. P., Caro, A., Avila, M. D. M., Perez-Palacios, T., Antequera, T. and Rodriguez, P. G. (2022). A computer-aided inspection system to predict quality characteristics in food technology. IEEE Access, 10:71496-71507.
Trivedi, U. B., Bhatt, M. and Srivastava, P. (2021). Prevent Overfitting Problem in Machine Learning: A Case Focus on Linear Regression and Logistics Regression. In P. K. Singh, Z. Polkowski, S. Tanwar, S. K. Pandey, G. Matei, and D. Pirvu (Eds.), Innovations in Information and Communication Technologies (IICT-2020). Springer International Publishing, pp.345-349.
Wang, Y. (2023). Comparison of machine learning strategies in hazardous asteroids prediction. Highlights in Science, Engineering and Technology, 39:201-208.
Yang, F., Sun, J., Cheng, J., Fu, L., Wang, S. and Xu, M. (2023). Detection of starch in minced chicken meat based on hyperspectral imaging technique and transfer learning. Journal of Food Process Engineering, 46:e14304.