Classification of Urease Activity in Full-Fat Soybean Production by Extrusion Using Machine Learning Algorithms

Abstract

Soybean is an important food source that is frequently preferred in animal feeds with its high protein value. However, soybeans contain many bioactive compounds that are antinutritional and/or poisonous. Urease is one of the most important of these. Processes such as extrusion is used to reduce these components' effect. Here, factors such as steam pressure and temperature affect the cooking level of the product. In the case of undercooked soybeans, components that harm animal health preserve their effect, while their nutritional value decreases in case of overcooking. The urease test has been used for many years to evaluate the cooking level of soybean. Here, according to the color change on the product as a result of the test, the cooking level is evaluated by an expert. This process is mostly done manually and is dependent on expert judgment. In this study, a machine learning-based approach has been proposed to evaluate the images of urease test results. Accordingly, samples were taken from the extruder during the processing of full-fat soybean. A data set consisting of over-cooked, well-cooked and undercooked sample images was prepared by performing the urease test. A binary classification process as cooked and undercooked and a classification process with three classes was carried out with four different machine learning models on the data set. In this way, it is aimed to both automate the process and minimize the problems that may arise from expert errors. Classification achievements of 96.57% and 90.29% were achieved, respectively, for two and three class tests with the CNN-LSTM model in 10-fold cross-validation tests.

Keywords

• Convolutional neural network
• Long short-term memory network
• Soybean urease test

References

1. G. L. Cromwell, “Soybean Meal-The ‘Gold Standard,’” 1999. Accessed: Apr. 25, 2021. [Online]. Available: https://www.nutritime.com.br/arquivos_internos/artigos/soybeanmeal-thegolfstandard.pdf.
2. R. Real-Guerra, … F. S.-A. C. S., and 2013, “Soybean urease: over a hundred years of knowledge,” books.google.com, Accessed: Apr. 25, 2021. [Online]. Available: https://books.google.com/books?hl=tr&lr=&id=87WiDwAAQBAJ&oi=fnd&pg=PA317&dq=Real-Guerra,+Rafael,+Fernanda+Stanisçuaski,+and+Célia+Regina+Carlini.+%22Soybean+urease:+over+a+hundred+years+of+knowledge.%22+A+Comprehensive+Survey+of+International+Soybean+Rese.
3. K. Zhang, Q. Wu, and Y. Chen, “Detecting soybean leaf disease from synthetic image using multi-feature fusion faster R-CNN,” Comput. Electron. Agric., vol. 183, p. 106064, Apr. 2021, doi: 10.1016/j.compag.2021.106064.
4. Y. Ni et al., “Computational model and adjustment system of header height of soybean harvesters based on soil-machine system,” Elsevier, Accessed: Apr. 25, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0168169920331124.
5. E. Clarke and J. Wiseman, “Effects of extrusion conditions on trypsin inhibitor activity of full fat soybeans and subsequent effects on their nutritional value for young broilers,” Br. Poult. Sci., vol. 48, no. 6, pp. 703–712, Dec. 2007, doi: 10.1080/00071660701684255.
6. I. E. Liener, “Implications Of Antinutritional Components In Soybean Foods,” Crit. Rev. Food Sci. Nutr., vol. 34, no. 1, pp. 31–67, Jan. 1994, doi: 10.1080/10408399409527649.
7. G. B. Huntington, D. L. Harmon, N. B. Kristensen, K. C. Hanson, and J. W. Spears, “Effects of a slow-release urea source on absorption of ammonia and endogenous production of urea by cattle,” Anim. Feed Sci. Technol., vol. 130, no. 3–4, pp. 225–241, Nov. 2006, doi: 10.1016/j.anifeedsci.2006.01.012.
8. G. Qin, E. R. Ter Elst, M. W. Bosch, and A. F. B. Van Der Poel, “Thermal processing of whole soya beans: Studies on the inactivation of antinutritional factors and effects on ileal digestibility in piglets,” Anim. Feed Sci. Technol., vol. 57, no. 4, pp. 313–324, Mar. 1996, doi: 10.1016/0377-8401(95)00863-2.

9. C. Luanga Ouédraogo, E. Combe, J.-P. Lallès, R. Toullec, S. Trèche, and J.-F. Grongnet, “Nutritional value of the proteins of soybeans roasted at a small-scale unit level in Africa as assessed using growing rats.” Accessed: Apr. 25, 2021. [Online]. Available: https://rnd.edpsciences.org/articles/rnd/pdf/1999/02/RND_0926-5287_1999_39_2_ART0005.pdf.
10. S. Yalcin and A. Basman, “Effects of infrared treatment on urease, trypsin inhibitor and lipoxygenase activities of soybean samples,” Food Chem., vol. 169, pp. 203–210, Feb. 2015, doi: 10.1016/j.foodchem.2014.07.114.
11. F. S. Tabibloghmany, M. Mazaheri Tehrani, and A. Koocheki, “Optimization of the extrusion process through response surface methodology for improvement in functional and nutritional properties of soybean hull,” J. Food Sci. Technol., vol. 57, no. 11, pp. 4054–4064, Nov. 2020, doi: 10.1007/s13197-020-04439-w.
12. Y. Jing and Y. J. Chi, “Effects of twin-screw extrusion on soluble dietary fibre and physicochemical properties of soybean residue,” Food Chem., vol. 138, no. 2–3, pp. 884–889, Jun. 2013, doi: 10.1016/j.foodchem.2012.12.003.
13. N. Şenköylü, H. Akyürek, H. Ersin ŞAMLI, and A. Ağma, “Tam Yağlı Soyanın Metabolik Enerji Değerinin Broyler Performansından Tahmini,” 2004. Accessed: Apr. 25, 2021. [Online]. Available: https://dergipark.org.tr/en/pub/uluvfd/issue/13529/163661.
14. K. Krishnamurthy, H. K. Khurana, J. Soojin, J. Irudayaraj, and A. Demirci, “Infrared heating in food processing: An overview,” in Comprehensive Reviews in Food Science and Food Safety, Jan. 2008, vol. 7, no. 1, pp. 2–13, doi: 10.1111/j.1541-4337.2007.00024.x.
15. G. L. Grinblat, L. C. Uzal, M. G. Larese, and P. M. Granitto, “Deep learning for plant identification using vein morphological patterns,” Comput. Electron. Agric., vol. 127, pp. 418–424, Sep. 2016, doi: 10.1016/j.compag.2016.07.003.
16. R. A. Schwalbert, T. Amado, G. Corassa, L. P. Pott, P. V. V. Prasad, and I. A. Ciampitti, “Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil,” Agric. For. Meteorol., vol. 284, p. 107886, Apr. 2020, doi: 10.1016/j.agrformet.2019.107886.
17. M. Yoosefzadeh-Najafabadi, H. J. Earl, D. Tulpan, J. Sulik, and M. Eskandari, “Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean,” Front. Plant Sci., vol. 11, Jan. 2021, doi: 10.3389/fpls.2020.624273.
18. M. Herrero-Huerta, P. Rodriguez-Gonzalvez, and K. M. Rainey, “Yield prediction by machine learning from UAS-based multi-sensor data fusion in soybean,” Springer, doi: 10.1186/s13007-020-00620-6.
19. J. Zhang, Y. Huang, K. N. Reddy, and B. Wang, “Assessing crop damage from dicamba on non-dicamba-tolerant soybean by hyperspectral imaging through machine learning,” Wiley Online Libr., vol. 75, no. 12, pp. 3260–3272, Dec. 2019, doi: 10.1002/ps.5448.
20. A. D. de Medeiros, N. P. Capobiango, J. M. da Silva, L. J. da Silva, C. B. da Silva, and D. C. F. dos Santos Dias, “Interactive machine learning for soybean seed and seedling quality classification,” Sci. Rep., vol. 10, no. 1, p. 11267, Dec. 2020, doi: 10.1038/s41598-020-68273-y.
21. J. Xia, S. Pan, M. Yan, G. Cai, J. Yan, and G. Ning, “Prognostic model of small sample critical diseases based on transfer learning.,” Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, vol. 37, no. 1, pp. 1–9, Feb. 2020, doi: 10.7507/1001-5515.201905074.
22. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2323, 1998, doi: 10.1109/5.726791.
23. I. OZER, “Pseudo-colored rate map representation for speech emotion recognition,” Biomed. Signal Process. Control, vol. 66, p. 102502, Apr. 2021, doi: 10.1016/j.bspc.2021.102502.
24. J. Ma, F. Wu, J. Zhu, D. Xu, and D. Kong, “A pre-trained convolutional neural network based method for thyroid nodule diagnosis,” Ultrasonics, vol. 73, pp. 221–230, Jan. 2017, doi: 10.1016/j.ultras.2016.09.011.
25. Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553. Nature Publishing Group, pp. 436–444, May 27, 2015, doi: 10.1038/nature14539.
26. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798–1828, 2013, doi: 10.1109/TPAMI.2013.50.
27. I. Ozer, Z. Ozer, and O. Findik, “Noise robust sound event classification with convolutional neural network,” Neurocomputing, vol. 272, pp. 505–512, Jan. 2018, doi: 10.1016/j.neucom.2017.07.021.
28. L. Wen, X. Li, L. Gao, and Y. Zhang, “A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method,” IEEE Trans. Ind. Electron., vol. 65, no. 7, pp. 5990–5998, Jul. 2018, doi: 10.1109/TIE.2017.2774777.
29. U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, and H. Adeli, “Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals,” Comput. Biol. Med., vol. 100, pp. 270–278, Sep. 2018, doi: 10.1016/j.compbiomed.2017.09.017.
30. I. Ozer, S. Efe, and H. Ozbay, “A combined deep learning application for short term load forecasting,” Alexandria Eng. J., vol. 60.4, pp. 3807–3818, 2021, Accessed: Apr. 25, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S111001682100137X.
31. X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” JMLR Workshop and Conference Proceedings, Mar. 2010. Accessed: Apr. 25, 2021. [Online]. Available: http://www.iro.umontreal.
32. J. B. Kingma, Diederik P., “Adam: A methodfor stochastic optimization,” International Conference onLearning Representations (ICLR), 2015.
33. C. Feng, A. Mehmani, and J. Zhang, “Deep Learning-Based Real-Time Building Occupancy Detection Using AMI Data,” IEEE Trans. Smart Grid, vol. 11, no. 5, pp. 4490–4501, Sep. 2020, doi: 10.1109/TSG.2020.2982351.
34. M. Z. Islam, M. M. Islam, and A. Asraf, “A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images,” Informatics Med. Unlocked, vol. 20, p. 100412, Jan. 2020, doi: 10.1016/j.imu.2020.100412.