Application of Artificial Neural Networks to Predict Inhibition in Probiotic Experiments

Year 2021, , 106 - 125, 09.12.2021

Ecren Uzun Yaylacı

https://doi.org/10.24107/ijeas.1019382

Cited By: 1

Abstract

Artificial neural networks (ANNs) provide a modeling approach that can be used in the in vitro stages of probiotic studies. The aim of the study was to evaluate the ability of multilayer perceptron (MLP) and radial-basis function (RBF) ANNs to predict the inhibition level of indicator bacteria in co-culture experiments performed at various initial concentrations. In both types of networks, time, initial concentrations of L. lactis and Aeromonas spp. were the input variables and the inhibition concentration of Aeromonas spp. was the output value. In the construction of the models, different numbers of neurons in the hidden layer, and different activation functions were examined. The performance of the developed MLP and RBF models was tested with root mean square error (RMSE), coefficient of determination (R2) and relative error (e) statistical analysis. Both ANN models were showed a strong agreement between the predicted and experimental values. However, the developed MLP models showed higher accuracy and efficiency than the RBF models. The results indicated that ANNs developed in this study can successfully predict the inhibition concentration of Aeromonas spp. co-cultured with L. lactis in vitro and can be used to determine bacterial concentrations in the design of further experiments.

Keywords

artificial neural network, in vitro, probiotic

References

• Burke, V., Robinson, J., Cooper, M., Beaman, J., Partridge, K., Peterson, D. and Gracey, M., Biotyping and virulence factors in clinical and environmental isolates of Aeromonas species, Applied and Environmental Microbiology, 47, 1146–1149, 1984.
• Monfort, P. and Baleux, B., Dynamics of Aeromonas hydrophila, Aeromonas sobria, and Aeromonas caviae in a sewage treatment pond, Applied and Environmental Microbiology, 56, 1999–2006, 1990.
• Janda, J.M. and Abbott, S.L., Evolving concepts regarding the genus Aeromonas: an expanding Panorama of species, disease presentations, and unanswered questions. Clinical Infectious Diseases, 27:332–344.1998.
• Teunis, P. and Figueras, M.J., Reassessment of the Enteropathogenicity of mesophilic Aeromonas Species, Frontiers in Microbiology, 7, 1395, 2016.
• John, N. and Hatha, A.A.M., Distribution, extracellular virulence factors and drug resistance of motile aeromonads in freshwater ornamental fishes and associated carriage water. International Journal of Fisheries and Aquaculture, 3, 92–100, 2013.
• Garcia, F., Pilarski, F., Onaka, E.M, de Moraes, F.R. and Martins, M.L., Hematology of Piaractus mesopotamicus fed diets supplemented with vitamins C and E, challenged by Aeromonas hydrophila. Aquaculture, 271, 39–46, 2007.
• Done, H.Y., Venkatesan, A.K. and Halden, R.U., Does the recent growth of aquaculture create antibiotic resistance threats different from those associated with land animal production in agriculture? The AAPS Journal, 17, 513–524. 2015.
• Khemariya, P., Singh, S., Nath, G. and Gulati, A.K., Probiotic Lactococcus lactis: A review. Turkish Journal of Agriculture - Food Science and Technology, 556–652, 2017.
• Resende, J.A., Silva, V.L., Fontes, C.O., et al., Multidrug-resistance and toxic metal tolerance of medically important bacteria isolated from an aquaculture system, Microbes and Environments, 27,449–455, 2012
• Hossain, S., De Silva B., Dahanayake, P. and Heo, G-J., Characterization of virulence properties and multi-drug resistance profiles in motile Aeromonas spp. isolated from zebrafish (Danio rerio), Letters in Applied Microbiology, 67, 598–605, 2018. 
• Austin, B., Stuckey, L.E., Robertson, P.A.W., Effendi, I. and Griffith, D.R.W., A probiotic strain of Vibrio alginolyticus effective in reducing disease caused by Aeromonas salmonicida, Vibrio anguillarum and Vibrio ordalli., Journal of Fish Disease, 18, 93–96, 1995.
• Moriarty, D.J.W., The role of microorganisms in aquaculture ponds, Aquaculture, 151, 333–349, 1997.
• Irianto, A. and Austin, B., Probiotics in aquaculture, Journal of Fish Disease, 25, 633–642, 2002.
• Pérez-Sánchez, T., Ruiz-Zarzuela, I., Blas, I. and Balcázar, J.L., Probiotics in aquaculture: a current assessment, Reviews in Aquaculture, 6, 133–146, 2014.
• Xia, Y., Lu, M., Chen, G., Cao, J., Gao, F., Wang, M., Liu, Z., Zhang, D., Zhu, H. and Yi, M., Effects of dietary Lactobacillus rhamnosus JCM1136 and Lactococcus lactis subsp. lactis JCM5805 on the growth, intestinal microbiota, morphology, immune response and disease resistance of juvenile Nile tilapia, Oreochromis niloticus, Fish and Shellfish Immunology, 76, 368–379, 2018.
• Abumourad, I.M.K., Abbas, W.T., Awaad, E.S.,  Authman, M.M.N., El-Shafei, K., Sharaf, O.M., Ibrahim, G.A., Sadek, Z.I. and  El-Sayed, H.S., Evaluation of Lactobacillus plantarum as a probiotic in aquaculture: emphasis on growth performance and innate immunity, The Journal of Applied Sciences Research, 9, 572–582, 2013.
• Salminen, S., von Wright, A., Morelli, L., Marteau, P., Brassart, D., de Vos, W.M., Fondén, R., Saxelin, M., Collins, K., Mogensen, G., Birkeland, S.E. and Mattila-Sandholm, T., Demonstration of safety of probiotics–a review, International Journal of Food Microbiology, 44, 93–106, 1998
• Balcázar, J.L., de Blas, I., Ruiz-Zarzuela, I., Vendrell, D., Gironés, O. and Muzquiz, J.L., Sequencing of variable regions of the 16S rRNA gene for identification of lactic acid bacteria isolated from the intestinal microbiota of healthy salmonids. Comparative Immunology, Microbiology & Infectious Diseases, 30, 111–118, 2007.
• Mauguin, S. and Novel, G., Characterization of lactic acid bacteria isolated from seafood. Applied and Environmental Microbiology Journal, 76, 616–625, 1994.
• Sequeiros, C., Vallejo, M., Marguet, E.R. and Olivera, N.L. Inhibitory activity against the fish pathogen Lactococcus garvieae produced by Lactococcus lactis TW34, a lactic acid bacterium isolated from the intestinal tract of a Patagonian fish,  Archives of Microbiology, 192, 237–245, 2010. 
• Nishant, T., Sathish, Kumar, D., Arun Kumar, R., Hima Bindu, K. and Raviteja, Y., Bacteriocin producing probiotic lactic acid bacteria, Journal of Microbial & Biochemical Technology, 3, 121–124, 2011.
• Kumar, M., Jain, A.K., Ghosh, M. and Ganguli, A., Bacteriocin of Lactococcus Lactis. Journal of Food Safety, 32, 369–378, 2012.
• Messi, P., Bondi, M., Sabia, C., Battini, R. and Manicardi, G., Detection and preliminary characterization of a bacteriocin (plantaricin 35d) produced by a Lactobacillus plantarum strain, International Journal of Food Microbiology, 64, 193–198, 2001.
• Todorov, S. and Dicks, L.M.T., Pediocin ST18, an anti-listerial bacteriocin produced by Pediococcus pentosaceus ST18 isolated from boza, a traditional cereal beverage from Bulgaria, Process Biochemistry, 40, 365–370, 2005.
• Ayeni, F. A., Sánchez, B., Adeniyi, B.A., de Los Reyes-Gavilán, C.G., Margolles, A. and Ruas-Madiedo, P., Evaluation of the functional potential of Weissella and Lactobacillus isolates obtained from Nigerian traditional fermented foods and cow’s intestine, International Journal of Food Microbiology, 147, 97–104, 2011.
• Jacobsen, C.N., Rosenfeldt, Nielsen, V., Hayford, A.E., Møller, P.L., Michaelsen, K.F., Paerregaard, A., Sandström, B., Tvede, M. and Jakobsen, M., Screening of probiotic activities of forty-seven strains of Lactobacillus spp. by in vitro techniques and evaluation of the colonization ability of five selected strains in humans, Applied and Environmental Microbiology, 65, 4949–4956, 1999.
• Zielińska, D., Rzepkowska, A., Radawska, A. and Zieliński, K., in vitro screening of selected probiotic properties of Lactobacillus strains isolated from traditional fermented cabbage and cucumber, Current Microbiology, 70, 183–194, 2015.
• Begley, M., Gahan, C.G.M. and Hill, C., The interaction between bacteria and bile, FEMS Microbiology Reviews, 29, 625–651, 2005.
• Tjørve, K.M.C. and Tjørve, E., The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family, PloS One, 12, e0178691, 2017.
• Hiura, S. Koseki, S. and Koyama, K., Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database, Scientific Reports, 11, 10613, 2021.
• Kim, K. and Hong, J.S.A., Hybrid decision tree algorithm for mixed numeric and categorical data in regression analysis, Pattern Recognition Letters, 98, 39–45, 2017.
• Hajmeer, M., Basheer, I. and Cliver, D.O., Survival curves of Listeria monocytogenes in chorizos modeled with artificial neural networks. Food Microbiology , 23, 561–570, 2006.
• Uzun Yaylacı, E., Yaylacı, M., Ölmez, H. and Birinci, A., Artificial neural network calculations for a receding contact problem, Computers and Concrete , 25, 551–563, 2020.
• Yaylacı, M., Eyüboğlu, A., Adıyaman, G., Uzun Yaylacı, E., Öner, E. and Birinci, A., Assessment of different solution methods for receding contact problems in functionally graded layered mediums, Mechanics of Materials 154, 103730, 2021.
• Trujillo, M.C.R., Alarcon, T.E., Dalmau, O.S. and Ojeda, A.Z., Segmentation of carbon nanotube images through an artificial neural network, Soft Computing, 21, 611–625, 2017.
• Yan, H., Jiang, Y., Zheng, J., Peng, C. and Li, Q., A multilayer perceptron based medical decision support system for heart disease diagnosis, Expert Systems With Applications, 30, 272–81, 2006.
• Uzun Yaylacı E., Developing a differentiation technique for the pathogenic bacteria causing disease in sea bass (Dicentrarchus labrax) by using artifıcial neural networks. Doctoral thesis, Karadeniz Technical University, The Graduate School of Natural and Applied Sciences, Trabzon, Turkey, 47p., 2019
• Jarvis, B., Statistical aspects of the microbiological analysis of foods. Elsevier, Amsterdam, 1989
• Panagou, E.Z., Tassou, C.C., Saravanos, E.K. and Nychas, G.J., Application of neural networks to simulate the growth profile of lactic acid bacteria in green olive fermentation. Journal of Food Protection, 70, 1909–1916, 2007.
• Fath, A.H., Madanifar, F. and Abbasi, M., Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems, Petroleum, 6, 80–91, 2020.
• Cakiroglu, E., Comez, I. and Erdol, R., Application of artificial neural network to double receding contact problem with a rigid stamp, Structural Engineering and Mechanics, 21, 205–220, 2005.
• Yu, H., Xie, T., Paszczynski, S. and Wilamowski, B., Advantages of Radial Basis Function Networks for Dynamic System Design, IEEE Transactions on Industrial Electronics, 58, 5438–5450, 2011.
• Bayram, S., Ocal, M., E., Laptali, Oral, E. and Atis, C.D.,  Comparison of multi-layer perceptron (MLP) and radial basis function (RBF) for construction cost estimation: the case of Turkey, The Journal of Civil Engineering and Management, 22, 480–490, 2016.
• Wawrzyniak, J., Application of artificial neural networks to assess the mycological state of bulk stored rapeseeds, Agriculture, 10, 567, 2020.
• Kumar, A., Kundu, S. and Debnath, M., Effects of the probiotics Lactococcus lacttis (MTCC-440) on Salmonella enteric serovar Typhi in co-culture study,  Microbial Pathogenesis, 120, 42–46, 2018.
• Vaseeharan, B. and Ramasamy, P., Control of pathogenic Vibrio spp. by Bacillus subtilis BT23, a possible probiotic treatment for black tiger shrimp Penaeus monodon, Letters in Applied Microbiology, 36, 83–87, 2003.
• Eren, B. and Eyüpoğlu, V., Modelling of recovery efficiency of Ni(II) ion using artificial neural network, in 6th International Advanced Tech-nologies Symposium (IATS’11), 16–18 May 2011, Elazığ, Turkey.
• Kayadelen, C., Taşkıran, T., Günaydın, O. and Fener, M., Adaptive neuro-fuzzy modeling for the swelling potential of compacted soils, Environmental Earth Sciences, 59, 109–115, 2009.
• Orhan, U., Hekim, M. and Ozer, M., EEG signals classification using the K-means clustering and a multilayer perceptron neural network model, Expert Systems With Applications, 38, 13475–13481, 2011.
• Le, Cun, Y., Denker, J.S. and Solla, S.A., Optimal brain damage, Advances in Neural Information Processing Systems, 2, 598–605, 1990.