Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Intestinal broiler microflora estimation by artificial neural network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Microflora population of poultry was affected by various factors. Many methods and techniques were developed to study microflora population. But, most of them confronted some problems. Moreover, being costly, laborious, and time-consuming made it impossible to measure microflora population several times. In this study, we tried to estimate intestinal microflora population using artificial neural network (ANN). Lactic acid bacteria were used as model of microflora population. Time and lactic acid bacteria were used as input and output variables, respectively. The best model of ANN was determined based on coefficient of determination, root mean square error, and mean absolute error criteria. The results of current study have shown that ANN is appropriate, cheap, and reliable tools to estimate intestinal microflora population (lactic acid bacteria) of broiler at different ages.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Abdolmaleki P, Naderimanesh H, Falah Farbord SK, Taghdir M, Sadeghi M (2001) Predict of protein structure by use of artificial neural network. 10th international biologic conference Iran. Shiraz University, 91212 (In Persian)

  2. Apajalahti JHA, Kettuneen A, Holben WE, Nurminrn PH, Rautonen N, Mutanen M (2002) Culture-independent microbial community analysis reveals that inuline in the diet primarily affects previously unknown bacteria in the mouse ceacum. Appl Environ Microbiol 68:4986–4995

    Article  Google Scholar 

  3. Apajalahti JHA, Kettuneen A, Bedford MR, Holben WE (2001) Percent G + C profiling accurately reveals diet-related differences in the gastrointestinal microbial community of broiler chickens. Appl Environ Microbiolo 67:5656–5667

    Article  Google Scholar 

  4. Barrow PA (1992) Probiotics for chicken. In: Fuller R (ed) Probiotics: the scientific basis. Chapman and Hall, London, pp 229–234

    Google Scholar 

  5. Chaveerch P, Keuzenkamp DA, Lipman LJA, Van Knapen F (2004) Effect of organic acids in drinking water for young broilers on campylobacter infection, volatile fatty acid production, gut microflora and histological cell changes. Poult Sci 83:330–334

    Google Scholar 

  6. Cigizoglu HK, Kisi O (2005) Flow prediction by two back propagation techniques using k-fold partitioning of neural network training data. Nord Hydrol 36(1):1–16

    Google Scholar 

  7. Engberg RM, Hedmann MS, Steenfeldt S, Jensen B (2004) Influence of whole wheat and xylanase on broiler performance and microbial composition and activity in the digestive tract. Poult Sci 83:925–938

    Google Scholar 

  8. Engberg RM, Hedmann MS, Jensen B (2002) The influence of grinding and pelleting of feed on the microbial composition and activity in the digestive tract of broiler chickens. Br Poultr Sci 43:569–579

    Article  Google Scholar 

  9. Escalante-Minakata P, Ibarra-Junquera V, Rosu HC, De Leon-Rodriguez A, Gonzalez-Garcia R (2009) Online monitoring of Mezcal fermentation based on redox potential measurements. Bioprocess Biosyst Eng 32:47–52

    Article  Google Scholar 

  10. Govindaraju RS, Ramachander Rao A (eds) (2000) Water science and technology library. Artificial neural networks in hydrology. Kluwer Academic Publishers, Dordrecht, The Netherlands

    Google Scholar 

  11. Hubener K, Vahjen W, Simon O (2002) Bacterial responses to different dietary cereal types and xylanase supplementation in the intestine of broiler chicken. Arch Anim Nutr 56:167–187

    Google Scholar 

  12. Imrie CE, Durucan S, Korre A (2000) River flow prediction using artificial neural networks: generalization beyond the calibration range. J Hydrol 233:138–153

    Article  Google Scholar 

  13. Jain A, Srintvasulu S (2004) Development of effective and efficient rainfall–runoff models using integration of deterministic, real-coded genetic algorithms, and artificial neural network techniques. Water Resour Res 40(4): W04302. doi:10.1029/2003WR002355

  14. Jin LZ, Marquardt RR, Zhoa X (2000) A strain of enterococcus faecium (18C23) inhibits adhesion of enterotoxigenic escherichia coli K88 to porcine small intestine mucus. Appl Environ Microbiol 66:4200–4204

    Article  Google Scholar 

  15. Keskin ME, Terzi O (2006) Artificial neural networks models of daily pan evaporation. J Hydrol Eng ASCE 11(1):65–70

    Article  Google Scholar 

  16. Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J Hydrol 351:299–317

    Article  Google Scholar 

  17. Kisi O, Yildirim G (2005) Discussion of ‘Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique’ by K.P. Sudheer; A.K. Gosain; and K.S. Ramasastri. ASCE J Irrigation Drainage Eng 131(2):219–220

    Article  Google Scholar 

  18. Knarreborg A, Engberg RM, Jensen SK, Jensen BB (2002) Quantitative determination of bile salt hydrolase activity in bacteria isolated from the small intestine of chickens. Appl Environ Microbiol 68:1120–1124

    Google Scholar 

  19. Knarreborg A, Simon MA, Engberg RM, Jensen BB, Tannock GW (2002) Effects of dietary fat source and subtherapeutic levels of antibiotic on the bacterial community in the ileum of broiler chickens at various ages. Appl Environ Microbiol 68:5918–5924

    Article  Google Scholar 

  20. Lan Y, Xun S, Tamminga S, Williams BA, Verstegem MWA, Erdi G (2004) Real-time PCR detection of lactic acid bacteria in cecal contents of Eimeria tenella-infected broilers fed soybean oligosaccharides and soluble soybean polysaccharides. Poult Sci 83:1696–1702

    Google Scholar 

  21. Landeras G, Ootiz-Barredo A, Lopez JJ (2008) Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agric Water Manag 95:553–565

    Article  Google Scholar 

  22. Lu J, Idris U, Harmon B, Hofacre C, Maurer JJ, Lee MD (2003) Diversity and succession of the intestinal bacterial community of the maturing broiler chicken. Appl Environ Microbiol 69:6816–6824

    Article  Google Scholar 

  23. Mathlouthi N, Mallet S, Saulnier L, Quemener B, Larbier M (2002) Effects of xylanase and -glucanase addition on performance, nutrient digestibility, and physico-chemical conditions in the small intestine contents and caecal microflora of broiler chickens fed a wheat and barley-based diet. J Anim Sci 51:395–406

    Google Scholar 

  24. Mead GC (2000) Prospects for ‘competitive exclusion’ treatment to control salmonellas and other foodborne pathogens in poultry. Vet J 159:111–123

    Article  Google Scholar 

  25. Ochi Y, Mitsuoka T, Sega T (1964) Nature Untersuchungen uber die Darmflora des Huhn III Mitteilung: die Entwicklung der Darmflora von Kuken bis zum Huhn. Zentralbl Bakteriol Parasitenkd Infektionskr Hyg Abt 1 Orig 193:80–95

  26. Patterson JA, Burkholder KM (2003) Application of prebiotics and probiotics in poultry production. Poult Sci 82:627–631

    Google Scholar 

  27. Riad S, Mania G, Bouchaou L, Najjar Y (2004) Rainfall-runoff model using an artificial neural network approach. Math Comput Model 40:839–846

    Article  MATH  Google Scholar 

  28. Smirnov A, Perez R, Amit-Romach E, Skllan D, Uni Z (2005) Mucin dynamics and microbial populations in chicken small intestine are changed by dietary probiotic and antibiotic growth promoter supplementation. J Nutr 135:187–192

    Google Scholar 

  29. Torok VA, Ophel-Keller K, Hughes RJ (2005) The development of molecular tools for monitoring gut microflora of poultry. Aust Poult Sci Symp 17:93–95

    Google Scholar 

  30. Van Der Wielen PW, Keuzenkamp DA, Lipman LJ, Van Knapen F, Biesterveld S (2002) Spatial and temporal variation of the intestinal bacterial community in commercially raised broiler chickens during growth. Microb Ecol 44:286–293

    Article  Google Scholar 

  31. Van Leeuwen P, Mouwen JM, Van Der Klis JD, Verstegen MW (2004) Morphology of the small intestinal mucosal surface of broilers in relation to age, diet formulation, small intestinal microflora and performance. Br Poult Sci 45:41–48

    Article  Google Scholar 

  32. Vidabarachi JK, Mikkelsen LL, Sims IM, Iji PA, Choct M (2006) Selected plant extracts modulate the gut microflora in broilers. Aust Poult Sci Symp 18:145–148

    Google Scholar 

  33. Williams BA, Verstegen MWA, Tamminga S (2001) Fermentation in the large intestine of single-stomached animals and its relationship to animal health. Nutr Res Rev 14:207–227

    Article  Google Scholar 

  34. Zoetendal EG, Akkeremans ADL, De Vos WM (1998) Temperature gradient gel electrophoresis analysis of 16S rRNA from human fecal samples reveals stable and host-specific communities of active bacteria. Appl Environ Microbiol 64:3854–3859

    Google Scholar 

Download references

Acknowledgments

Our special thanks of Bu-Ali Sina University for providing facilities and financial support of this study. We also wish to thank to the staff of the Department of Animal Science of this university for their excellent scientific collaboration.

Author information

Authors and Affiliations

  1. Faculty of Agriculture, Department of Animal Science, Bu-Ali Sina University, P Box 651783131, 65175, Hamaden, Iran

    Hamid Reza Hemati Matin, Ali Asghar Saki, Hasan Aliarabi, Mojtaba Shadmani & Hamid Zare Abyane

Authors
  1. Hamid Reza Hemati Matin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Asghar Saki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hasan Aliarabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mojtaba Shadmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hamid Zare Abyane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Asghar Saki.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hemati Matin, H.R., Saki, A.A., Aliarabi, H. et al. Intestinal broiler microflora estimation by artificial neural network. Neural Comput & Applic 21, 1043–1047 (2012). https://doi.org/10.1007/s00521-011-0553-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0553-2

Keywords

Search

Navigation