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Research and application of biological potency soft sensor modeling method in the industrial fed-batch chlortetracycline fermentation process

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Abstract

The potency of fermentation broth is one of the key parameters reflect the yield and quality of fermentation products of chlortetracycline (CTC). But so far, there is no instrument available on-line detection of CTC potency. All the tests are done by manual off-line testing, it takes several hours to sample and analyze the results from a fermentation tank (the production process usually has dozens of small, large fermenters). The use of analytical results to control the amount of operation will lead to severe lag. This paper combines self-organizing feature map (SOM) neural network with accurate classification of data and least squares support vector machine (LSSVM) algorithm with strong described the nonlinear characteristics. The SOM–LSSVM global modeling method of forecasting CTC fermentation potency is establish in this paper. According to the characteristics of nonlinear CTC fermentation process, just-in-time learning-recursive least squares support vector regression (JITL–RLSSVR) is used to perform local real-time modeling and 10-folding cross validation, and a hybrid soft sensor modeling method (JITL–RLSSVR + SOM–LSSVM) for online prediction of CTC fermentation potency is proposed in this paper. Field experiments show that this method can obtain more accurate potency prediction value, and it can meet the requirements of the production process.

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References

  1. Fu, J.: The Balancing Process Optimization and Metabolic Parameters Related to the Calculation of Chlortetracycline Fermentation Process. East China University of Science and Technology, Shanghai (2003) (Chinese)

  2. Sarmah, A.K., Meyer, M.T., Boxall, A.: A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment. Chemosphere 65(5), 725–759 (2006)

    Article  Google Scholar 

  3. Shi, Z., Feng, P.: Fermentation Process Analysis, Control and Measurement Technology, pp. 1–2. Chemical Industry Press, Beijing (2010) (Chinese)

  4. Cinar, A., Parulekar, S.J., Undey, C., et al.: Batch Fermentation: Modeling: Monitoring, and Control. CRC Press, New York (2003)

    Google Scholar 

  5. Nomikos, P., MacGregor, J.F.: Multivariate SPC charts for monitoring batch processes. Technometrics 37(1), 41–59 (1995)

    Article  Google Scholar 

  6. Li, J., Bao, J., Chen, Y.: Assay of the fermentation concentration of active substance in the fermentation broth. J. Hebei Acad. Sci. 22(1), 52–53, 57 (2005) (Chinese)

  7. Xu, Y., Peng, W., Wang, D., et al.: Determination of lipstatin concentration in fermentation broth by RP-HPLC. J. Shenyang Pharm. Univ. 30(1), 31–34 (2013) (Chinese)

  8. Li, H., Liu, L., Guan, L.: Rapid determination of streptomycin in broth by spectrophotometry. Sci. Technol. Eng. 13(20), 6011–6014 (2013) (Chinese)

  9. Zhang, Y., Ni, Z., Liu, L., et al.: Rapid detection of bacitracin in fermentation broth by spectrophotometry. China Brew. (4), 171–172 (2011) (Chinese)

  10. Chen, L.: Study on the fermentation process technology of chlortetracycline. Strait Pharm. J. 22(6), 23–25 (2010) (Chinese)

  11. Wang, P.: Study on the Breeding and Fermentation Process Optimization of CTC-Producing Strains. Inner Mongolia University, Hohhot (2013) (Chinese)

  12. He, J., Ruan, C., Tu, X., et al.: Study on determination of Honggumycin potency in fermentation by biological method. Sci. Technol. Food Ind. 32(11), 411–442 (2011) (Chinese)

  13. Gao, F., Chen, T., Gou, L., et al.: Effect of Vitreoscilla hemoglobin gene (vgb) expression on growth and metabolism of Streptomyces venezuelae var. qinlingensis and its effect on Zuelacmycin valence. J. Agric. Biotechnol. 18(6), 1182–1188 (2010) (Chinese)

  14. Liao, A., Chen, K., Zhang, H.: Microbiological assay of gentamicin in broth with paper discuss method. Pharm. Biotechnol. 11(3), 187–189 (2004) (Chinese)

  15. Gao, R., Zhao, Y.: A novel approach for accurate estimating the potency of antibiotics in fermentative fluid. J. Shandong Univ. 40(6), 112–116 (2005) (Chinese)

  16. Tedeschi, L.O., Kononoff, P.J., Karges, K., et al.: Effects of chemical composition variation on the dynamics of ruminal fermentation and biological value of corn milling (co)products. J. Dairy Sci. 92(1), 401–413 (2009)

    Article  Google Scholar 

  17. Abecia-Soria, L., Pezoa-García, N.H., Amaya-Farfan, J.: Soluble albumin and biological value of protein in cocoa (Theobroma cacao L.) beans as a function of roasting time. J. Food Sci. 70(4), 294–298 (2005)

    Article  Google Scholar 

  18. Han, J.C., Chen, G.H., Zhang, J.L., et al.: Relative biological value of 1\(\alpha \)-hydroxycholecalciferol to 25-hydroxycholecalciferol in broiler chicken diets. Poult. Sci. 96(7), 2330–2335 (2017)

    Article  Google Scholar 

  19. Mistry, B., Patel, R.V., Keum, Y.-S., et al.: Evaluation of the biological potencies of newly synthesized berberine derivatives bearing benzothiazole moieties with substituted functionalities[J]. J. Saudi Chem. Soc. 21(2), 210–219 (2017)

    Article  Google Scholar 

  20. Jatkauskas, J., Vrotniakiene, V.: The influence of application of a biological additive on the fermentation and nutritive value of Lucerne silage. Zemdirb. Agric. 96(4), 197–208 (2009)

    Google Scholar 

  21. Tu, Q., Zhao, H., Chen, J.: Optimization of medium for biological potency of fermentation supernatant of Paenibacillus brasilensis YS-1. Minerva. Biotecnol. 27(2), 93–98 (2015)

    Google Scholar 

  22. Chen, B., Li, R., Guo, Y., et al.: Purification and preparation of moenomycin A from fermentation broth by multidimensional chromatography. Chromatographia 79(11—-12), 667–674 (2016)

    Article  Google Scholar 

  23. Kobayashi, M., Kanasaki, R., Sato, I., et al.: FR207944, an antifungal antibiotic from Chaetomium sp. no. 217—I. Taxonomy, fermentation, and biological properties. Biosci. Biotechnol. Biochem. 69(3), 515–521 (2005)

    Article  Google Scholar 

  24. Wan, F., Feng, F., Huang, Y., et al.: Development of a titer measuring system based on light emitting diodes. Mod. Instrum. Med. Treat. 22(1), 13–15 (2016) (Chinese)

  25. He, K., Zhao, L., Wang, J., et al.: Soft-sensor modeling method in a fermentation process based on the samples of a sparse Gaussian process. J. Beijing Univ. Chem. Technol. (Nat. Sci.) 41(3), 108–113 (2014) (Chinese)

  26. Yu, T., Wang, J., He, K., et al.: Staged soft-sensor modeling method for fermentation process based on MPCA-GP. Chin. J. Sci. Instrum. 34(12), 2703–2708 (2013) (Chinese)

  27. Tang, H.S., Xue, S.T., Chen, R., et al.: Online weighted LS-SVM for hysteretic structural system V identification. Eng. Struct. 28(12), 1728–1735 (2006)

    Article  Google Scholar 

  28. Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)

    Article  Google Scholar 

  29. Ismail, S., Shabri, A., Samsudin, R.: A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting. Expert Syst. Appl. 38(8), 10574–10578 (2011)

    Article  Google Scholar 

  30. Kalteh, A.M., Hjorth, P., Berndtsson, R.: Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application. Environ. Model. Softw. 23(7), 835–845 (2008)

    Article  Google Scholar 

  31. Mendes, E.M.A.M., Billings, S.A.: An alternative solution to the model structure selection problem. IEEE Trans. Syst. Man Cybern. A 31(6), 597–608 (2001)

    Article  Google Scholar 

  32. Yan, W., Shao, H., Wang, X.: Soft sensing modeling based on support vector machine and Bayesian model selection. Comput. Chem. Eng. 28(8), 1489–1498 (2004)

    Article  Google Scholar 

  33. Liu, G., Zhou, D., Xu, H., et al.: Model optimization of SVM for a fermentation soft sensor. Expert Syst. Appl. 37(4), 2708–2713 (2010)

    Article  Google Scholar 

  34. Okada, T., Kaneko, H., Funatsu, K.: Development of a model selection method based on the reliability of a soft sensor model. Sonklanakarin J. Sci. Technol. 34(2), 217 (2010)

    Google Scholar 

  35. Smith, R.S., Doyle, J.C.: Model validation: a connection between robust control and identification. IEEE Trans. Autom. Control 37(7), 942–952 (1992)

    Article  MathSciNet  Google Scholar 

  36. Lindgren, F., Hansen, B., Karcher, W., et al.: Model validation by permutation tests: applications to variable selection. J. Chemom. 10(5–6), 521–532 (1996)

    Article  Google Scholar 

  37. Billings, S.A., Zhu, Q.M.: Nonlinear model validation using correlation tests. Int. J. Control 60(6), 1107–1120 (1994)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work is financially supported by Natural Science Foundation (No. ZR2016FM28) of Shandong Province in 2016. We also thank Charoen Pokphand Group for their financial support and for providing the industrial datasets offed-batch CTC fermentation process.

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Authors and Affiliations

  1. College of Electronic Engineering, Yantai Nanshan University, Longkou, 265713, Shandong, People’s Republic of China

    Yu-mei Sun & Qiao-yan Sun

  2. School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 100081, People’s Republic of China

    Ni Du, Xiang-guang Chen & Jian-wen Yang

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  1. Yu-mei Sun
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Correspondence to Xiang-guang Chen.

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Sun, Ym., Du, N., Sun, Qy. et al. Research and application of biological potency soft sensor modeling method in the industrial fed-batch chlortetracycline fermentation process. Cluster Comput 22 (Suppl 3), 6019–6030 (2019). https://doi.org/10.1007/s10586-018-1790-2

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