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High-throughput screening of high Monascus pigment-producing strain based on digital image processing

  • Biotechnology Methods
  • Published:
Journal of Industrial Microbiology & Biotechnology

Abstract

This work proposed a new method which applied image processing and support vector machine (SVM) for screening of mold strains. Taking Monascus as example, morphological characteristics of Monascus colony were quantified by image processing. And the association between the characteristics and pigment production capability was determined by SVM. On this basis, a highly automated screening strategy was achieved. The accuracy of the proposed strategy is 80.6 %, which is compatible with the existing methods (81.1 % for microplate and 85.4 % for flask). Meanwhile, the screening of 500 colonies only takes 20–30 min, which is the highest rate among all published results. By applying this automated method, 13 strains with high-predicted production were obtained and the best one produced as 2.8-fold (226 U/mL) of pigment and 1.9-fold (51 mg/L) of lovastatin compared with the parent strain. The current study provides us with an effective and promising method for strain improvement.

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Acknowledgments

This work was financially supported by Open Funding Project of the National Key Laboratory of Biochemical Engineering (No. 2013KF-01), and the National High Technology Research and Development Program (863 Program, 2012AA021302).

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Authors and Affiliations

  1. State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China

    Meng-lei Xia, Lan Wang & Hong-zhang Chen

  2. University of Chinese Academy of Sciences, Beijing, 100039, China

    Meng-lei Xia

  3. College of Mathematics and System Science, Xinjiang University, Urumchi, 830046, China

    Zhi-xia Yang

Authors
  1. Meng-lei Xia
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  2. Lan Wang
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  4. Hong-zhang Chen
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Correspondence to Hong-zhang Chen.

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Xia, Ml., Wang, L., Yang, Zx. et al. High-throughput screening of high Monascus pigment-producing strain based on digital image processing. J Ind Microbiol Biotechnol 43, 451–461 (2016). https://doi.org/10.1007/s10295-015-1729-z

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  • DOI: https://doi.org/10.1007/s10295-015-1729-z

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