Clavulanic acid production estimation based on color and structural features of streptomyces clavuligerus bacteria using self-organizing map and genetic algorithm

The utilization of antibiotics produced by Clavulanic acid (CA) is an increasing need in medicine and industry. Usually, the CA is created from the fermentation of Streptomycen Clavuligerus (SC) bacteria. Analysis of visual and morphological features of SC bacteria is an appropriate measure to estimate the growth of CA. In this paper, an automatic and fast CA production level estimation algorithm based on visual and structural features of SC bacteria instead of statistical methods and experimental evaluation by microbiologist is proposed. In this algorithm, structural features such as the number of newborn branches, thickness of hyphal and bacterial density and also color features such as acceptance color levels are extracted from the SC bacteria. Moreover, PH and biomass of the medium provided by microbiologists are considered as specified features. The level of CA production is estimated by using a new application of Self-Organizing Map (SOM), and a hybrid model of genetic algorithm with back propagation network (GA-BPN). The proposed algorithm is evaluated on four carbonic resources including malt, starch, wheat flour and glycerol that had used as different mediums of bacterial growth. Then, the obtained results are compared and evaluated with observation of specialist. Finally, the Relative Error (RE) for the SOM and GA-BPN are achieved 14.97% and 16.63%, respectively.

[1]  L. K. Siu,et al.  Antibiotics: action and resistance in gram-negative bacteria. , 2002, Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi.

[2]  L. Miller,et al.  A review of the antimicrobial activity of clavulanate. , 2003, The Journal of antimicrobial chemotherapy.

[3]  S. Essack,et al.  The development of beta-lactam antibiotics in response to the evolution of beta-lactamases. , 2001, Pharmaceutical research.

[4]  Hossein Pourghassem,et al.  Content-based medical image classification using a new hierarchical merging scheme , 2008, Comput. Medical Imaging Graph..

[5]  Hujun Yin,et al.  The Self-Organizing Maps: Background, Theories, Extensions and Applications , 2008, Computational Intelligence: A Compendium.

[6]  J. Menezes,et al.  Morphology and viability analysis of Streptomyces clavuligerus in industrial cultivation systems , 2004, Bioprocess and biosystems engineering.

[7]  M. N. Pons,et al.  Morphology and viability analysis of Streptomyces clavuligerus in industrial cultivation systems , 2004 .

[8]  P. Cox,et al.  Image analysis of the morphology of filamentous micro-organisms. , 1998, Microbiology.

[9]  J. Menezes,et al.  Effects of preculture variability on clavulanic acid fermentation. , 2001, Biotechnology and bioengineering.

[10]  Matjaz Kukar,et al.  Image processing and machine learning for fully automated probabilistic evaluation of medical images , 2011, Comput. Methods Programs Biomed..

[11]  S. Essack,et al.  The Development of β-Lactam Antibiotics in Response to the Evolution of β-Lactamases , 2001, Pharmaceutical Research.

[12]  Dmitriy Shutin,et al.  Application of information-theoretic measures to quantitative analysis of immunofluorescent microscope imaging , 2010, Comput. Methods Programs Biomed..

[13]  Benoit Boulet,et al.  Estimating postprandial glucose fluxes using hierarchical Bayes modelling , 2012, Comput. Methods Programs Biomed..

[14]  G. Woodnutt,et al.  Augmentin (amoxicillin/clavulanate) in the treatment of community-acquired respiratory tract infection: a review of the continuing development of an innovative antimicrobial agent. , 2004, The Journal of antimicrobial chemotherapy.

[15]  Siu Lk.  Antibiotics: action and resistance in gram-negative bacteria. , 2002 .

[16]  K. Bush,et al.  Beta-lactamase inhibitors from laboratory to clinic , 1988, Clinical Microbiology Reviews.

[17]  Emilio Soria-Olivas,et al.  Visual data mining with self-organising maps for ventricular fibrillation analysis , 2013, Comput. Methods Programs Biomed..

[18]  Hossein Pourghassem,et al.  Using structural and color features of streptomyces clavuligerus bacteria in approximating of production level of clavulanic acid , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[19]  Artur Klepaczko,et al.  Texture and color based image segmentation and pathology detection in capsule endoscopy videos , 2014, Comput. Methods Programs Biomed..

[20]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[21]  Svante Wold,et al.  Analysis of variance (ANOVA) , 1989 .

[22]  W. C. Guenther,et al.  Analysis of variance , 1968, The Mathematical Gazette.

[23]  Li Xiaojuan,et al.  A novel bacteria classification scheme based on microscopic image analysis , 2007 .

[24]  K. Okamoto,et al.  Target Affinities of Faropenem to and Its Impact on the Morphology of Gram-Positive and Gram-Negative Bacteria , 2003, Chemotherapy.

[25]  Yeuvo Jphonen,et al.  Self-Organizing Maps , 1995 .

[26]  A. L. Demain,et al.  Involvement of aeration in carbon source regulation of cephem antibiotic biosynthesis inStreptomyces clavuligerus , 2005, Biotechnology Letters.

[27]  Jo Campling,et al.  Analysis of Variance (ANOVA) , 2002 .

[28]  Te-Sheng Li,et al.  FEATURE SELECTION FOR CLASSIFICATION BY USING A GA-BASED NEURAL NETWORK APPROACH , 2006 .

[29]  Ming-Yih Lee,et al.  Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images , 2010, Comput. Methods Programs Biomed..