Skip to main content
Log in

Automatic characterisation of dye decolourisation in fungal strains using expert, traditional, and deep features

  • Methodologies and Application
  • Published:
Soft Computing 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

Fungi have diverse biotechnological applications in, among others, agriculture, bioenergy generation, or remediation of polluted soil and water. In this context, culture media based on colour change in response to degradation of dyes are particularly relevant, but measuring dye decolourisation of fungal strains mainly relies on a visual and semiquantitative classification of colour intensity changes. Such a classification is a subjective, time-consuming, and difficult to reproduce process. In order to deal with these problems, we have performed a systematic evaluation of different image-classification approaches considering ad hoc expert features, traditional computer vision features, and transfer-learning features obtained from deep neural networks. Our results favour the transfer learning approach reaching an accuracy of 96.5% in the evaluated dataset. In this paper, we provide the first, at least up to the best of our knowledge, method to automatically characterise dye decolourisation level of fungal strains from images of inoculated plates.

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

Access this article

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

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

References

  • Abadi M et al (2015) TensorFlow: large-scale machine learning on heterogeneous systems. http://tensorflow.org/

  • Abdel-Raheem A, Shearer CA (2002) Extracellular enzyme production by freshwater ascomycetes. Fungal Divers 11:1–19

    Google Scholar 

  • Aguilar-Pontes MW et al (2014) (Post-) genomics approaches in fungal research. Brief Funct Genomics 13(6):424–439

    Article  Google Scholar 

  • Anastasi A et al (2009) Decolourisation of model and industrial dyes by mitosporic fungi in different culture conditions. World J Microbiol Biotechnol 25(8):1363–1374

    Article  Google Scholar 

  • Andrews MY et al (2016) Digital image quantification of siderophores on agar plates. Data Brief 6:890–898

    Article  Google Scholar 

  • Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

    MATH  Google Scholar 

  • Branco P, Torgo L, Ribeiro R (2016) A survey of predictive modeling on imbalanced domains. ACM Comput Surv 49(2):31:1–31:50

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  • Casieri L et al (2010) Survey of ectomycorrhizal, litter-degrading, and wood-degrading basidiomycetes for dye decolorization and ligninolytic enzyme activity. Antonie van Leeuwenhoek 98(4):483–504

    Article  Google Scholar 

  • Chambergo FS, Valencia EY (2016) Fungal biodiversity to biotechnology. Appl Microbiol Biotechnol 100(6):2567–2577

    Article  Google Scholar 

  • Chawla NV, Bowyer KW, Hall L, Kegelmeyer W (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321–357

    Article  MATH  Google Scholar 

  • Chawla NV, Japkowicz N, Kotcz A (2004) Editorial: special issue on learning from imbalanced datasets. ACM SIGKDD Explor Newsl 6(1):1–6

    Article  Google Scholar 

  • Chollet F (2016) Xception: deep learning with depthwise separable convolutions. CoRR arXiv:1610.02357

  • Chollet F et al (2015) Keras. https://github.com/fchollet/keras

  • Christodoulidis S et al (2017) Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE J Biomed Health Inform 21(1):76–84

    Article  Google Scholar 

  • Codella N et al (2015) Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. In: Proceedings of international workshop on machine learning in medical imaging (MICCAI 2015). Lecture notes in computer science. Springer, pp 118–126

  • Coelho LP (2013) Mahotas: open source software for scriptable computer vision. J Open Res Softw 1(1):e3

    Article  MathSciNet  Google Scholar 

  • Cohen J (1969) Statistical power analysis for the behavioral sciences. Academic Press, Cambridge

    MATH  Google Scholar 

  • Cohen J (1973) Eta-squared and partial eta-squared in fixed factor anova designs. Educ Psychol Meas 33:107–112

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  • Cover T, Hart P (2006) Nearest neighbor pattern classification. IEEE Trans Inf Theor 13(1):21–27

    Article  MATH  Google Scholar 

  • Culibrk L et al (2016) Systems biology approaches for host–fungal interactions: an expanding multi-omics frontier. Omics J Integr Biol 20(3):127–138

    Article  Google Scholar 

  • Cázares-García SV et al (2016) Typing and selection of wild strains of Trichoderma spp. producers of extracellular laccase. Biotechnol Prog 32(3):787–798

    Article  Google Scholar 

  • Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol 1, CVPR ’05. IEEE Computer Society, San Diego, pp 886–893

  • Dhouib A et al (2005) Screening for ligninolytic enzyme production by diverse fungi from Tunisia. World J Microbiol Biotechnol 21(8):1415–1423

    Article  Google Scholar 

  • Gao D et al (2010) A critical review of the application of white rot fungus to environmental pollution control. Crit Rev Biotechnol 30(1):70–77

    Article  Google Scholar 

  • Garcia S et al (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180:2044–2064

    Article  Google Scholar 

  • Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3–42

    Article  MATH  Google Scholar 

  • Ghafoorian M et al (2017) Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. CoRR arXiv:1702.07841

  • Hanking L, Anagnostakis SL (1975) The use of solid media for detection of enzyme production by fungi. Mycology 67(3):597–607

    Article  Google Scholar 

  • Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC–3(6):610–621

    Article  Google Scholar 

  • He K et al (2016) Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16), IEEE Computer Society. IEEE, Las Vegas, pp 770–778

  • Holm OS (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70

    MathSciNet  MATH  Google Scholar 

  • Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17)

  • Hunter RS (1948) Photoelectric color-difference meter. J Opt Soc Am 38(7):661

    Google Scholar 

  • Hyun MW et al (2006) Detection of cellulolytic activity in Ophiostoma and Leptographium species by chromogenic reaction. Mycobiology 34(2):108–110

    Article  Google Scholar 

  • Jayasiri SC et al (2015) The faces of fungi database: fungal names linked with morphology, phylogeny and human impacts. Fungal Divers 74(1):3–18

    Article  Google Scholar 

  • Kaehler A, Bradski G (2015) Learning OpenCV 3. O’Reilly Media, Sebastopol

    Google Scholar 

  • Kameshwar AKS, Qin W (2017) Qualitative and quantitative methods for isolation and characterization of lignin-modifying enzymes secreted by microorganisms. BioEnergy Res 10(1):248–266

    Article  Google Scholar 

  • Kasana RC et al (2008) A rapid and easy method for the detection of microbial cellulases on agar plates using Gram’s iodine. Curr Microbiol 57(5):503–507

    Article  Google Scholar 

  • Kiiskinen LL et al (2004) Screening for novel laccase producing microbes. J Appl Microbiol 97:640–646

    Article  Google Scholar 

  • Korniłłowicz-Kowalska T, Rybczyńska K (2015) Screening of microscopic fungi and their enzyme activities for decolorization and biotransformation of some aromatic compounds. Int J Environ Sci Technol 12(8):2673–2686

    Article  Google Scholar 

  • Krizhevsky A et al (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., Red Hook, pp 1097–1105

    Google Scholar 

  • LeCun Y et al (2002) Neural networks: tricks of the trade. Lecture notes in computer science, vol 1524, chap. Efficient BackProp. Springer, Berlin, pp 9–50

  • Lee H et al (2014) Biotechnological procedures to select white rot fungi for the degradation of PAHs. J Microbiol Methods 97:56–62

    Article  Google Scholar 

  • Levene H (1960) Contributions to probability and statistics: essays in honor of Harold Hotelling, chap. Robust tests for equality of variances. Stanford University Press, Stanford, pp 278–292

  • McCullagh P, Nelder JA (1989) Generalized linear models. Chapman & Hall, London

    Book  MATH  Google Scholar 

  • Menegola A et al (2017) Knowledge transfer for melanoma screening with deep learning. CoRR arXiv:1703.07479

  • Mouhamadou B et al (2017) Molecular screening of xerophilic Aspergillus strains producing mycophenolic acid. Fungal Biol 121(2):103–111

    Article  Google Scholar 

  • Nyanhongo GS et al (2002) Decolorization of textile dyes by laccases from a newly isolated strain of Trametes modesta. Water Res 36:1449–1456

    Article  Google Scholar 

  • Oses R et al (2006) Evaluation of fungal endophytes for lignocellulolytic enzyme production and wood biodegradation. Int Biodeterior Biodegrad 57(2):129–135

    Article  Google Scholar 

  • Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  • Peay KG et al (2016) Dimensions of biodiversity in the Earth mycobiome. Nat Rev Microbiol 14(7):434–447

    Article  Google Scholar 

  • Pedregosa F et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  • Pedrini N et al (2009) Control of pyrethroid-resistant chagas disease vectors with entomopathogenic fungi. PLOS Negl Trop Dis 3:1–11

    Article  Google Scholar 

  • Pointing SB (1999) Qualitative methods for the determination of lignocellulolytic enzyme production by tropical fungi. Fungal Divers 2:17–33

    Google Scholar 

  • Pointing SB et al (2000) Dye decolorization by sub-tropical basidiomycetous fungi and the effect of metals on decolorizing ability. World J Microbiol Biotechnol 16(2):199–205

    Article  Google Scholar 

  • Pointing SB et al (2003) Production of wood-decay enzymes, mass loss and lignin solubilization in wood by tropical xylariaceae. Mycol Res 107(2):231–235

    Article  Google Scholar 

  • Razavian AS et al (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’14), IEEE Computer Society. IEEE, Columbus, pp 512–519

  • Rhoads DD et al (2015) A review of the current state of digital plate reading of cultures in clinical microbiology. J Pathol Inform 6(23):1–8

    Google Scholar 

  • Rodríguez-Fdez I et al (2015) STAC: a web platform for the comparison of algorithms using statistical tests. In: Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

  • Rovati JI et al (2013) Polyphenolic substrates and dyes degradation by yeasts from 25 de Mayo/King George Island (Antarctica). Yeast 30(11):459–470

    Article  Google Scholar 

  • Russakovsky O et al (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  • Schoch CL et al (2014) Finding needles in haystacks: linking scientific names, reference specimens and molecular data for fungi. Database. https://doi.org/10.1093/database/bau061

    Article  Google Scholar 

  • Sermanet P et al (2013) OverFeat: integrated recognition, localization and detection using convolutional networks. CoRR arXiv:1312.6229

  • Shapiron SS, Wilk MB (1965) An analysis for variance test for normality (complete samples). Inf Sci 180:2044–2064

    Google Scholar 

  • Sheskin D (2011) Handbook of parametric and nonparametric statistical procedures. CRC Press, London

    MATH  Google Scholar 

  • Simard P, Steinkraus D, Platt JC (2003) Best practices for convolutional neural networks applied to visual document analysis. In: I.C. Society (ed) Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR’03), vol. 2, pp 958–964

  • Simonis JL et al (2008) Extracellular enzymes and soft rot decay: are ascomycetes important degraders in fresh water? Fungal Divers 31(1):135–146

    Google Scholar 

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR arXiv:1409.1556

  • Sørensen A et al (2011) Onsite enzyme production during bioethanol production from biomass: screening for suitable fungal strains. Appl Biochem Biotechnol 164(7):1058–1070

    Article  Google Scholar 

  • Szegedy C et al (2014) DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14), IEEE Computer Society. IEEE, pp 1701–1708

  • Szegedy C et al (2015a) Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15), IEEE Computer Society. IEEE, Boston, pp 1–9

  • Szegedy C et al (2015b) Rethinking the inception architecture for computer vision. CoRR arXiv:1512.00567

  • Szekeres A et al (2006) A novel, image analysis-based method for the evaluation of in vitro antagonism. J Microbiol Methods 65(3):619–622

    Article  Google Scholar 

  • Szeliski R (2010) Computer vision: algorithms and applications. Springer, London

    MATH  Google Scholar 

  • Tortella GR et al (2008) Enzymatic characterization of Chilean native wood-rotting fungi for potential use in the bioremediation of polluted environments with chlorophenols. World J Microbiol Biotechnol 24(12):2805

    Article  Google Scholar 

  • Wolpert DH (1996) The lack of a priori distinction between learning algorithms. Neural Comput 8(7):1341–1390

    Article  Google Scholar 

  • Xu C et al (2015) Screening of ligninolytic fungi for biological pretreatment of lignocellulosic biomass. Can J Microbiol 61(10):745–752

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the Ministerio de Economía y Competitividad [MTM2014-54151-P, MTM2017-88804-P], and Agencia de Desarrollo Económico de La Rioja [2017-I-IDD-00018].

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jónathan Heras or Gerardo Vázquez-Marrufo.

Ethics declarations

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arredondo-Santoyo, M., Domínguez, C., Heras, J. et al. Automatic characterisation of dye decolourisation in fungal strains using expert, traditional, and deep features. Soft Comput 23, 12799–12812 (2019). https://doi.org/10.1007/s00500-019-03832-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-03832-8

Keywords