Evolutionary algorithm in adaptive neuro-fuzzy inference system for modeling growth of foodborne fungi

Abstract

The formation of mycotoxins and potentially allergenic spores associated with fungal growth can cause spoilage of food and animal feed. This study integrated an improved genetic algorithm (IGA) in an adaptive neuro-fuzzy inference system (ANFIS) for predicting the presence of foodborne fungi and modeling their growth. The IGA enhanced the performance of the ANFIS model in predictive microbiology. Based on temperature, pH, and water quantity, the proposed IGA-ANFIS model can accurately predict the maximum specific growth rate of the ascomycetous fungus Monascus ruber. The model uses Gaussian membership functions to minimize the root-mean-square error, which was used as a performance index. Experiments verified that the prediction accuracy of the proposed IGA-ANFIS model is higher than those of existing neural network models and neural fuzzy network models.

Keywords

Adaptive neuro-fuzzy inference system genetic algorithm food mycology

Get full access to this article

View all access options for this article.

References

Jang

J.S.R.

, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics 23 (1993), 665–685.

Jang

J.S.R.

, Sun

C.T.

, Mizutani

Neuro-Fuzzy and Soft Computing, Pearson Education, Taipei, 2004.

Kakar

, Nystrom

, Aarup

L.R.

, Nøttrup

T.J.

D.R.

Olsen

, Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS), Physics in Medicine and Biology 50 (2005), 4721–4728.

Keskin

M.E.

, Taylan

and Terzi

Ö.

, Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series, Hydrological Sciences Journal 51 (2006), 588–598.

Mousavi

S.J.

, Ponnambalam

and Karray

, Inferring operating rules for reservoir operations using fuzzy regression and ANFIS, Fuzzy Sets and Systems 158 (2007), 1064–1082.

Buragohain

and Mahanta

, A novel approach for ANFIS modelling based on full factorial design, Applied Soft Computing 8 (2008), 609–625.

Caydaş

Hasçalik

Ekici

, An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM, Expert Systems with Applications 36 (2009), 6135–6139.

Boyacioglu

M.A.

and Avci

, An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange, Expert Systems with Applications 37 (2010), 7908–7912.

Yilmaz

and Kaynar

, Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils, Expert Systems with Applications 38 (2011), 5958–5966.

10.

Singh

, Kainthola

and Singh

T.N.

, Estimation of elastic constant of rocks using an ANFIS approach, Applied Soft Computing 12 (2012), 40–45.

11.

Abu-Rub

, Iqbal

, Ahmed

S.M.

, Peng

F.Z.

, Li

and Baoming

, Quasi-z-source inverter-based photovoltaic generation system with maximum power tracking control using ANFIS, IEEE Transactions on Sustainable Energy 4 (2013), 11–20.

12.

Prakasha

and Kumar

, ANFIS modelling of a natural convection greenhouse drying system for jaggery: An experimental validation, International Journal of Sustainable Energy 33 (2014), 316–335.

13.

Osórioa

G.J.

, Matiasa

J.C.O.

and Catalão

J.P.S.

, Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information, Renewable Energy 75 (2015), 301–307.

14.

Choubin

, Khalighi-Sigaroodi

, Malekian

and Kisi

, Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals, Hydrological Sciences Journal 61 (2016), 1001–1009.

15.

Xie

, Ni

and Su

, A prediction model of ammonia emission from a fattening pig room based on the indoor concentration using adaptive neuro fuzzy inference system, Journal of Hazardous Materials 325 (2017), 301–309.

16.

Tsai

J.T.

, Liu

T.K.

and Chou

J.H.

, Hybrid Taguchi-genetic algorithm for global numerical optimization, IEEE Trans on Evolutionary Computation 8 (2004), 365–377.

17.

Tsai

J.T.

, Chou

J.H.

and Liu

T.K.

, Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm, IEEE Trans on Neural Networks 17 (2006), 69–80.

18.

W.H.

, Chen

J.X.

, Lee

I.N.

and Su

H.C.

, An ANFIS-based model for predicting adequacy of vancomycin regimen using improved genetic algorithm, Expert Systems with Applications 38 (2011), 13050–13056.

19.

Panagou

E.Z.

, Skandamis

P.N.

and Nychas

G.J.E.

, Use of gradient plates to study combined effects of temperature, pH, and NaCl concentration on growth of Monascus ruber van Tieghem, an ascomycetes fungus isolated from green table olives, Applied and Environmental Microbiology 71 (2005), 392–399.

20.

Panagou

E.Z.

, Skandamis

P.N.

and Nychas

G.J.E.

, Modelling the combined effect of temperature, pH and aw on the growth rate of Monascus ruber, a heat-resistant fungus isolated from green table olives, Journal of Applied Microbiology 94 (2003), 146–156.

21.

Ross

and McMeekin

T.A.

, Review paper: Predictive microbiology, Int J of Food Microbiology 23 (1994), 241–264.

22.

Murphy

P.A.

, Hendrich

, Landgren

and Bryant

C.M.

, Food mycotoxins: An update, Journal of Food Science 71 (2006), 51–65.

23.

Dantigny

, Guilmart

and Bensoussan

, Basis of predictive mycology, Int J of Food Microbiology 100 (2005), 187–196.

24.

Panagou

E.Z.

, Kodogiannis

and Nychas

G.J.E.

, Modelling fungal growth using radial basis function neural networks: The case of the ascomycetous fungus Monascus ruber van Tieghem, International Journal of Food Microbiology 117 (2007), 276–286.

25.

Panagou

E.Z.

and Kodogiannis

V.S.

, Application of neural networks as a non-linear modelling technique in food mycology, Expert Systems with Applications 36 (2009), 121–131.

26.

Hajjaj

, Blanc

P.J.

, Groussac

, Goma

, Uribelarrea

J.L.

and Loubiere

, Improvement of red pigment/citrinin production ratio as a function of environmental conditions by Monascus ruber, Biotechnology and Bioengineering 64 (1999), 497–501.

27.

Martinkova

, Patakova

, Encyclopedia of Food Microbiology, Academic Press, London, 1999.

28.

Hajjaj

, Blanc

, Groussac

, Uribelarrea

J.L.

, Goma

and Loubiere

, Kinetic analysis of red pigment and citrinin production by Monascus ruber as a function of organic acid accumulation, Enzyme and Microbial Technology 27 (2000), 619–625.

29.

Hamano

P.S.

, Orozco

S.F.B.

and Kilikian

B.V.

, Concentration determination of extracellular and intracellular red pigments produced by Monascus sp, Brazilian Archives of Biology and Technology 48 (2005), 43–49.

30.

Wang

Y.Z.

, Ju

X.L.

and Zhou

Y.G.

, The variability of citrinin production in Monascus type cultures, Food microbiology 22 (2005), 145–148.

31.

Hamano

P.S.

and Kilikian

B.V.

, Production of red pigments by Monascus ruber in culture media containing corn steep liquor, Brazilian Journal of Chemical Engineering 23 (2006), 443–449.

32.

Silveira

S.T.

, Daroit

D.J.

and Brandelli

, Pigment production by Monascus purpureus in grape waste using factorial design, Food Science and Technology 41 (2008), 170–174.

33.

Lian

, Wang

and Guo

, Identification of new red pigments produced by Monascus ruber, Dyes and Pigments 73 (2007), 121–125.

34.

Yang

Y.J.

and Lee

, Agrobactrium tumefaciens-mediated transformation of Monascus ruber, Journal of Microbiology and Biotechnology 18 (2008), 754–758.

35.

Shao

, Ding

, Zhao

, Yang

, Xie

and Chen

, Characteristic analysis of transformants in T-DNA mutation library of Monascus ruber, World Journal of Microbiology and Biotechnology 25 (2009), 989–995.

36.

Tsukahara

, Shinzato

, Tamaki

, Namihira

and Matsui

, Red yeast rice fermentation by selected Monascus sp. with deep-red color, lovastatin production but no citrinin, and effect of temperature-shift cultivation on lovastatin production, Applied Biochemistry and Biotechnology 158 (2009), 476–482.

37.

, Shao

, Li

, Yang

and Chen

, Identification of Mga1, a G-protein α-subunit gene involved in regulating citrinin and pigment production in Monascus ruber M7, FEMS Microbiology Letters 308 (2010), 108–114.

38.

Panda

B.P.

, Javed

and Ali

, Optimization of fermentation parameters for higher lovastatin production in red mold rice through co-culture of Monascus purpureus and Monascus ruber, Food and Bioprocess Technology 3 (2010), 373–378.

39.

Lai

, Wang

, Qing

and Chen

, Effects of cyclic AMP on development and secondary metabolites of Monascus ruber M-7, Letters in Applied Microbiology 52 (2011), 420–426.

40.

Meinicke

R.M.

, Vendruscolo

, Moritz

D.E.

, de Oliveira

Schmidell

R.W.

Samohyl

J.L.

Ninow

Potential use of glycerol as substrate for the production of red pigments by Monascus ruber in submerged fermentation, Biocatalysis and Agricultural Biotechnology 1 (2012), 238–242.

41.

Yang

, Li

, Shao

and Chen

, mrflbA, encoding a putative FlbA, is involved in aerial hyphal development and secondary metabolite production in Monascus ruber M-7, Fungal Biology 116 (2012), 225–233.

42.

Xie

, Liu

and Chen

, Deletion of pigR gene in Monascus ruber leads to loss of pigment production, Biotechnology Letters 35 (2013), 1425–1432.

43.

Mapari

S.A.S.

, Nielsen

K.F.

and Larssen

T.O.

, Exploring fungal biodiversity for the production of water-soluble pigments as potential natural food colorants, Current Opinion in Biotechnology 16 (2005), 231–238.

44.

W.H.

, Tsai

J.T.

and Wang

H.Y.

, Neural fuzzy network model with evolutionary learning algorithm for mycological study of foodborne fungi, Int J of Innovative Computing, Information and Control 8 (2012), 4565–4577.