Degradation Kinetics of Basic violet 3 by Staphylococcus aureus

Authors

  • Motharasan Manogaran Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, UPM 43400 Serdang, Selangor, Malaysia.
  • Aa'ishah Abd Gafar Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, UPM 43400 Serdang, Selangor, Malaysia.
  • Aisami Abubakar Department of Biochemistry, Faculty of science, Gombe State University, P.M.B 127, Tudun Wada, Gombe, Gombe State, Nigeria.

DOI:

https://doi.org/10.54987/ajpb.v4i2.788

Keywords:

Basic Violet 3, Triphenylmethane dye, Decoulorization, Staphylococcus aureus, Baranyi-Roberts model

Abstract

Synthetic dyes are abundantly used in recent years and mainly consumed in the textile, pharmaceutical, plastic and cosmetic industries. The release of toxic constituents from dyes give adverse effect on human health and marine life. In textile industries, large amount of dye discharged into the wastewater and eventually to the aquatic system are mainly came from the critical step of dyeing and finishing processes in textile. The be able to precisely forecast the rate of bioremediation, depends on the gathering of precise rate of decolourisation, and this can be inaccurately acquired by natural logarithm transformation of the decolourisation process over time. In cases like this, a nonlinear regression of the curve must be performed making use of accessible rate models. Consequently, numerous primary models for example modified Logistic, modified Gompertz, modified logistics, modified Richards, modified Schnute, Baranyi-Roberts, Buchanan-3-phase, von Bertalanffy and the Huang models were utilized to fit the specific decolourisation rate. Several models did not converge and was disregarded and only Huang, Baranyi-Roberts, modified Gompertz, modified Richards and modified Logistics could actually model the data. The very best model according to statistical analysis was Baranyi Roberts with the highest value for Adjusted Coefficient of Determination and the lowest values for RMSE, AICc, HQC and BIC and the closest value to 1.0 for accuracy and bias factors. The Baranyi-Roberts fitted curve was discovered to conform to normality tests and is satisfactory to be used to fit the experimental data. The parameters extracted from this exercise may be used for additional secondary modelling training to gleam information about how substrate (dye) impact the rate of decolourisation of the substrate

References

Sandhiya R, Begum KS, Charumathi D. Decolourization of triphenylmethane dyes and dye industry effluent by Staphylococcus aureus isolated from dye contaminated site. Int J Pharm Pharm Sci. 2016;8(9):258-66.

Yang Y, Jung DW, Bai DG, Yoo GS, Choi JK. Counterion-dye staining method for DNA in agarose gels using crystal violet and methyl orange. Electrophoresis. 2001 Mar;22(5):855-9.

Barbolini G, Pessina AC. Non-specificity of crystal violet staining for renin granules. Acta Histochem. 1977;58(2):191-3.

Cho B, Yang T, Bankenship L, Moody J, Churchwell M, Bebland F, et al. Synthesis and characterization of N-demethylated metabolites of malachite green and leuco malachite green. Chem Res Toxicol. 2003;16:285-94.

Littlefield N, Blackwell B, Hewitt C, Gaylor D. Chronic toxicity and carcinogenicity studies of gentian violet in mice. Fundam Appl Toxicol. 1985;(5):902-12.

Decampo R, Moreno S. The metabolism and mode of action of gentian violet, drug. Metab Rev. 1990;22:161-78.

Deivasigamani C, Das N. Biodegradation of Basic Violet 3 by Candida krusei isolated from textile wastewater. Biodegradation. 2011;22(6):1169-80.

López S, Prieto M, Dijkstra J, Dhanoa MS, France J. Statistical evaluation of mathematical models for microbial growth. Int J Food Microbiol. 2004;96(3):289-300.

Sandhiya R, Begum KS, Charumathi D. Decolourization of triphenylmethane dyes and dye industry effluent by Staphylococcus aureus isolated from dye contaminated site. Int J Pharm Pharm Sci. 2016 Sep 1;258-66.

Rohatgi A. WebPlotDigitizer-Extract Data from Plots, Images, and Maps [Internet]. 2018 [cited 2019 Jan 1]. Available from: http://arohatgi. info/WebPlotDigitizer

Halmi MIE, Shukor MS, Johari WLW, Shukor MY. Mathematical modelling of the degradation kinetics of Bacillus cereus grown on phenol. J Environ Bioremediation Toxicol. 2014 Sep 29;2(1):1-5.

Manogaran M, Yasid NA, Ahmad SA. Mathematical modelling of glyphosate degradation rate by Bacillus subtilis. J Biochem Microbiol Biotechnol. 2017 Jul 31;5(1):21-5.

Motulsky HJ, Ransnas LA. Fitting curves to data using nonlinear regression: a practical and nonmathematical review. FASEB J Off Publ Fed Am Soc Exp Biol. 1987 Nov;1(5):365-74.

Halmi, MIE, ,Shukor MS, Johari W.L.W WLW, Shukor MY. Mathematical Modeling of the Growth Kinetics of Bacillus sp . on Tannery Effluent Containing Chromate. J Environ Bioremediation Toxicol. 2014;2(1):6-10.

Akaike H. Factor analysis and AIC. Psychometrika. 1987;52(3):317-32.

Motulsky HJ, Ransnas LA. Fitting curves to data using nonlinear regression: a practical and nonmathematical review. FASEB J Off Publ Fed Am Soc Exp Biol. 1987;1(5):365-74.

Ross T, McMeekin TA. Predictive microbiology. Int J Food Microbiol. 1994;23(3-4):241-64.

Halmi MIE, Shukor MS, Masdor NA, Shamaan NA, Shukor MY. Testing the normality of residuals on regression model for the growth of Paracoccus sp. SKG on acetonitrile. J Environ Bioremediation Toxicol. 2015;3(1):15-7.

Halmi MIE, Shukor MS, Masdor NA, Shamaan NA, Shukor MY. Test for the presence of autocorrelation in the modified gompertz model used in the fitting the growth of sludge microbes on PEG 600. J Environ Microbiol Toxicol. 2015;3(1):6-8.

Yu L, Cao M yue, Wang P tao, Wang S, Yue Y rong, Yuan W duo, et al. Simultaneous decolorization and biohydrogen production from xylose by Klebsiella oxytoca GS-4-08 in the presence of azo dyes with sulfonate and carboxyl groups. Appl Environ Microbiol. 2017 May 15;83(10):e00508-17.

Wang Z, Yin Q, Gu M, He K, Wu G. Enhanced azo dye Reactive Red 2 degradation in anaerobic reactors by dosing conductive material of ferroferric oxide. J Hazard Mater. 2018 Sep 5;357:226-34.

Baranyi J, Roberts TA. Mathematics of predictive food microbiology. Int J Food Microbiol. 1995 Jul 1;26(2):199-218.

Yoon Y, Park JN, Sohn HS, Song BS, Kim JH, Byun MW, et al. Modeling the effect of ?-irradiation on reducing total bacterial populations in Gochujang intended for consumption by astronauts in space programs. Food Sci Biotechnol. 2011;20(2):377-82.

Yahuza S, Dan-Iya BI, Sabo IA. Modelling the Growth of Enterobacter sp. on Polyethylene. J Biochem Microbiol Biotechnol. 2020 Jul 31;8(1):42-6.

Ibrahim S, Abdulrasheed M, Ibrahim H, Abubakar A, Yakasai HM. Mathematical Modelling of the Growth of Yeast Candida tropicalis TL-F1 on Azo Dyes. J Biochem Microbiol Biotechnol. 2021 Jul 30;9(1):43-7.

Abubakar A, Ibrahim S, Abba M. Mathematical Modelling of Azo Blue Dye Degradation by Streptomyces DJP15. Bull Environ Sci Sustain Manag E-ISSN 2716-5353. 2021 Jul 31;5(1):27-31.

Sabo IA, Yahuza S, Shukor MY. Mathematical Modeling of the growth of Acinetobacter baumannii YNWH 226 on Azo dye Congo red. J Environ Bioremediation Toxicol. 2021 Dec 31;4(2):7-10.

Yahuza S, Sabo IA. Mathematical Modelling of the Growth of Bacillus cereus Strain wwcp1on Malachite Green Dye. J Biochem Microbiol Biotechnol. 2021 Dec 31;9(2):25-9.

Kolmogorov A. Sulla determinazione empirica di una legge di distribuzione. G Dell' Ist Ital Degli Attuari. 1933;4:83-91.

Smirnov N. Table for estimating the goodness of fit of empirical distributions. Ann Math Stat. 1948;19:279-81.

Royston P. Wilks-Shapiro algorithm. Appl Stat. 1995;44(4):R94.

D'Agostino RB. Tests for Normal Distribution. In: D'Agostino RB, Stephens MA, editors. Goodness-Of-Fit Techniques. Marcel Dekker; 1986.

Nee CW, Aziz AAA, Salvamani S, Shaharuddin NA, Shukor MY, Syed MA. Characterization of azo dye-degrading Coriolopsis sp. strain arf5. Bioremediation Sci Technol Res. 2013;1(1):8-14.

Zwietering MH, Jongenburger I, Rombouts FM, Van't Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990;56(6):1875-81.

Grover R. Studies on the degradation of 4-amino-3,5,6-trichloroplcolinic acid in soil. Weed Res. 1967;7(1):61-7.

Premuzic ET, Francis AJ, Lin M, Schubert J. Induced formation of chelating agents by Pseudomonas aeruginosa grown in presence of thorium and uranium. Arch Environ Contam Toxicol. 1985 Nov 1;14(6):759-68.

Nishino SF, Spain JC. Cell density-dependent adaptation of Pseudomonas putida to biodegradation of p-nitrophenol. Environ Sci Technol. 1993 Mar 1;27(3):489-94.

Dyreborg S, Arvin E, Broholm K. Biodegradation of NSO-compounds under different redox-conditions. J Contam Hydrol. 1997 Mar 1;25(3):177-97.

Riffat R, Krongthamchat K. Specific methanogenic activity of halophilic and mixed cultures in saline wastewater. Int J Environ Sci Technol. 2006 Dec 1;2(4):291-9.

Franco LC, Steinbeisser S, Zane GM, Wall JD, Fields MW. Cr(VI) reduction and physiological toxicity are impacted by resource ratio in Desulfovibrio vulgaris. Appl Microbiol Biotechnol. 2018;102(6):2839-50.

Jo KH, Silverstein J. Substrate inhibition of degradation of 2,4-Dinitrophenol in activated sludge. Water Environ Res. 1998;70(1):94-100.

Downloads

Published

31.12.2022

How to Cite

Manogaran, M., Gafar, A. A., & Abubakar, A. (2022). Degradation Kinetics of Basic violet 3 by Staphylococcus aureus. Asian Journal of Plant Biology, 4(2), 45–49. https://doi.org/10.54987/ajpb.v4i2.788

Issue

Section

Articles