Test of Randomness of Residuals for the Buchanan-three-phase model used in the Fitting the Growth of Moraxella sp. B on Monobromoacetic acid (MBA)

Main Article Content

Sabullah M.K.


Monobromoacetic acid (MBA) is a chemical intermediate often use in agriculture andpharmaceutical industries. The use of monohalogenated acetic acids including monobromoaceticacids in industry and agriculture has led to numerous environmental issues. Bioremediation ofmonobromoacetic acid, has been recommended as a cheaper and achievable method whencompared with physical and chemical approaches. Formerly, we model the growth of growth ofMoraxella sp. B on monobromoacetic acid from published literature to obtain vital growthconstants. We discovered that the Buchanan-three-phase model via nonlinear regression utilizing the least square method was the very best model to explain the growth curve. However, an essential consideration, which has not been pointed out enough, is the residuals of the model have to be random. To ensure that randomness being met we carry out the Wald-Wolfowitz runstest. The results showed that the number of runs was 13, and the expected number of runs under the assumption of randomness was 7.462, indicating the series of residuals had adequate runs. The p-value obtained was greater than 0.05, therefore the null hypothesis is not rejected indicating no convincing evidence of non-randomness of the residuals and they do represent noise.

Article Details

How to Cite
M.K., Sabullah. Test of Randomness of Residuals for the Buchanan-three-phase model used in the Fitting the Growth of Moraxella sp. B on Monobromoacetic acid (MBA). Bulletin of Environmental Science and Management, [S.l.], v. 3, n. 1, p. 13-15, dec. 2015. ISSN 2289-5876. Available at: <http://journal.hibiscuspublisher.com/index.php/BESM/article/view/261>. Date accessed: 21 sep. 2018.


[1] Hamid AAA, Tengku Abdul Hamid TH, Wahab RA, Huyop F.
Identification of functional residues essential for dehalogenation
by the non-stereospecific -haloalkanoic acid dehalogenase from
Rhizobium sp. RC1. J Basic Microbiol. 2015;55(3):324–30.
[2] Weightman AL, Weightman AJ, Slater JH. Microbial
dehalogenation of trichloroacetic acid. World J Microbiol
Biotechnol. 1992;8(5):512–8.
[3] Yu P, Welander T. Growth of an aerobic bacterium with
trichloroacetic acid as the sole source of energy and carbon. Appl
Microbiol Biotechnol. 1995;42(5):769–74.
[4] Olaniran AO, Babalola GO, Okoh AI. Aerobic dehalogenation
potentials of four bacterial species isolated from soil and sewage
sludge. Chemosphere. 2001;45(1):45–50.
[5] McRae BM, Lapara TM, Hozalski RM. Biodegradation of
haloacetic acids by bacterial enrichment cultures. Chemosphere.
[6] Olaniran AO, Pillay D, Pillay B. Haloalkane and haloacid
dehalogenases from aerobic bacterial isolates indigenous to
contaminated sites in Africa demonstrate diverse substrate
specificities. Chemosphere. 2004;55(1):27–33.
[7] Torz MS, Yankov DS, Beschkov DN. Biodegradation of monoand
dihaloacetic acids by Moraxella sp. B. Comptes Rendus
L’Academie Bulg Sci. 2006;59(3):295–300.
[8] Alomar D, Abdul Hamid AA, Khosrowabadi E, Gicana RG,
Lamis RJ, Huyop F, et al. Molecular characterization of
monochloroacetate-degrading Arthrobacter sp. Strain d2 isolated
from Universiti Teknologi Malaysia agricultural area.
Bioremediation J. 2014;18(1):12–9.
[9] Tonomura K, Kawasaki H, Tone N, Yahara H. Plasmid encoding
mercury reductase and haloacetate halidohydrolase in drugresistant
bacteria. J Pharmacobiodyn. 1981;4(4).
[10] Liu J-Q, Kurihara T, Ichiyama S, Miyagi M, Tsunasawa S,
Kawasaki H, et al. Reaction mechanism of fluoroacetate
dehalogenase from Moraxella sp. B. J Biol Chem.
[11] Shukor MS. Modelling the growth of Moraxella sp. B on
Monobromoacetic acid (MBA). Bull Environ Sci Manag.
[12] Razali NM, Wah YB. Power comparisons of Shapiro–Wilk,
Kolmogorov– Smirnov, Lilliefors and Anderson–Darling tests. J
Stat Model Anal. 2011;2:21–3.
[13] Jarque CM, Bera AK. Efficient tests for normality,
homoscedasticity and serial independence of regression residuals:
Monte Carlo evidence. Econ Lett. 1981;7(4):313–8.
[14] Snedecor GW, Cochran WG. Statistical methods. 7th ed. Ames
Iowa: Iowa State University Press; 1980.
[15] 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.
[16] Draper NR, Smith H. Applied Regression Analysis. Wiley, New
York; 1981.
[17] Rohatgi,.A..WebPlotDigitizer.
http://arohatgi.info/WebPlotDigitizer/app/ Accessed June 2
[18] Halmi MIE, Shukor MS, Johari WLW, Shukor MY. Evaluation
of several mathematical models for fitting the growth of the algae
Dunaliella tertiolecta. Asian J Plant Biol. 2014;2(1):1–6.
[19] Huitema BE, McKean JW, Zhao J. The runs test for
autocorrelated errors: unacceptable properties. J Educ Behav
Stat. 1996;21(4):390–404