scholarly journals Mathematical Modelling of the Growth of Bacillus cereus Strain wwcp1on Malachite Green Dye

2021 ◽  
Vol 9 (2) ◽  
pp. 25-29
Author(s):  
Salihu Yahuza ◽  
Ibrahim Alhaji Sabo
Keyword(s):  
Confidence Interval ◽  
Bacillus Cereus ◽  
Malachite Green ◽  
Bacterial Growth ◽  
Growth Models ◽  
Von Bertalanffy ◽  
Bacterial Growth Rate ◽  
Von Bertalanffy Model ◽  
Malachite Green Dye ◽  
Bias Factor

In this paper, various growth models such as Von Bertalanffy, Huang, Baranyi-Roberts, Modified Gompertz, Buchnam-3-phase, Modified-Richards and Modified-Logistics, were presented in fitting and evaluating the growth of Bacillus cereus wwcp1 on Malachite green dye. The Von Bertalanffy model was found to be the best model with the lowest RMSE and highest R2 values. The Accuracy and Bias factor values were near unity (1.0). The von Bertalanffy parameters such as A (lower asymptote bacterial growth), μ (bacterial growth rate) and k (curve fitting parameter) were found to be 2.757 (95% confidence interval from 2.131 to 3.382 ), 0.287 (95% confidence interval from 0.244 to 0.329) and 4.323 (95% confidence interval from 4.285 to 4.361) respectively.

Molybdenum Blue Production from Serratia sp. strain DRY5: Secondary Modeling

2021 ◽  
Vol 9 (2) ◽  
pp. 21-24
Author(s):  
Ibrahim Alhaji Sabo ◽  
Salihu Yahuza ◽  
Mohd Yunus Shukor
Keyword(s):  
Confidence Interval ◽  
Degradation Rate ◽  
Growth Models ◽  
Coefficient Of Determination ◽  
Specific Substrate ◽  
The Novel ◽  
Monod Model ◽  
Molybdenum Blue ◽  
Kinetic Growth ◽  
Bias Factor

In this work, kinetic growth models such as Luong, Yano, Teissier-Edward, Aiba, Haldane, Monod, Han and Levenspiel were used to model molybdenum blue production from Serratia sp. strain DRY5. Based on statistical analyses such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), and accuracy factor (AF), the Monod model was chosen as the best. The calculated values for the monod constants qmax (the maximum specific substrate degradation rate (h−1), and Ks (concentration of substrate at the half maximal degradation rate (mg/L)) were found to be 3.86 (95% confidence interval of 2.29 to 5.43), and 43.41 (95% confidence interval of 12.36 to 74.46) respectively. The novel constants discovered during the modelling exercise could be used in further secondary modelling.

Effect of Individual Variability on the von Bertalanffy Growth Equation

1980 ◽  
Vol 37 (2) ◽  
pp. 241-247 ◽  
Author(s):  
K. J. Sainsbury
Keyword(s):  
Growth Models ◽  
Growth Increment ◽  
Mean Value ◽  
Individual Variability ◽  
Age And Growth ◽  
Growth Equation ◽  
Von Bertalanffy ◽  
Von Bertalanffy Model ◽  
The Mean ◽  
Von Bertalanffy Growth Equation

The growth in length of a group of animals is examined. Each animal is assumed to grow according to the von Bertalanffy model with fixed parameters, but these parameters are allowed to differ between individuals. Equations governing the mean and variance of length at given age and growth increment at given length are provided, and their implications discussed. Results indicate that the traditional growth equation is likely to result in an underestimate of the mean value of K when either length at age or growth increment data are analyzed. This problem does not appear serious when using length at age data. However, the problems of interpretation are more serious in the case of growth increment data where serious overestimates of the reconstructed mean length at age can result. A thorough analysis of growth cannot be made for a population exhibiting individual variability in L∞ and K from growth increment data alone. In particular a nonlinear relationship between growth increment and initial length does not necessarily imply that the von Bertalanffy model is inappropriate to the species in question. A topic urgently in need of examination is the form of the joint distribution of K and L∞ in animal populations.Key words: von Bertalanffy, growth models

scholarly journals Effect of insulin-like growth factor 1 gene on growth traits of Kejobong goat and its growth analysis

2020 ◽  
Vol 13 (1) ◽  
pp. 127-133
Author(s):  
Dela Ayu Lestari ◽  
Takuro Oikawa ◽  
Sutopo Sutopo ◽  
Endang Purbowati ◽  
Asep Setiaji ◽  
...  
Keyword(s):  
Growth Factor ◽  
Growth Performance ◽  
Genetic Marker ◽  
Growth Analysis ◽  
Growth Traits ◽  
Growth Models ◽  
Insulin Like Growth Factor ◽  
Von Bertalanffy ◽  
Von Bertalanffy Model ◽  
Intron 4

Aim: This study aimed to identify the effect of the insulin-like growth factor 1 (IGF1) gene on growth, to uncover the genetic marker at the IGF1 gene, and to predict growth performance by analyzing growth models of Kejobong goats based on their genotype. Materials and Methods: DNA and records of body weight (BW) and body measurements (BM) of Kejobong goats were collected, the IGF1 gene was amplified from the DNA template by polymerase chain reaction (PCR); the PCR products were then sequenced to determine single nucleotide polymorphisms (SNP). Linear mixed model (LMM) was used to analyze the association between SNP and growth traits. Four non-linear growth models were analyzed using non-LMM to describe the growth model and to compare the growth within genotypes. Results: An SNP at intron 4 (g5752G→C) genotyped into GG and CC was significantly associated with BW and BM. Goats of genotype GG had a significantly higher BW and BM (p<0.05) than those of genotype CC. Growth analysis showed that the von Bertalanffy model was the most fit for describing BW, the Brody model for chest width and hip height, the Gompertz and Logistic models for heart girth, and the von Bertalanffy and Gompertz models for hip width. Conclusion: An SNP at intron 4 of the IGF1 gene was associated with the growth trait and was usable as a genetic marker candidate for improvement of growth traits of Kejobong goats while von Bertalanffy model provides proper and accurate estimates of parameters to describe the growth performance of Kejobong goats.

scholarly journals Mathematical Modeling of the growth of Acinetobacter baumannii YNWH 226 on Azo dye Congo red

2021 ◽  
Vol 4 (2) ◽  
pp. 7-10
Author(s):  
Ibrahim Alhaji Sabo ◽  
Salihu Yahuza ◽  
Mohd Yunus Shukor
Keyword(s):  
Confidence Interval ◽  
Azo Dyes ◽  
Statistical Tests ◽  
Industrial Effluents ◽  
Information Criteria ◽  
Bacterial Growth Rate ◽  
Aquatic Life ◽  
Bias Factor ◽  
Three Phase ◽  
Schnute Model

Industrial effluents (Azo dyes) are brightly coloured, making their disposal into receiving waters undesirable not only because many Azo dyes and their breakdown products are toxic to aquatic life and mutagenic to humans, but also because many Azo dyes and their breakdown products are harmful to aquatic life due to the presence of aromatics and metals, chlorides, and other chemicals. Various kinetic models, including modified Gompertz, Baranyi-Roberts, modified Richards, Von Bertalanffy, modified Logistics, modified Schnute, Buchanan three-phase, and the most recently presented Huang, were used in this study. Based on statistical tests, the modified Schnute model provided the best fit, with the lowest values for RMSE and corrected Akaike Information Criteria (AICc), the greatest value for adjusted R2, and the closest to unity for both Accuracy and Bias Factor. The Modified Schnute parameters such as λ (lag time), µmax (maximum specific bacterial growth rate) and curve fitting parameters α and β (Constant), were found to be -4.39 (95% confidence interval of -77.58 to 68.79), 57.00 (95% confidence interval of -2854.30 to 2968.30), 0.78 (95% confidence interval of -0.34 to 1.89) and 0.96 (95% confidence interval of -0.85 to 2.78, respectively.

scholarly journals Enzymatic Decolorization of Malachite Green Dye by a Newly Isolated Bacillus Cereus Strain wwcp

2014 ◽  
Vol 8 (12) ◽  
pp. 58-64 ◽  
Author(s):  
Wycliffe Chisutia Wanyonyi ◽  
◽  
John Mmari Onyari ◽  
Paul Mwanza Shiundu ◽  
Francis Jackim Mulaa
Keyword(s):  
Bacillus Cereus ◽  
Malachite Green ◽  
Malachite Green Dye ◽  
Enzymatic Decolorization

scholarly journals Biodegradation and Detoxification of Malachite Green Dye Using Novel Enzymes from Bacillus cereus Strain KM201428: Kinetic and Metabolite Analysis

2017 ◽  
Vol 119 ◽  
pp. 38-51 ◽  
Author(s):  
Wycliffe Chisutia Wanyonyi ◽  
John Mmari Onyari ◽  
Paul Mwanza Shiundu ◽  
Francis Jackim Mulaa
Keyword(s):  
Bacillus Cereus ◽  
Malachite Green ◽  
Metabolite Analysis ◽  
Malachite Green Dye

scholarly journals Kinetic Analysis of the Adsorption of Malachite Green onto Graphene Oxide Sheets Integrated with Gold Nanoparticles

2021 ◽  
Vol 9 (2) ◽  
pp. 48-52
Author(s):  
Ibrahim Alhaji Sabo ◽  
Salihu Yahuza ◽  
Bilal Ibrahim Dan-Iya ◽  
Abdussamad Abubakar
Keyword(s):  
Graphene Oxide ◽  
Confidence Interval ◽  
Malachite Green ◽  
Information Criterion ◽  
Cost Effective ◽  
Order Rate Constant ◽  
Visual Observation ◽  
Coefficient Of Determination ◽  
Textile Dye ◽  
Bias Factor

Malachite green is extensively used in the textile dye industry and in agriculture as fish pests’ pesticide. Biosorption is a type of sorption technique that uses a biological sorbent. As of now, biosorption is viewed as a simple and cost-effective process that might be used as an alternative to traditional pollution treatment methods. Bioremediation is one of the branches of bioremediation that is used to minimise pollution in the context of incorrect textile waste disposal. The sorption isotherm of Malachite Green onto graphene oxide were analyzed using three models—pseudo-1st, pseudo-2nd and Elovich, and fitted using non-linear regression. The Elovich model was the poorest in fitting the curve based on visual observation and the best was pseudo-2nd order based on statistical analysis such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC) and Hannan–Quinn information criterion (HQC). Nonlinear regression analysis using the pseudo-2nd order model gave values of equilibrium sorption capacity qe of 6.164 mg/g (95% confidence interval from 5.918 to 6.410) and a value of the pseudo-2nd-order rate constant, k2 of 0.034 (95% confidence interval from 0.024 to 0.045). Further analysis is needed to provide proof for the chemisorption mechanism usually tied to this kinetic.

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