Mathematical modeling of Molybdenum-blue production from Bacillus sp. strain Neni-10
Heavy metals can be remediated using microorganism by altering the redox function i.e. reduction from more toxic oxidation state to non-toxic one. Molybdenum reduction to molybdenum blue by bacteria is an emerging tool for remediation of the metal. Mathematical modelling via nonlinear regression of...
| Main Authors: | , , , , |
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| Format: | Article |
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Hibiscus Publisher
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/94102/ |
| _version_ | 1848861911144202240 |
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| author | Rusnam Yakasai, Hafeez Muhammad Rahman, Mohd Fadhil Gusmanizar, Neni Shukor, Mohd Yunus |
| author_facet | Rusnam Yakasai, Hafeez Muhammad Rahman, Mohd Fadhil Gusmanizar, Neni Shukor, Mohd Yunus |
| author_sort | Rusnam |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Heavy metals can be remediated using microorganism by altering the redox function i.e. reduction from more toxic oxidation state to non-toxic one. Molybdenum reduction to molybdenum blue by bacteria is an emerging tool for remediation of the metal. Mathematical modelling via nonlinear regression of the heavy metal's reduction can yield important reduction parameters such as theoretical maximum reduction, specific reduction rate, and the lag period of reduction. Nonlinear regression can be utilized using various models such as logistic, Richards, Gompertz, Baranyi-Roberts, Schnute, Buchanan 3-phase, Von Bertalanffy and Huang with the best model yielding an underlying mechanistic property for the reduction. We demonstrate that the Baranyi-Roberts model was the best model in modelling the Mo-blue production curve of the bacterium Bacillus sp. strain Neni-10 based on statistical tests such as root-mean-square error (RMSE), corrected AICc (Akaike Information Criterion), adjusted coefficient of determination (R2), accuracy factor (AF) and bias factor (BF). The model parameters or constants obtained were maximum lag time (λ), Mo-blue production rate (μm), and maximal Mo-blue production (Ymax). The construction of secondary models will benefit greatly from the use of bacterial growth models to acquire realistic Mo-blue production rates. According to a literature search, this technique is wholly unique for molybdenum reduction to Mo-blue in particular, and in the heavy metals' detoxification process in general. The results of this study have demonstrated the usefulness of these models in simulating Mo-blue synthesis in bacteria. |
| first_indexed | 2025-11-15T13:08:39Z |
| format | Article |
| id | upm-94102 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:08:39Z |
| publishDate | 2021 |
| publisher | Hibiscus Publisher |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-941022023-05-23T02:37:12Z http://psasir.upm.edu.my/id/eprint/94102/ Mathematical modeling of Molybdenum-blue production from Bacillus sp. strain Neni-10 Rusnam Yakasai, Hafeez Muhammad Rahman, Mohd Fadhil Gusmanizar, Neni Shukor, Mohd Yunus Heavy metals can be remediated using microorganism by altering the redox function i.e. reduction from more toxic oxidation state to non-toxic one. Molybdenum reduction to molybdenum blue by bacteria is an emerging tool for remediation of the metal. Mathematical modelling via nonlinear regression of the heavy metal's reduction can yield important reduction parameters such as theoretical maximum reduction, specific reduction rate, and the lag period of reduction. Nonlinear regression can be utilized using various models such as logistic, Richards, Gompertz, Baranyi-Roberts, Schnute, Buchanan 3-phase, Von Bertalanffy and Huang with the best model yielding an underlying mechanistic property for the reduction. We demonstrate that the Baranyi-Roberts model was the best model in modelling the Mo-blue production curve of the bacterium Bacillus sp. strain Neni-10 based on statistical tests such as root-mean-square error (RMSE), corrected AICc (Akaike Information Criterion), adjusted coefficient of determination (R2), accuracy factor (AF) and bias factor (BF). The model parameters or constants obtained were maximum lag time (λ), Mo-blue production rate (μm), and maximal Mo-blue production (Ymax). The construction of secondary models will benefit greatly from the use of bacterial growth models to acquire realistic Mo-blue production rates. According to a literature search, this technique is wholly unique for molybdenum reduction to Mo-blue in particular, and in the heavy metals' detoxification process in general. The results of this study have demonstrated the usefulness of these models in simulating Mo-blue synthesis in bacteria. Hibiscus Publisher 2021-07-31 Article PeerReviewed Rusnam and Yakasai, Hafeez Muhammad and Rahman, Mohd Fadhil and Gusmanizar, Neni and Shukor, Mohd Yunus (2021) Mathematical modeling of Molybdenum-blue production from Bacillus sp. strain Neni-10. Bioremediation Science and Technology Research, 9 (1). pp. 7-12. ISSN 2289-5892 https://journal.hibiscuspublisher.com/index.php/BSTR/article/view/591 10.54987/bstr.v9i1.591 |
| spellingShingle | Rusnam Yakasai, Hafeez Muhammad Rahman, Mohd Fadhil Gusmanizar, Neni Shukor, Mohd Yunus Mathematical modeling of Molybdenum-blue production from Bacillus sp. strain Neni-10 |
| title | Mathematical modeling of Molybdenum-blue production from Bacillus sp. strain Neni-10 |
| title_full | Mathematical modeling of Molybdenum-blue production from Bacillus sp. strain Neni-10 |
| title_fullStr | Mathematical modeling of Molybdenum-blue production from Bacillus sp. strain Neni-10 |
| title_full_unstemmed | Mathematical modeling of Molybdenum-blue production from Bacillus sp. strain Neni-10 |
| title_short | Mathematical modeling of Molybdenum-blue production from Bacillus sp. strain Neni-10 |
| title_sort | mathematical modeling of molybdenum-blue production from bacillus sp. strain neni-10 |
| url | http://psasir.upm.edu.my/id/eprint/94102/ http://psasir.upm.edu.my/id/eprint/94102/ http://psasir.upm.edu.my/id/eprint/94102/ |