Machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal species
Algae are recognized for their potential in biofuel production due to their high biomass yield, protein and lipid. This study investigates the influence of pH, temperature, light intensity, light colour and CO2 concentration on biomass and biochemical composition in five algal genera (Chlorella, Bot...
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The Royal Society
2025-04-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.241336 |
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| author | Aisha Khan Saleem Ullah Rifat Ali Mahwish Rehman Said Moshawih Khang Wen Goh Long Chiau Ming Lai Ti Gew |
| author_facet | Aisha Khan Saleem Ullah Rifat Ali Mahwish Rehman Said Moshawih Khang Wen Goh Long Chiau Ming Lai Ti Gew |
| author_sort | Aisha Khan |
| collection | DOAJ |
| description | Algae are recognized for their potential in biofuel production due to their high biomass yield, protein and lipid. This study investigates the influence of pH, temperature, light intensity, light colour and CO2 concentration on biomass and biochemical composition in five algal genera (Chlorella, Botryococcus, Chlamydomonas, Tetraselmis and Closterium). Algal samples were isolated from aquatic environments in KPK-Pakistan and cultured under controlled conditions. Environmental variables were systematically varied: pH, temperature, light intensity, light colour and CO2 concentration. Biochemical analyses revealed biomass ranging from 0.2 to 2.1 g l−1, lipids 7.2–24.5% and proteins 8–49.5%, with optimal conditions of pH 7, 30°C, red light, 3000 lux and 9% CO₂. Machine learning was applied to optimize environmental conditions, with random forest (RF) identified as the most effective model. A novel metric, W_new, combining performance and error metrics, facilitated robust model evaluation and hyperparameter tuning. The model’s feature importance analysis ranked CO₂ concentration and pH as the most influential factors. RF achieved R² scores of 0.686 (training) and 0.534 (validation), demonstrating strong predictive performance. This study integrates experimental and computational approaches, providing a detailed framework for optimizing algal cultivation. We highlighted the utility of machine learning in enhancing biomass and lipid productivity, advancing the sustainable production of biofuel. |
| format | Article |
| id | doaj-art-87ffe03434784a218ced473f6c29bfea |
| institution | DOAJ |
| issn | 2054-5703 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj-art-87ffe03434784a218ced473f6c29bfea2025-08-20T03:19:16ZengThe Royal SocietyRoyal Society Open Science2054-57032025-04-0112410.1098/rsos.241336Machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal speciesAisha Khan0Saleem Ullah1Rifat Ali2Mahwish Rehman3Said Moshawih4Khang Wen Goh5Long Chiau Ming6Lai Ti Gew7Department of Agricultural Chemistry and Biochemistry, The University of Agriculture, Peshawar, PakistanDepartment of Agricultural Chemistry and Biochemistry, The University of Agriculture, Peshawar, PakistanDirectorate General Agriculture Research, Government of Khyber Pakhtunkhwa , Peshawar, PakistanDirectorate General Agriculture Research, Government of Khyber Pakhtunkhwa , Peshawar, PakistanDepartment of Pharmaceutical Sciences, Faculty of Pharmacy, Al-Ahliyya Amman University, Amman, JordanFaculty of Data Science and Information Technology, INTI International University, Nilai, MalaysiaSir Jeffrey Cheah Sunway Medical School, Faculty of Medical and Life Sciences, Sunway University, Sunway City, MalaysiaSir Jeffrey Cheah Sunway Medical School, Faculty of Medical and Life Sciences, Sunway University, Sunway City, MalaysiaAlgae are recognized for their potential in biofuel production due to their high biomass yield, protein and lipid. This study investigates the influence of pH, temperature, light intensity, light colour and CO2 concentration on biomass and biochemical composition in five algal genera (Chlorella, Botryococcus, Chlamydomonas, Tetraselmis and Closterium). Algal samples were isolated from aquatic environments in KPK-Pakistan and cultured under controlled conditions. Environmental variables were systematically varied: pH, temperature, light intensity, light colour and CO2 concentration. Biochemical analyses revealed biomass ranging from 0.2 to 2.1 g l−1, lipids 7.2–24.5% and proteins 8–49.5%, with optimal conditions of pH 7, 30°C, red light, 3000 lux and 9% CO₂. Machine learning was applied to optimize environmental conditions, with random forest (RF) identified as the most effective model. A novel metric, W_new, combining performance and error metrics, facilitated robust model evaluation and hyperparameter tuning. The model’s feature importance analysis ranked CO₂ concentration and pH as the most influential factors. RF achieved R² scores of 0.686 (training) and 0.534 (validation), demonstrating strong predictive performance. This study integrates experimental and computational approaches, providing a detailed framework for optimizing algal cultivation. We highlighted the utility of machine learning in enhancing biomass and lipid productivity, advancing the sustainable production of biofuel.https://royalsocietypublishing.org/doi/10.1098/rsos.241336algaebiodiesel productionsustainable energymachine learningclean fuelgreen product |
| spellingShingle | Aisha Khan Saleem Ullah Rifat Ali Mahwish Rehman Said Moshawih Khang Wen Goh Long Chiau Ming Lai Ti Gew Machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal species Royal Society Open Science algae biodiesel production sustainable energy machine learning clean fuel green product |
| title | Machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal species |
| title_full | Machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal species |
| title_fullStr | Machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal species |
| title_full_unstemmed | Machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal species |
| title_short | Machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal species |
| title_sort | machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal species |
| topic | algae biodiesel production sustainable energy machine learning clean fuel green product |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.241336 |
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