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Hamza A, Khalad A, Kumar DS. Enhanced production of mycelium biomass and exopolysaccharides of Pleurotus ostreatus by integrating response surface methodology and artificial neural network. BIORESOURCE TECHNOLOGY 2024; 399:130577. [PMID: 38479624 DOI: 10.1016/j.biortech.2024.130577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/08/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024]
Abstract
This study aimed to enhance the production of mycelium biomass and exopolysaccharides (EPS) of Pleurotus ostreatus in submerged fermentation. Response Surface Methodology (RSM)sought to optimize culture conditions, whereas Artificial Neural Network (ANN)aimed to predict the mycelium biomass and EPS. After optimization of RSM model conditions, the maximum biomass (36.45 g/L) and EPS (6.72 g/L) were obtained at the optimum temperature of 22.9 °C, pH 5.6, and agitation of 138.9 rpm. Further, the Genetic Algorithm (GA) was employed to optimize the cultivation conditions in order to maximize the mycelium biomass and EPS production. The ANN model with an optimized network structure gave the coefficient of determination (R2) value of 0.99 and the least mean squared error of 1.9 for the validation set. In the end, a graphical user interface was developed to predict mycelium biomass and EPS production.
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Affiliation(s)
- Arman Hamza
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Telangana, India
| | - Abdul Khalad
- Department of Mechanical Engineering, Indian Institute of Technology Hyderabad, Telangana, India
| | - Devarai Santhosh Kumar
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Telangana, India.
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Guo H, Xi Y, Guzailinuer K, Wen Z. Optimization of preparation conditions for Salsola laricifolia protoplasts using response surface methodology and artificial neural network modeling. PLANT METHODS 2024; 20:52. [PMID: 38584286 PMCID: PMC11000288 DOI: 10.1186/s13007-024-01180-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 03/25/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND Salsola laricifolia is a typical C3-C4 typical desert plant, belonging to the family Amaranthaceae. An efficient single-cell system is crucial to study the gene function of this plant. In this study, we optimized the experimental conditions by using Box-Behnken experimental design and Response Surface Methodology (RSM)-Artificial Neural Network (ANN) model based on the previous studies. RESULTS Among the 17 experiment groups designed by Box-Behnken experimental design, the maximum yield (1.566 × 106/100 mg) and the maximum number of viable cells (1.367 × 106/100 mg) were obtained in group 12, and the maximum viability (90.81%) was obtained in group 5. Based on these results, both the RSM and ANN models were employed for evaluating the impact of experimental factors. By RSM model, cellulase R-10 content was the most influential factor on protoplast yield, followed by macerozyme R-10 content and mannitol concentration. For protoplast viability, the macerozyme R-10 content had the highest influence, followed by cellulase R-10 content and mannitol concentration. The RSM model performed better than the ANN model in predicting yield and viability. However, the ANN model showed significant improvement in predicting the number of viable cells. After comprehensive evaluation of the protoplast yield, the viability and number of viable cells, the optimal results was predicted by ANN yield model and tested. The amount of protoplast yield was 1.550 × 106/100 mg, with viability of 90.65% and the number of viable cells of 1.405 × 106/100 mg. The corresponding conditions were 1.98% cellulase R-10, 1.00% macerozyme R-10, and 0.50 mol L-1 mannitol. Using the obtained protoplasts, the reference genes (18SrRNA, β-actin and EF1-α) were screened for expression, and transformed with PEG-mediated pBI121-SaNADP-ME2-GFP plasmid vector. There was no significant difference in the expression of β-actin and EF1-α before and after treatment, suggesting that they can be used as internal reference genes in protoplast experiments. And SaNADP-ME2 localized in chloroplasts. CONCLUSION The current study validated and evaluated the effectiveness and results of RSM and ANN in optimizing the conditions for protoplast preparation using S. laricifolia as materials. These two methods can be used independently of experimental materials, making them suitable for isolating protoplasts from other plant materials. The selection of the number of viable cells as an evaluation index for protoplast experiments is based on its ability to consider both protoplast yield and viability. The findings of this study provide an efficient single-cell system for future genetic experiments in S. laricifolia and can serve as a reference method for preparing protoplasts from other materials.
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Affiliation(s)
- Hao Guo
- State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
- College of Life Sciences, Shihezi University, Shihezi, 832003, China
- Xinjiang Production and Construction Corps Key Laboratory of Oasis Town and Mountain-Basin System Ecology, Shihezi, 832003, China
| | - Yuxin Xi
- State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
- Xinjiang Key Lab of Conservation and Utilization of Plant Gene Resources, Urumqi, 830011, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kuerban Guzailinuer
- State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Sino-Tajikistan Joint Laboratory for Conservation and Utilization of Biological Resources, Urumqi, 830011, China
| | - Zhibin Wen
- State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China.
- The Specimen Museum of Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China.
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Guendouzi S, Benmati M, Bounabi H, Vicente Carbajosa J. Application of response surface Methodology coupled with Artificial Neural network and genetic algorithm to model and optimize symbiotic interactions between Chlorella vulgaris and Stutzerimonas stutzeri strain J3BG for chlorophyll accumulation. BIORESOURCE TECHNOLOGY 2024; 394:130148. [PMID: 38086458 DOI: 10.1016/j.biortech.2023.130148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023]
Abstract
Research on microalgae has surged due to its diverse biotechnological applications and capacity for accumulating bioactive compounds. Despite considerable advancements, microalgal cultivation remains costly, prompting efforts to reduce expenses while enhancing productivity. This study proposes a cost-effective approach through the coculture of microalgae and bacteria, exploiting mutualistic interactions. An engineered consortium of Chlorella vulgaris and Stutzerimonas stutzeri strain J3BG demonstrated biofilm-like arrangements, indicative of direct cell-to-cell interactions and metabolite exchange. Strain J3BG's enzymatic characterization revealed amylase, lipase, and protease production, sustaining mutual growth. Employing Response Surface Methodology (RSM), Artificial Neural Network (ANN), and Genetic Algorithm (GA) in a hybrid modeling approach resulted in a 2.1-fold increase in chlorophyll production. Optimized conditions included a NaNO3 concentration of 128.52 mg/l, a 1:2 (Algae:Bacteria) ratio, a 6-day cultivation period, and a pH of 5.4, yielding 10.92 ± 0.88 mg/l chlorophyll concentration.
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Affiliation(s)
- Salma Guendouzi
- Higher National School of Biotechnology Taoufik KHAZNADAR, nouveau Pôle universitaire Ali Mendjeli, BP. E66, Constantine 25100, Algeria; Laboratory of Biotechnology, Higher National School of Biotechnology Taoufik KHAZNADAR, nouveau Pôle universitaire Ali Mendjeli, BP. E66, Constantine 25100, Algeria.
| | - Mahbouba Benmati
- Higher National School of Biotechnology Taoufik KHAZNADAR, nouveau Pôle universitaire Ali Mendjeli, BP. E66, Constantine 25100, Algeria
| | - Hadjira Bounabi
- Higher National School of Biotechnology Taoufik KHAZNADAR, nouveau Pôle universitaire Ali Mendjeli, BP. E66, Constantine 25100, Algeria; Laboratory of Biotechnology, Higher National School of Biotechnology Taoufik KHAZNADAR, nouveau Pôle universitaire Ali Mendjeli, BP. E66, Constantine 25100, Algeria
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Zhang J, Zheng X, Xiao H, Shan C, Li Y, Yang T. Quality and Process Optimization of Infrared Combined Hot Air Drying of Yam Slices Based on BP Neural Network and Gray Wolf Algorithm. Foods 2024; 13:434. [PMID: 38338569 PMCID: PMC10855503 DOI: 10.3390/foods13030434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 01/09/2024] [Accepted: 01/18/2024] [Indexed: 02/12/2024] Open
Abstract
In this paper, the effects on drying time (Y1), the color difference (Y2), unit energy consumption (Y3), polysaccharide content (Y4), rehydration ratio (Y5), and allantoin content (Y6) of yam slices were investigated under different drying temperatures (50-70 °C), slice thicknesses (2-10 mm), and radiation distances (80-160 mm). The optimal drying conditions were determined by applying the BP neural network wolf algorithm (GWO) model based on response surface methodology (RMS). All the above indices were significantly affected by drying conditions (p < 0.05). The drying rate and effective water diffusion coefficient of yam slices accelerated with increasing temperature and decreasing slice thickness and radiation distance. The selection of lower temperature and slice thickness helped reduce the energy consumption and color difference. The polysaccharide content increased and then decreased with drying temperature, slice thickness, and radiation distance, and it was highest at 60 °C, 6 mm, and 120 mm. At 60 °C, lower slice thickness and radiation distance favored the retention of allantoin content. Under the given constraints (minimization of drying time, unit energy consumption, color difference, and maximization of rehydration ratio, polysaccharide content, and allantoin content), BP-GWO was found to have higher coefficients of determination (R2 = 0.9919 to 0.9983) and lower RMSEs (reduced by 61.34% to 80.03%) than RMS. Multi-objective optimization of BP-GWO was carried out to obtain the optimal drying conditions, as follows: temperature 63.57 °C, slice thickness 4.27 mm, radiation distance 91.39 mm, corresponding to the optimal indices, as follows: Y1 = 133.71 min, Y2 = 7.26, Y3 = 8.54 kJ·h·kg-1, Y4 = 20.73 mg/g, Y5 = 2.84 kg/kg, and Y6 = 3.69 μg/g. In the experimental verification of the prediction results, the relative error between the actual and predicted values was less than 5%, proving the model's reliability for other materials in the drying technology process research to provide a reference.
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Affiliation(s)
- Jikai Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (J.Z.); (Y.L.); (T.Y.)
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China
- Key Laboratory of Modern Agricultural Machinery Corps, Shihezi 832003, China
| | - Xia Zheng
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (J.Z.); (Y.L.); (T.Y.)
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China
- Key Laboratory of Modern Agricultural Machinery Corps, Shihezi 832003, China
| | - Hongwei Xiao
- College of Engineering, China Agricultural University, Beijing 100080, China;
| | - Chunhui Shan
- College of Food, Shihezi University, Shihezi 832003, China;
| | - Yican Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (J.Z.); (Y.L.); (T.Y.)
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China
- Key Laboratory of Modern Agricultural Machinery Corps, Shihezi 832003, China
| | - Taoqing Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (J.Z.); (Y.L.); (T.Y.)
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China
- Key Laboratory of Modern Agricultural Machinery Corps, Shihezi 832003, China
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