Abstract
The trend in bioplastic application has increased over the years where polyhydroxyalkanoates (PHAs) have emerged as a potential candidate with the advantage of being bio-origin, biodegradable, and biocompatible. The present study aims to understand the effect of acetic acid concentration (in combination with sucrose) as a mixture variable and its time of addition (process variable) on PHA production by Cupriavidus necator. The addition of acetic acid at a concentration of 1 g l−1 showed a positive influence on biomass and PHA yield; however, the further increase had a reversal effect. The addition of acetic acid at the time of incubation showed a higher PHA yield, whereas maximum biomass was achieved when acetic acid was added after 48 h. Genetic algorithm (GA) optimized artificial neural network (ANN) was used to model PHA concentration from mixture-process design data. Fitness of the GA-ANN model (R2: 0.935) was superior when compared to the polynomial model (R2: 0.301) from mixture design. Optimization of the ANN model projected 2.691 g l−1 PHA from 7.245 g l−1 acetic acid, 12.756 g l−1 sucrose, and the addition of acetic acid at the time of incubation. Sensitivity analysis indicates the inhibitory effect of all the predictors at higher levels. ANN model can be further used to optimize the variables while extending the bioprocess to fed-batch operation.
Similar content being viewed by others
Data Availability
All data generated or analyzed during this study are available from the corresponding author upon reasonable request.
References
Kumar, R., Verma, A., Shome, A., et al. (2021). Impacts of plastic pollution on ecosystem services, sustainable development goals, and need to focus on circular economy and policy interventions. Sustainability, 13(17), 9963. https://doi.org/10.3390/su13179963
Lhamo, P., Behera, S. K., & Mahanty, B. (2021). Process optimization, metabolic engineering interventions and commercialization of microbial polyhydroxyalkanoates production – A state-of-the art review. Biotechnology Journal, 16(9), 2100136. https://doi.org/10.1002/biot.202100136
Pandey, A., Adama, N., Adjallé, K., & Blais, J. F. (2022). Sustainable applications of polyhydroxyalkanoates in various fields: A critical review. International Journal of Biological Macromolecules, 221, 1184–1201. https://doi.org/10.1016/j.ijbiomac.2022.09.098
Luo, Z., Wu, Y., Li, Z., & Loh, X. J. (2019). Recent progress in polyhydroxyalkanoates-based copolymers for biomedical applications. Biotechnology Journal, 14(12), 1900283. https://doi.org/10.1002/biot.201900283
Kaniuk, Ł, & Stachewicz, U. (2021). Development and advantages of biodegradable pha polymers based on electrospun PHBV fibers for tissue engineering and other biomedical applications. ACS Biomaterials Science & Engineering, 7(12), 5339–5362. https://doi.org/10.1021/acsbiomaterials.1c00757
Tan, D., Wang, Y., Tong, Y., & Chen, G.-Q. (2021). Grand challenges for industrializing polyhydroxyalkanoates (PHAs). Trends in Biotechnology, 39(9), 953–963. https://doi.org/10.1016/j.tibtech.2020.11.010
Mezzolla, V., D’Urso, O., & Poltronieri, P. (2018). Role of PhaC type I and type II enzymes during PHA biosynthesis. Polymers, 10(8), 910. https://doi.org/10.3390/polym10080910
Kingsly, J. S., Chathalingath, N., Parthiban, S. A., et al. (2022). Utilization of sugarcane molasses as the main carbon source for the production of polyhydroxyalkanoates from Enterobacter cloacae. Energy Nexus, 6, 100071. https://doi.org/10.1016/j.nexus.2022.100071
Ratnaningrum, D., Saraswaty, V., Priatni, S., Lisdiyanti, P., Purnomo, A., & Pudjiraharti, S. (2019). Screening of polyhydroxyalkanoates (PHA)-producing bacteria from soil bacteria strains. IOP Conference Series: Earth and Environmental Science, 277(1), 012003. https://doi.org/10.1088/1755-1315/277/1/012003
Sathya, A. B., Sivasubramanian, V., Santhiagu, A., Sebastian, C., & Sivashankar, R. (2018). Production of polyhydroxyalkanoates from renewable sources using bacteria. Journal of Polymers and the Environment, 26(9), 3995–4012. https://doi.org/10.1007/s10924-018-1259-7
Gottardo, M., Bolzonella, D., Adele Tuci, G., Valentino, F., Majone, M., Pavan, P., & Battista, F. (2022). Producing volatile fatty acids and polyhydroxyalkanoates from foods by-products and waste: A review. Bioresource Technology, 361, 127716. https://doi.org/10.1016/j.biortech.2022.127716
Szacherska, K., Oleskowicz-Popiel, P., Ciesielski, S., & Mozejko-Ciesielska, J. (2021). Volatile fatty acids as carbon sources for polyhydroxyalkanoates production. Polymers, 13(3), 321. https://doi.org/10.3390/polym13030321
Sun, S., Ding, Y., Liu, M., Xian, M., & Zhao, G. (2020). Comparison of glucose, acetate and ethanol as carbon resource for production of poly(3-Hydroxybutyrate) and other acetyl-CoA derivatives. Frontiers in Bioengineering and Biotechnology, 8. https://doi.org/10.3389/fbioe.2020.00833
Vu, D. H., Wainaina, S., Taherzadeh, M. J., Åkesson, D., & Ferreira, J. A. (2021). Production of polyhydroxyalkanoates (PHAs) by Bacillus megaterium using food waste acidogenic fermentation-derived volatile fatty acids. Bioengineered, 12(1), 2480–2498. https://doi.org/10.1080/21655979.2021.1935524
Wang, M.-R., Li, H.-F., Yi, J.-J., Tao, S.-Y., & Li, Z.-J. (2022). Production of polyhydroxyalkanoates by three novel species of Marinobacterium. International Journal of Biological Macromolecules, 195, 255–263. https://doi.org/10.1016/j.ijbiomac.2021.12.019
Bravo-Porras, G., Fernández-Güelfo, L. A., Álvarez-Gallego, C. J., Carbú, M., Sales, D., & Romero-García, L. I. (2021). Influence of the total concentration and the profile of volatile fatty acids on polyhydroxyalkanoates (PHA) production by mixed microbial cultures. Biomass Conversion and Biorefinery. https://doi.org/10.1007/s13399-021-02208-z
Szacherska, K., Moraczewski, K., Rytlewski, P., Czaplicki, S., Ciesielski, S., Oleskowicz-Popiel, P., & Mozejko-Ciesielska, J. (2022). Polyhydroxyalkanoates production from short and medium chain carboxylic acids by Paracoccus homiensis. Scientific Reports, 12(1), 7263. https://doi.org/10.1038/s41598-022-11114-x
Reddy, M. V., Watanabe, A., Onodera, R., et al. (2020). Polyhydroxyalkanoates (PHA) production using single or mixture of fatty acids with Bacillus sp. CYR1: Identification of PHA synthesis genes. Bioresource Technology Reports, 11, 100483. https://doi.org/10.1016/j.biteb.2020.100483
Marudkla, J., Lee, W.-C., Wannawilai, S., Chisti, Y., & Sirisansaneeyakul, S. (2018). Model of acetic acid-affected growth and poly(3-hydroxybutyrate) production by Cupriavidus necator DSM 545. Journal of Biotechnology, 268, 12–20. https://doi.org/10.1016/j.jbiotec.2018.01.004
Munir, S., & Jamil, N. (2018). Polyhydroxyalkanoates (PHA) production in bacterial co-culture using glucose and volatile fatty acids as carbon source. Journal of Basic Microbiology, 58(3), 247–254. https://doi.org/10.1002/jobm.201700276
Fradinho, J. C., Oehmen, A., & Reis, M. A. M. (2019). Improving polyhydroxyalkanoates production in phototrophic mixed cultures by optimizing accumulator reactor operating conditions. International Journal of Biological Macromolecules, 126, 1085–1092. https://doi.org/10.1016/j.ijbiomac.2018.12.270
Catherine, M.-C., Guwy, A., & Massanet-Nicolau, J. (2022). Effect of acetate concentration, temperature, pH and nutrient concentration on polyhydroxyalkanoates (PHA) production by glycogen accumulating organisms. Bioresource Technology Reports, 20, 101226. https://doi.org/10.1016/j.biteb.2022.101226
Almeida, P. Z., Messias, J. M., Pereira, M. G., et al. (2018). Mixture design of starchy substrates hydrolysis by an immobilized glucoamylase from Aspergillus brasiliensis. Biocatalysis and Biotransformation, 36(5), 389–395. https://doi.org/10.1080/10242422.2017.1423059
Shahid, S., Corroler, D., Mosrati, R., et al. (2021). Optimization of growth conditions for the biosynthesis of medium-chain length polyhydroxyalkanoates from Bacillus megaterium DSM 509: Experimental analysis, statistical modelling, and characterization. Biomass Conversion and Biorefinery. https://doi.org/10.1007/s13399-021-01986-w
Pan, W., Nomura, C. T., & Nakas, J. P. (2012). Estimation of inhibitory effects of hemicellulosic wood hydrolysate inhibitors on PHA production by Burkholderia cepacia ATCC 17759 using response surface methodology. Bioresource Technology, 125, 275–282. https://doi.org/10.1016/j.biortech.2012.08.107
Dietrich, K., Dumont, M.-J., Schwinghamer, T., Orsat, V., & Del Rio, L. F. (2018). Model study to assess softwood hemicellulose hydrolysates as the carbon source for PHB production in Paraburkholderia sacchari IPT 101. Biomacromolecules, 19(1), 188–200. https://doi.org/10.1021/acs.biomac.7b01446
Bezerra, M. A., Lemos, V. A., Novaes, C. G., de Jesus, R. M., Filho, H. R. S., Araújo, S. A., & Alves, J. P. S. (2020). Application of mixture design in analytical chemistry. Microchemical Journal, 152, 104336. https://doi.org/10.1016/j.microc.2019.104336
Li, M., Eskridge, K. M., & Wilkins, M. R. (2019). Optimization of polyhydroxybutyrate production by experimental design of combined ternary mixture (glucose, xylose and arabinose) and process variables (sugar concentration, molar C: N ratio). Bioprocess and Biosystems Engineering, 42(9), 1495–1506. https://doi.org/10.1007/s00449-019-02146-1
Bhuva, P., & Bhogayata, A. (2022). A review on the application of artificial intelligence in the mix design optimization and development of self-compacting concrete. Materials Today: Proceedings, 65, 603–608. https://doi.org/10.1016/j.matpr.2022.03.194
Bose, S. A., Rajulapati, S. B., Velmurugan, S., Arockiasamy, S., Jayaram, K., Kola, A. K., & Raja, S. (2023). Process intensification of biopolymer polyhydroxybutyrate production by Pseudomonas putida SS9: A statistical approach. Chemosphere, 313, 137350. https://doi.org/10.1016/j.chemosphere.2022.137350
Yu, H.-C., Huang, S.-M., Lin, W.-M., Kuo, C.-H., & Shieh, C.-J. (2019). Comparison of artificial neural networks and response surface methodology towards an efficient ultrasound-assisted extraction of chlorogenic acid from Lonicera japonica. Molecules, 24(12), 2304. https://doi.org/10.3390/molecules24122304
Tantoco, C.J.A., Requiso, P.J., Alfafara, C.G., Capunitan, J.A., Nayve Jr., F.R.P., & Ventura, J.-R. S. (2023). Response surface methodology and artificial neural network optimization and modeling of the saccharification and fermentation conditions of the polyhydroxybutyrate from corn stover. Philippine Journal of Science, 152(1), 357–374.
Zafar, M., Kumar, S., Kumar, S., & Dhiman, A. K. (2012). Optimization of polyhydroxybutyrate (PHB) production by Azohydromonas lata MTCC 2311 by using genetic algorithm based on artificial neural network and response surface methodology. Biocatalysis and Agricultural Biotechnology, 1(1), 70–79. https://doi.org/10.1016/j.bcab.2011.08.012
Gupta, T. K., & Raza, K. (2019). Optimization of ANN architecture: A review on nature-inspired techniques. In Machine Learning in Bio-Signal Analysis and Diagnostic Imaging (pp. 159–182). Elsevier. https://doi.org/10.1016/B978-0-12-816086-2.00007-2
Zhang, L., Han, X., Yuan, B., Zhang, A., Feng, J., & Zhang, J. (2021). Mechanism of purification of low-pollution river water using a modified biological contact oxidation process and artificial neural network modeling. Journal of Environmental Chemical Engineering, 9(2), 104832. https://doi.org/10.1016/j.jece.2020.104832
Rebocho, A. T., Pereira, J. R., Neves, L. A., et al. (2020). Preparation and characterization of films based on a natural P(3HB)/mcl-PHA blend obtained through the co-culture of Cupriavidus necator and Pseudomonas citronellolis in apple pulp waste. Bioengineering, 7(2), 34. https://doi.org/10.3390/bioengineering7020034
Jiao, D., Shi, C., Yuan, Q., An, X., & Liu, Y. (2018). Mixture design of concrete using simplex centroid design method. Cement and Concrete Composites, 89, 76–88. https://doi.org/10.1016/j.cemconcomp.2018.03.001
Pradhan, U. K., Lal, K., Dash, S., & Singh, K. N. (2017). Design and analysis of mixture experiments with process variable. Communications in Statistics - Theory and Methods, 46(1), 259–270. https://doi.org/10.1080/03610926.2014.990104
Erzurum Cicek, Z. I., & Kamisli Ozturk, Z. (2021). Optimizing the artificial neural network parameters using a biased random key genetic algorithm for time series forecasting. Applied Soft Computing, 102, 107091. https://doi.org/10.1016/j.asoc.2021.107091
Haldar, D., Shabbirahmed, A. M. A. M., & Mahanty, B. (2023). Multivariate regression and artificial neural network modelling of sugar yields from acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass. Bioresource Technology, 370, 128519. https://doi.org/10.1016/j.biortech.2022.128519
Moodley, P., Rorke, D. C. S., & Gueguim Kana, E. B. (2019). Development of artificial neural network tools for predicting sugar yields from inorganic salt-based pretreatment of lignocellulosic biomass. Bioresource Technology, 273, 682–686. https://doi.org/10.1016/j.biortech.2018.11.034
Chung, W. J., & Liu, C. (2022). Analysis of input parameters for deep learning-based load prediction for office buildings in different climate zones using explainable artificial intelligence. Energy and Buildings, 276, 112521. https://doi.org/10.1016/j.enbuild.2022.112521
Tsoka, T., Ye, X., Chen, Y., Gong, D., & Xia, X. (2022). Explainable artificial intelligence for building energy performance certificate labelling classification. Journal of Cleaner Production, 355, 131626. https://doi.org/10.1016/j.jclepro.2022.131626
Yu, X., Ergan, S., & Dedemen, G. (2019). A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption. Applied Energy, 253, 113497. https://doi.org/10.1016/j.apenergy.2019.113497
Lhamo, P., & Mahanty, B. (2022). Multiple bioanalytical method based residual biomass prediction in microbial culture using multivariate regression and artificial neural network. Chemometrics and Intelligent Laboratory Systems, 231, 104687. https://doi.org/10.1016/j.chemolab.2022.104687
Lowry, O. H., Rosebrough, N. J., Farr, A. L., & Randall, R. J. (1951). Protein measurement with the folin phenol reagent. Journal of Biological Chemistry, 193(1), 265–275. https://doi.org/10.1016/S0021-9258(19)52451-6
Jain, A., Jain, R., & Jain, S. (2020). Quantitative analysis of reducing sugars by 3, 5-dinitrosalicylic acid (DNSA method). In Basic techniques in biochemistry, microbiology and molecular biology (pp. 181–183). Humana Press. https://doi.org/10.1007/978-1-4939-9861-6_43
Langenfeld, N. J., Payne, L. E., & Bugbee, B. (2021). Colorimetric determination of urea using diacetyl monoxime with strong acids. PLOS ONE, 16(11), e0259760. https://doi.org/10.1371/journal.pone.0259760
Pradhan, S., Dikshit, P. K., & Moholkar, V. S. (2018). Production, ultrasonic extraction, and characterization of poly (3-hydroxybutyrate) (PHB) using Bacillus megaterium and Cupriavidus necator. Polymers for Advanced Technologies, 29(8), 2392–2400. https://doi.org/10.1002/pat.4351
Cristea, A., Baricz, A., Leopold, N., et al. (2018). Polyhydroxybutyrate production by an extremely halotolerant Halomonas elongata strain isolated from the hypersaline meromictic Fără Fund Lake (Transylvanian Basin, Romania). Journal of Applied Microbiology, 125(5), 1343–1357. https://doi.org/10.1111/jam.14029
Garcia-Gonzalez, L., & De Wever, H. (2018). Acetic acid as an indirect sink of CO2 for the synthesis of polyhydroxyalkanoates (PHA): Comparison with PHA production processes directly using CO2 as feedstock. Applied Sciences, 8(9), 1416. https://doi.org/10.3390/app8091416
Venkata Mohan, S., & Venkateswar Reddy, M. (2013). Optimization of critical factors to enhance polyhydroxyalkanoates (PHA) synthesis by mixed culture using Taguchi design of experimental methodology. Bioresource Technology, 128, 409–416. https://doi.org/10.1016/j.biortech.2012.10.037
Al Battashi, H., Al-Kindi, S., Gupta, V. K., & Sivakumar, N. (2021). Polyhydroxyalkanoate (PHA) production using volatile fatty acids derived from the anaerobic digestion of waste paper. Journal of Polymers and the Environment, 29(1), 250–259. https://doi.org/10.1007/s10924-020-01870-0
Yang, S., Li, S., & Jia, X. (2019). Production of medium chain length polyhydroxyalkanoate from acetate by engineered Pseudomonas putida KT2440. Journal of Industrial Microbiology and Biotechnology, 46(6), 793–800. https://doi.org/10.1007/s10295-019-02159-5
Poontawee, & Limtong. (2020). Feeding strategies of two-stage fed-batch cultivation processes for microbial lipid production from sugarcane top hydrolysate and crude glycerol by the oleaginous red yeast Rhodosporidiobolus fluvialis. Microorganisms, 8(2), 151. https://doi.org/10.3390/microorganisms8020151
Kacanski, M., Pucher, L., Peral, C., Dietrich, T., & Neureiter, M. (2022). Cell retention as a viable strategy for PHA production from diluted VFAs with Bacillus megaterium. Bioengineering, 9(3), 122. https://doi.org/10.3390/bioengineering9030122
Kokkonen, P., Beier, A., Mazurenko, S., Damborsky, J., Bednar, D., & Prokop, Z. (2021). Substrate inhibition by the blockage of product release and its control by tunnel engineering. RSC Chemical Biology, 2(2), 645–655. https://doi.org/10.1039/D0CB00171F
Białek, J., Bujalski, W., Wojdan, K., Guzek, M., & Kurek, T. (2022). Dataset level explanation of heat demand forecasting ANN with SHAP. Energy, 261, 125075. https://doi.org/10.1016/j.energy.2022.125075
Vu, D. H., Mahboubi, A., Root, A., Heinmaa, I., Taherzadeh, M. J., & Åkesson, D. (2022). Thorough investigation of the effects of cultivation factors on polyhydroalkanoates (PHAs) production by Cupriavidus necator from food waste-derived volatile fatty acids. Fermentation, 8(11), 605. https://doi.org/10.3390/fermentation8110605
Ling, C., Qiao, G. Q., Shuai, B. W., Olavarria, K., Yin, J., Xiang, R. J., … Chen, G. Q. (2018). Engineering NADH/NAD + ratio in Halomonas bluephagenesis for enhanced production of polyhydroxyalkanoates (PHA). Metabolic Engineering, 49, 275–286. https://doi.org/10.1016/j.ymben.2018.09.007
Kedia, G., Passanha, P., Dinsdale, R. M., Guwy, A. J., & Esteves, S. R. (2014). Evaluation of feeding regimes to enhance PHA production using acetic and butyric acids by a pure culture of Cupriavidus necator. Biotechnology and Bioprocess Engineering, 19(6), 989–995. https://doi.org/10.1007/s12257-014-0144-z
Wang, J., Liu, S., Huang, J., Cui, R., Xu, Y., & Song, Z. (2023). Genetic engineering strategies for sustainable polyhydroxyalkanoate (PHA) production from carbon-rich wastes. Environmental Technology & Innovation, 30, 103069. https://doi.org/10.1016/j.eti.2023.103069
Chen, H., Meng, H., Nie, Z., & Zhang, M. (2013). Polyhydroxyalkanoate production from fermented volatile fatty acids: Effect of pH and feeding regimes. Bioresource Technology, 128, 533–538. https://doi.org/10.1016/j.biortech.2012.10.121
Valentino, F., Beccari, M., Fraraccio, S., Zanaroli, G., & Majone, M. (2014). Feed frequency in a sequencing batch reactor strongly affects the production of polyhydroxyalkanoates (PHAs) from volatile fatty acids. New Biotechnology, 31(4), 264–275. https://doi.org/10.1016/j.nbt.2013.10.006
Chakraborty, P., Gibbons, W., & Muthukumarappan, K. (2009). Conversion of volatile fatty acids into polyhydroxyalkanoate by Ralstonia eutropha. Journal of Applied Microbiology, 106(6), 1996–2005. https://doi.org/10.1111/j.1365-2672.2009.04158.x
Korkakaki, E., Mulders, M., Veeken, A., Rozendal, R., van Loosdrecht, M. C. M., & Kleerebezem, R. (2016). PHA production from the organic fraction of municipal solid waste (OFMSW): Overcoming the inhibitory matrix. Water Research, 96, 74–83. https://doi.org/10.1016/j.watres.2016.03.033
Zhao, L., Bao, M., Zhao, D., & Li, F. (2021). Correlation between polyhydroxyalkanoates and extracellular polymeric substances in the activated sludge biosystems with different carbon to nitrogen ratio. Biochemical Engineering Journal, 176, 108204. https://doi.org/10.1016/j.bej.2021.108204
Pokój, T., Klimiuk, E., & Ciesielski, S. (2019). Interactive effect of crude glycerin concentration and C: N ratio on polyhydroxyalkanoates accumulation by mixed microbial cultures modelled with response surface methodology. Water Research, 156, 434–444. https://doi.org/10.1016/j.watres.2019.03.033
Chavan, S., Yadav, B., Tyagi, R. D., & Drogui, P. (2021). A review on production of polyhydroxyalkanoate (PHA) biopolyesters by thermophilic microbes using waste feedstocks. Bioresource Technology, 341, 125900. https://doi.org/10.1016/j.biortech.2021.125900
Pagliano, G., Galletti, P., Samorì, C., Zaghini, A., & Torri, C. (2021). Recovery of polyhydroxyalkanoates from single and mixed microbial cultures: A review. Frontiers in Bioengineering and Biotechnology, 9, 624021. https://doi.org/10.3389/fbioe.2021.624021
Rodriguez-Perez, S., Serrano, A., Pantión, A. A., & Alonso-Fariñas, B. (2018). Challenges of scaling-up PHA production from waste streams. A review. Journal of Environmental Management, 205, 215–230. https://doi.org/10.1016/j.jenvman.2017.09.083
Acknowledgements
PL and BM acknowledge the support from the Karunya Institute of Technology and Sciences. Standard ethical and professional conduct has been followed.
Author information
Authors and Affiliations
Contributions
Pema Lhamo contributed to conceptualization-supporting, formal analysis-equal, visualization-lead, writing—original draft-lead, writing—review and editing-equal. Biswanath Mahanty contributed to conceptualization-lead, project administration-lead, supervision-lead, writing—original draft-supporting, writing—review and editing-equal.
Corresponding author
Ethics declarations
Ethical Approval
Not applicable.
Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Competing Interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Lhamo, P., Mahanty, B. Impact of Acetic Acid Supplementation in Polyhydroxyalkanoates Production by Cupriavidus necator Using Mixture-Process Design and Artificial Neural Network. Appl Biochem Biotechnol 196, 1155–1174 (2024). https://doi.org/10.1007/s12010-023-04567-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12010-023-04567-x