Authors : Martin Odinaka Ugwuoke; David Oche Idoko; Abutu Ann Oine

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/ykhmv73d

Scribd : https://tinyurl.com/3thbztzt

DOI : https://doi.org/10.38124/ijisrt/26aug1045

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Dissolved oxygen (DO) concentration and culture pH are two of the most influential process parameters governing Chinese Hamster Ovary (CHO) cell physiology, metabolic activity, and recombinant protein production during upstream bioprocessing. Precise regulation of these parameters is essential for maximizing cell growth, maintaining viability, minimizing the accumulation of inhibitory metabolites, and ensuring consistent product quality. Despite advances in bioprocess control, the dynamic interactions between DO, pH, cellular metabolism, and recombinant protein expression remain incompletely understood, particularly in high-throughput process development platforms. This study investigates the combined effects of varying dissolved oxygen and pH conditions on CHO cell growth, metabolism, and recombinant protein productivity using the Ambr® 15 high-throughput bioreactor system. Parallel fed-batch cultures will be established under systematically controlled DO and pH setpoints using a factorial experimental design to evaluate their individual and interactive effects on bioprocess performance. Cell growth kinetics, viable cell density, cell viability, glucose consumption, lactate production and consumption, glutamine utilization, ammonia accumulation, osmolality, and recombinant protein titer will be monitored throughout the cultivation period using standard analytical techniques. Where applicable, critical quality attributes, including glycosylation profiles and product integrity, will also be assessed. Multivariate statistical analysis and correlation modeling will be employed to identify significant relationships between environmental conditions, metabolic responses, and productivity outcomes. It is anticipated that optimized DO and pH control strategies will reduce metabolic stress, promote favorable metabolic shifts, extend culture longevity, improve nutrient utilization efficiency, and significantly enhance recombinant protein yield without compromising product quality. The findings will provide mechanistic insights into the influence of microenvironmental conditions on CHO cell metabolism and contribute to the development of more robust, scalable, and data-driven upstream bioprocesses. Ultimately, this research will support Quality by Design (QbD) principles, accelerate process development, improve manufacturing consistency, and provide practical guidance for optimizing industrial production of recombinant therapeutic proteins in mammalian cell culture systems.

Keywords : Chinese Hamster Ovary (CHO) Cells; Dissolved Oxygen; pH Dynamics; Recombinant Protein Production; Ambr® 15 Microbioreactor.

References :

1. Xu, W.J., Lin, Y., Mi, C.L., Pang, J.Y. and Wang, T.Y. (2023). Progress in fed-batch culture for recombinant protein production in CHO cells. Applied Microbiology and Biotechnology, 107(4), 1063–1075.
2. Lam, C., Sargon, A., Diaz, C., Lai, Z., Sangaraju, D., Yuk, I., Barnard, G. and Misaghi, S. (2024). Strategies to improve CHO cell culture performance: Targeted deletion of amino acid catabolism and apoptosis genes paired with growth inhibitor supplementation. Biotechnology Progress, 40(5), e3471.
3. Zakrzewski, R., Lee, K. and Lye, G.J. (2022). Development of a miniature bioreactor model to study the impact of pH and DOT fluctuations on CHO cell culture performance as a tool to understanding heterogeneity effects at large-scale. Biotechnology Progress, 38(4), e3264.
4. Nienow, A.W., Rielly, C.D., Brosnan, K.M., Bargh, N., Lee, K., Coopman, K. and Hewitt, C.J. (2013). The physical characterisation of a microscale parallel bioreactor platform with an industrial CHO cell line expressing an IgG4. Biochemical Engineering Journal, 76, 25–36.
5. Sartorius (2026). Ambr® 15 Cell Culture: Advanced Microbioreactor System. Sartorius Stedim Biotech.
6. Restelli, V., Wang, M.D., Huzel, N., Ethier, M., Perreault, H. and Butler, M. (2006). The effect of dissolved oxygen on the production and the glycosylation profile of recombinant human erythropoietin produced from CHO cells. Biotechnology and Bioengineering, 94(3), 481–494.
7. Lee, A.P. et al. (2021). Multi-omics profiling of a CHO cell culture system unravels the effect of culture pH on cell growth, antibody titer, and product quality. Biotechnology and Bioengineering, 118(11), 4305–4316.
8. International Council for Harmonisation (ICH) (2009). Q8(R2): Pharmaceutical Development. U.S. Food and Drug Administration.
9. Kim, J.Y., Kim, Y.G. and Lee, G.M. (2012). CHO cells in biotechnology for production of recombinant proteins: Current state and further potential. Applied Microbiology and Biotechnology, 93(3), 917–930.
10. Fischer, S., Handrick, R. and Otte, K. (2015). The art of CHO cell engineering: A comprehensive retrospect and future perspectives. Biotechnology Advances, 33(8), 1878–1896.
11. Glinšek, K., Bozovičar, K. and Bratkovič, T. (2023). CRISPR technologies in Chinese Hamster Ovary cell line engineering. International Journal of Molecular Sciences, 24(9), 8144..
12. Kirk, T.V. and Szita, N. (2013). Oxygen transfer characteristics of miniaturized bioreactor systems. Biotechnology and Bioengineering, 110(4), 1005–1019.
13. Pappenreiter, M., Sissolak, B., Sommeregger, W. and Striedner, G. (2019). Oxygen uptake rate soft-sensing via dynamic kLa computation: Cell volume and metabolic transition prediction in mammalian bioprocesses. Frontiers in Bioengineering and Biotechnology, 7, 195.
14. Chevallier, V., Andersen, M.R. and Malphettes, L. (2020). Oxidative stress-alleviating strategies to improve recombinant protein production in CHO cells. Biotechnology and Bioengineering, 117(4), 1172–1186.
15. Anane, E., Knudsen, I.M. and Wilson, G.C. (2021). Scale-down cultivation in mammalian cell bioreactors: The effect of bioreactor mixing time on the response of CHO cells to dissolved oxygen gradients. Biochemical Engineering Journal, 166, 107870.
16. Jiang, R., Chen, H. and Xu, S. (2018). pH excursions impact CHO cell culture performance and antibody N-linked glycosylation. Bioprocess and Biosystems Engineering, 41, 1731–1741.
17. Hoshan, L., Jiang, R., Moroney, J., Bui, A., Zhang, X., Hang, T.-C. and Xu, S. (2019). Effective bioreactor pH control using only sparging gases. Biotechnology Progress, 35(1), e2743.
18. Coulet, M., Kepp, O., Kroemer, G. and Basmaciogullari, S. (2022). Metabolic profiling of CHO cells during the production of biotherapeutics. Cells, 11(12), 1929.
19. Mulukutla, B.C., Gramer, M. and Hu, W.-S. (2012). On metabolic shift to lactate consumption in fed-batch culture of mammalian cells. Metabolic Engineering, 14(2), 138–149.
20. Rajendra, Y., Kiseljak, D., Baldi, L., Hacker, D.L. and Wurm, F.M. (2012). Reduced glutamine concentration improves protein production in growth-arrested CHO-DG44 and HEK-293E cells. Biotechnology Letters, 34(4), 619–626.
21. Chen, P. and Harcum, S.W. (2005). Effects of amino acid additions on ammonium stressed CHO cells. Journal of Biotechnology, 117(3), 277–286.
22. Singh, R., Fatima, E., Thakur, L., Singh, S., Ratan, C. and Kumar, N. (2024). Advancements in CHO metabolomics: techniques, current state and evolving methodologies. Frontiers in Bioengineering and Biotechnology, 12, 1347138.
23. Jones, J., Kindembe, D., Branton, H., Lawal, N., Lopez Montero, E., Mack, J., Shi, S., Patton, R. and Montague, G. (2023). Improved control strategies for the environment within cell culture bioreactors. Food and Bioproducts Processing, 138, 209–220.
24. Obrstar, D., Mandelc, S., Stojković, S., Francky, A., Bojić, L. and Javornik, B. (2016). Responses of CHO cell lines to increased pCO₂ at normal (37 °C) and reduced (33 °C) culture temperatures. Journal of Biotechnology, 219, 98–109.
25. Hippach, M.B., Schwartz, I., Pei, J., Huynh, J., Kawai, Y. and Zhu, M.M. (2018). Fluctuations in dissolved oxygen concentration during a CHO cell culture process affects monoclonal antibody productivity and the sulfhydryl-drug conjugation process. Biotechnology Progress, 34(6), 1427–1437.
26. Ha, T.K., Kim, D., Kim, C.L., Grav, L.M. and Lee, G.M. (2022). Factors affecting the quality of therapeutic proteins in recombinant Chinese hamster ovary cell culture. Biotechnology Advances, 54, 107831.
27. Zalai, D., Hevér, H., Lovász, K., Molnár, D., Wechselberger, P., Hofer, A., Párta, L., Putics, Á. and Herwig, C. (2016). A control strategy to investigate the relationship between specific productivity and high-mannose glycoforms in CHO cells. Applied Microbiology and Biotechnology, 100, 7011–7024.
28. Gomez, N., Subramanian, J., Ouyang, J., Nguyen, M.D.H., Hutchinson, M., Sharma, V.K., Lin, A.A. and Yuk, I.H. (2012). Culture temperature modulates aggregation of recombinant antibody in CHO cells. Biotechnology and Bioengineering, 109(1), 125–136.
29. Geng, S.L., Zhao, X.J., Zhang, X., Zhang, J.H., Mi, C.L. and Wang, T.Y. (2024). Recombinant therapeutic proteins degradation and overcoming strategies in CHO cells. Applied Microbiology and Biotechnology, 108, 182.
30. Rameez, S., Mostafa, S.S., Miller, C. and Shukla, A.A. (2014). High-throughput miniaturized bioreactors for cell culture process development: Reproducibility, scalability, and control. Biotechnology Progress, 30(3), 718–727.
31. Janakiraman, V., Kwiatkowski, C., Kshirsagar, R., Ryll, T. and Huang, Y.M. (2015). Application of high-throughput mini-bioreactor system for systematic scale-down modeling, process characterization, and control strategy development. Biotechnology Progress, 31(6), 1623–1632.
32. Alsayyari, A.A., Pan, X., Dalm, C., van der Veen, J.W., Vriezen, N. and Martens, D.E. (2018). Transcriptome analysis for the scale-down of a CHO cell fed-batch process. Journal of Biotechnology, 279, 61–72.
33. Goldrick, S., Holmes, W., Bond, N.J., Lewis, G., Kuiper, M., Turner, R., Suzanne, S. et al. (2017). Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody–peptide fusion. Biotechnology and Bioengineering, 114(10), 2222–2234.
34. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) (2023). ICH Q9(R1): Quality Risk Management. Geneva: ICH.
35. Brunner, M., Fricke, J., Kroll, P. and Herwig, C. (2017). Investigation of the interactions of critical scale-up parameters (pH, pO₂ and pCO₂) on CHO batch performance and critical quality attributes. Bioprocess and Biosystems Engineering, 40(2), 251–263.
36. Trummer, E., Fauland, K., Seidinger, S., Schriebl, K., Lattenmayer, C., Kunert, R., Vorauer-Uhl, K., Weik, R., Borth, N., Katinger, H. and Müller, D. (2006). Process parameter shifting: Part I. Effect of DOT, pH, and temperature on the performance of Epo-Fc expressing CHO cells cultivated in controlled batch bioreactors. Biotechnology and Bioengineering, 94(6), 1033–1044.
37. Pan, X., Streefland, M., Dalm, C., Wijffels, R.H. and Martens, D.E. (2017). Selection of chemically defined media for CHO cell fed-batch culture processes. Cytotechnology, 69(1), 39–56.
38. Kuwae, S., Miyakawa, I. and Doi, T. (2018). Development of a chemically defined platform fed-batch culture media for monoclonal antibody-producing CHO cell lines with optimized choline content. Cytotechnology, 70(3), 939–948.
39. Velugula-Yellela, S.R., Kohnhorst, C., Powers, D.N. and Agarabi, C. (2018). Use of high-throughput automated microbioreactor system for production of model IgG1 in CHO cells. Journal of Visualized Experiments, 139, 58231.
40. Velugula-Yellela, S.R., Williams, A., Trunfio, N. and Agarabi, C. (2018). Impact of media and antifoam selection on monoclonal antibody production and quality using a high throughput micro-bioreactor system. Biotechnology Progress, 34(1), 262–270.
41. Idowu, O. S., Idoko, D. O., Ogundipe, S. O., and Mensah, E. (2025). Optimizing SDS-PAGE for accurate protein characterization in nutritional research and food quality assessment. International Journal of Innovative Science and Research Technology, 10(1).
42. Donkor, F., Okafor, M. N., and Enyejo, J. O. (2025). Exploring metabolomics-guided authentication of plant-based meat alternatives supporting regulatory standards and consumer health protection. International Journal of Innovative Science and Research Technology, 10(10).
43. Imoh, P. O. (2023). Impact of gut microbiota modulation on autism-related behavioral outcomes via metabolomic and microbiome-targeted therapies. International Journal of Scientific Research and Modern Technology, 2(8).
44. Michael, I. A., Olade, A. D., Idoko, D. O., Yiga, V., Galadima, D., and Kwekutsu, E. (2025). Exploring microbial diversity in air and water and its role in enhancing agricultural resilience and urban ecosystem health. International Journal of Innovative Science and Research Technology, 10(1).
45. Idowu, O. S., Ayoola, V. O., Adegbola, F., and Adeyeye, Y. (2024). Air, water, and soil microbiomes as catalysts for smart agriculture, urban ecosystem revitalization, climate adaptation, and public health advancements. International Journal of Scientific Research and Modern Technology, 3(5).
46. Omoche, I. A., Amana, O., and Ebah, E. E. (2025). Impact of seasonal variation on the microbiological and physicochemical quality of stored rainwater in underground tanks. International Journal of Innovative Science and Research Technology, 10(2).
47. Muriithi, B. et al. (2025). High-efficiency analytical Protein A columns for monoclonal antibody quantification in bioprocessing. Published study indexed in PubMed.
48. Xing, Z., Kenty, B.M., Li, Z.J. and Lee, S.S. (2009). Scale-up analysis for a CHO cell culture process in large-scale bioreactors. Biotechnology and Bioengineering, 103(4), 733–746.
49. Gaugler, L., Hofmann, S., Schlüter, M. and Takors, R. (2024). Mimicking CHO large-scale effects in the single multicompartment bioreactor: A new approach to access scale-up behavior. Biotechnology and Bioengineering, 121(4), 1244–1256.
50. Idoko, F. A., Markus, L., and Henry, E. (2025). Assessing water-quality parameters for aquaculture in Nigeria: A case study of surface and groundwater in Makurdi. International Journal of Research Publication and Reviews, 6(2), 4758–4775.
51. Atalor, S. I., Raphael, F. O., and Enyejo, J. O. (2023). Wearable biosensor integration for remote chemotherapy monitoring in decentralized cancer-care models. International Journal of Scientific Research in Science and Technology, 10(3).
52. Adeniyi, M., Ayoola, V. B., Samuel, T. E., and Awosan, W. (2024). Artificial intelligence-driven wearable electronics and smart nanodevices for continuous cancer monitoring and enhanced diagnostic accuracy. International Journal of Scientific Research and Modern Technology, 3(11)
53. Okpanachi, A. T., Igba, E., Imoh, P. O., Dzakpasu, N. H., and Nyaledzigbor, M. (2025). Leveraging digital biomarkers and advanced data analytics in medical laboratories to enhance early detection and diagnostic accuracy in cardiovascular diseases. International Journal of Scientific Research in Science and Technology, 12.
54. Imoh, P. O., and Idoko, I. P. (2022). Gene-environment interactions and epigenetic regulation in autism etiology through multi-omics integration and computational biology approaches. International Journal of Scientific Research and Modern Technology, 1(8), 1–16.
55. Atalor, S. I. (2022). Data-driven cheminformatics models for predicting the bioactivity of natural compounds in oncology. International Journal of Scientific Research and Modern Technology, 1(1), 65–76.
56. Animasaun, J. B., Ijiga, O. M., Ayoola, V. B., and Enyejo, L. A. (2024). Evaluating the stability of cannabinoid extracts following different solvent evaporation conditions: A GC-MS/LC-MS degradation profiling study. International Journal of Scientific Research and Modern Technology, 3(1), 55–70.
57. Animasaun, J. B., Ijiga, O. M., Ayoola, V. B., and Enyejo, L. A. (2024). Impact of solvent polarity on volatile and non-volatile cannabinoid recovery: A multivariate GC-MS/LC-MS extraction optimisation study. International Journal of Scientific Research and Modern Technology, 3(1), 40–54.
58. Animasaun, J. B., Ijiga, O. M., Ayoola, V. B., and Enyejo, L. A. (2026). Development of a rapid GC-MS workflow for simultaneous quantification of volatile terpenes and cannabinoids in industrial hemp extracts. International Journal of Innovative Science and Research Technology, 11(1), 1155–1168.
59. Animasaun, J. B., Ijiga, O. M., Ayoola, V. B., and Enyejo, L. A. (2026). Application of FT-IR spectroscopy for differentiating hemp stem and bud chemical composition: A rapid screening approach. Chemistry and Material Sciences Research Journal, 5(1).
60. Ijiga, A. C., Igbede, M. A., Ukaegbu, C., Olatunde, T. I., Olajide, F. I., and Enyejo, L. A. (2024). Precision healthcare analytics: Integrating machine learning for automated image interpretation, disease detection, and prognosis prediction. World Journal of Biology Pharmacy and Health Sciences, 18(1), 336–354.
61. Oyebanji, O. S., Apampa, A. R., Afolabi, O., Eromonsei, S. O., and Babalola, A. (2024). Performance benchmarking of convolutional neural networks and ensemble machine-learning techniques for automated mammographic breast-cancer detection. World Journal of Advanced Engineering Technology and Sciences, 12(2), 808–833.
62. Oyebanji, O. S., Apampa, A. R., Idoko, I. P., Babalola, A., Ijiga, O. M., Afolabi, O., and Michael, C. I. (2024). Enhancing breast-cancer detection accuracy through transfer learning: A case study using EfficientNet. World Journal of Advanced Engineering Technology and Sciences, 13(1), 285–318.
63. Idoko, D. O., Adegbaju, M. M., Nduka, I., Okereke, E. K., Agaba, J. A., and Ijiga, A. C. (2024). Enhancing early detection of pancreatic cancer by integrating artificial intelligence with advanced imaging techniques. Magna Scientia Advanced Biology and Pharmacy, 12(2), 51–83.
64. Idoko, P. I., David-Olusa, A., Badu, S. G., Okereke, E. K., Agaba, J. A., and Bashiru, O. (2024). The dual impact of artificial intelligence and renewable energy in enhancing medicine for diagnostics, drug discovery, and public health. Magna Scientia Advanced Biology and Pharmacy, 12(2), 99–127.
65. Idoko, D. O., Adeniyi, M., Senejani, M. N., Erondu, O. F., and Adeyeye, Y. (2024). Nanoparticle-assisted cancer imaging and targeted drug delivery for early-stage tumour detection. International Journal of Innovative Science and Research Technology, 9(11).
66. Darko, D., Kwekutsu, E., and Idoko, I. P. (2025). Synergistic effects of phytochemicals in combating chronic diseases with insights into molecular mechanisms and nutraceutical development. International Journal of Innovative Science and Research Technology, 10(3).
67. Imoh, P. O., Adeniyi, M., Ayoola, V. B., and Enyejo, J. O. (2024). Advancing early autism diagnosis using multimodal neuroimaging and artificial intelligence-driven biomarkers. International Journal of Scientific Research and Modern Technology, 3(6), 40–56.
68. Gaye, A., Atanda, O. D., Ibrahim, A. I., Idoko, I. P., and Adeoye, A. F. (2024). Numerical optimisation and sensitivity analysis of a fractional-order hepatitis B virus transmission model under varying vaccination and memory parameters. International Journal of Scientific Research and Modern Technology, 3(6), 70–86.
69. Gaye, A. (2025). Predictive modelling and statistical analysis of hypertension cases at a small teaching hospital. Iconic Research and Engineering Journals, 8(11).
70. Gaye, A., Bamigwojo, O. V., Idoko, I. P., and Adeoye, A. F. (2025). Modelling hepatitis B virus transmission dynamics using the Atangana fractional-order network approach. International Journal of Innovative Science and Research Technology, 10(4).
71. Onuh, J. E., Idoko, I. P., Igbede, M. A., Olajide, F. I., Ukaegbu, C., and Olatunde, T. I. (2024). Harnessing synergy between biomedical and electrical engineering: A comparative analysis of healthcare advancement in Nigeria and the USA. World Journal of Advanced Engineering Technology and Sciences, 11(2), 628–649.
72. James, U. U., & Akujuobi, C. M. (2026). Multiscale signal processing framework for Zero Trust security in next-generation wireless networks. International Journal of Wireless and Mobile Networks, 18(2/3), 1–21. https://doi.org/10.5121/ijwmn.2026.18301
73. Idoko, D. O., Mbachu, O. E., Babalola, I. N. O., Felix, E. O., Dada-Abidakun, O., & Alemoh, P. O. (2024). The influence of prenatal vitamin use and community health programs on reducing teratogenic medication exposure and improving perinatal nutrition among African American adolescents with limited access to healthcare. International Journal of Scientific Research and Modern Technology, 3(10). https://doi.org/10.38124/ijsrmt.v3i10.62
74. Ijiga, A. C., Balogun, T. K., Ahmadu, E. O., Klu, E., Olola, T. M., & Addo, G. (2024). The role of the United States in shaping youth mental health advocacy and suicide prevention through foreign policy and media in conflict zones. Magna Scientia Advanced Research and Reviews, 12(1), 202–218. https://doi.org/10.30574/msarr.2024.12.1.0174
75. Ijiga, A. C., Balogun, T. K., Sariki, A. M., Klu, E., Ahmadu, E. O., & Olola, T. M. (2024). Investigating the influence of domestic and international factors on youth mental health and suicide prevention in societies at risk of autocratization. Iconic Research and Engineering Journals, 8(5), 93–106.
76. Ijiga, A. C., Enyejo, L. A., Odeyemi, M. O., Olatunde, T. I., Olajide, F. I., & Daniel, D. O. (2024). Integrating community-based partnerships for enhanced health outcomes: A collaborative model with healthcare providers, clinics, and pharmacies across the USA. Open Access Research Journal of Biology and Pharmacy, 10(2), 81–104. https://doi.org/10.53022/oarjbp.2024.10.2.0015
77. Ilesanmi, M. O., Okoh, O. F., Balogun, S. A., & Ijiga, O. M. (2024). Data-driven portfolio optimization for utility-scale solar, wind, and battery energy storage systems (BESS): Integrating performance analytics with investor EBITDA targets. International Journal of Scientific Research and Modern Technology, 3(11), 141–155. https://doi.org/10.38124/ijsrmt.v3i11.943
78. Adewale, L. D. (2026). Digital evidence chains for PPAP assurance: AR-guided data capture, AI-verified documentation and continuous audit automation for secure multi-tier supplier traceability in Industry 4.0 manufacturing. International Journal of Multidisciplinary Evolutionary Research, 7(1), 43–55. https://doi.org/10.54660/IJMER.2026.7.1.43-55
79. Adewale, L. D. (2026). Smart Factories, Smarter Evidence: Reinventing Quality Assurance for U.S. Manufacturing Competitiveness International Journal of Multidisciplinary Futuristic Development Vol 7 Iss. 1. DOI: https://doi.org/10.54660/IJMFD.2026.7.1.09-18
80. Ajiboye, A. S., Balogun, T. K., Imoh, P. O., Ijiga, A. C., Olola, T. M., & Ahmadu, E. O. (2025). Understanding the impact of social media on mental health in autistic youth and expanding access to culturally responsive behavioral health services in underserved communities. International Journal of Scientific Research in Humanities and Social Sciences, 2(3), 33–56. https://doi.org/10.32628/IJSRHSS25234
81. Ajiboye, A. S., Balogun, T. K., Peter-Anyebe, A. C., Ahmadu, E. O., & Olola, T. M. (2025). Investigating the epigenetic and psychological effects of community gun violence and mass shootings on youth and families through a transgenerational trauma lens. Social Values and Society, 7(2), 74–82. https://doi.org/10.26480/svs.02.2025.74.82
82. Anokwuru, E. A., Omachi, A., & Enyejo, J. O. (2024). Automation-enabled RFI/RFP market intelligence platforms: Redefining data-driven business development in global pharmaceutical markets. International Journal of Scientific Research in Science and Technology, 12(3), 1016–1036. https://doi.org/10.32628/IJSRST54310301.