Phytochemistry-five decades of research in Africa: A bibliometric analysis

Document Type : Original Article


1 Department of Pharmaceutical Microbiology, Faculty of Pharmaceutical Sciences, Ahmadu Bello University, Zaria, Kaduna, Nigeria

2 Department of Pharmacognosy and Ethno Pharmacy, Faculty of Pharmaceutical Sciences, Usman Danfodiyo University, Sokoto, Nigeria

3 Department of Library and Information Science, Usmanu Danfodiyo University, Sokoto, Nigeria


Background and aims: This study used bibliometric tools to quantitatively achieve a structural overview of research characteristics and potentials of phytochemistry in Africa within the last five decades (1970–2022).
Methods: A total of 2662 phytochemistry related publications from 822 sources published between 1970 and 2022 were identified from Dimensions database and subjected to bibliometrics analysis using Bibliometrix package and VOSviewer software.
Results: The publications span 9775 authors, 30 African countries, and 1142 organizations. In terms of research themes, text mining for high- relevance/frequency keywords revealed that “Phenol” (7.3182) and “Flavonoid” (5.3637) were the most cited plant metabolites among all publications. The key noun-phrases for solvents were “Aqueous”, “methanol” and “ethanol”. The most cited terms in plant family were “Tamaricaceae” (4.9575) and “Lamiaceae” (4.9273); plant species, “Acacia nilotica” (4.4909) and “Aloe barbadensis” (4.3946); bacteria strains, “Klebsiella pneumoniae” (4.6932) and “Staphylococcus aureus” (3.3538); fungal species, “Aspergillus” (2.7228) and “Penicillium notatum” (2.4054); Viral strains, “Human immune deficiency virus” (3.3482) and “Hepatitis C virus” (3.2796); parasites, “Plasmodium” (12.0576) and “Leishmania sp” (8.3602). The most cited methods of detection and analysis of phytochemicals were “Gas chromatography mass spectrometry” (1.7256) and “High performance liquid chromatography” (1.6889). Interactive Site Suitability Models, orthogonal partial least squares discriminant analysis (OPLS-DA), plant organically bound tritium (OBT), and quantitative structure–activity relationship models (QSAR models) were the most cited models. Inequality in the geographical distribution of publication output was the source of concern.
Conclusion: A drive towards computational phytochemistry could be detected as an important change in research focus.


1. Egbuna C, Ifemeje JC, Kryeziu TL, Mukherjee M, Shah H, Narasimha Rao GM, et al. Introduction to phytochemistry. In: Phytochemistry. Apple Academic Press; 2018. p. 3-36. doi: 10.1201/9780429426223-1. 
2. Phytochemical Society of North America. Phytochemistry. 2010;71(14–15):I. doi:10.1016/s0031-9422(10)00327-4 
3. Pang Z, Chen J, Wang T, Gao C, Li Z, Guo L, et al. Linking plant secondary metabolites and plant microbiomes: a review. Front Plant Sci. 2021;12:621276. doi: 10.3389/fpls.2021.621276. 
4. Saxena M, Saxena J, Nema R, Singh D, Gupta A. Phytochemistry of medicinal plants. J Pharmacogn Phytochem. 2013;1(6):168-82. 
5. Divekar PA, Narayana S, Divekar BA, Kumar R, Gadratagi BG, Ray A, et al. Plant secondary metabolites as defense tools against herbivores for sustainable crop protection. Int J Mol Sci. 2022;23(5):2690. doi: 10.3390/ijms23052690. 
6. Phillipson JD. Phytochemistry and pharmacognosy. Phytochemistry. 2007;68(22-24):2960-72. doi: 10.1016/j. phytochem.2007.06.028. 
7. Bondre S, Yadav U. Automated flower species identification by using deep convolution neural network. In: Satapathy SC, Peer P, Tang J, Bhateja V, Ghosh A, eds. Intelligent Data Engineering and Analytics. Singapore: Springer; 2022. p. 1-10. doi: 10.1007/978-981-16-6624-7_1. 
8. Linder HP. The evolution of African plant diversity. Front Ecol Evol. 2014;2:38. doi: 10.3389/fevo.2014.00038. 
9. Asaduzzaman M, Asao T. Introductory chapter: phytochemicals and disease prevention. In: Asao T, Asaduzzaman M, eds. Phytochemicals: Source of Antioxidants and Role in Disease Prevention. IntechOpen; 2018. doi: 10.5772/ intechopen.81877. 
10. Kasilo OMJ, Wambebe C, Nikiema JB, Nabyonga-Orem J. Towards universal health coverage: advancing the development and use of traditional medicines in Africa. BMJ Glob Health. 2019;4(Suppl 9):e001517. doi: 10.1136/ bmjgh-2019-001517. 
11. Ozioma EO, Chinwe OA. Herbal medicines in African traditional medicine. In: Builders PF, ed. Herbal Medicine. IntechOpen; 2019. doi: 10.5772/intechopen.80348. 
12. Adeiza S, Shuaibu A. Trends in monkeypox research: a sixty year bibliometric analysis. Microbes Infect Dis. 2022;3(3):500- 13. doi: 10.21608/mid.2022.147680.1334. 
13. Dimensions AI -- The most advanced scientific research database [Internet]. Dimensions. Available from: https://www. Accessed May 12, 2022. 
14. Hook DW, Porter SJ, Herzog C. Dimensions: building context for search and evaluation. Front Res Metr Anal. 2018;3:23. doi: 10.3389/frma.2018.00023. 
15. Aria M, Cuccurullo C. bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr. 2017;11(4):959-75. doi: 10.1016/j.joi.2017.08.007. 
16. VOSviewer - Visualizing scientific landscapes [Internet]. VOSviewer. Available from: Accessed May 12, 2022. 
17. Perianes-Rodriguez A, Waltman L, van Eck NJ. Constructing bibliometric networks: a comparison between full and fractional counting. J Informetr. 2016;10(4):1178-95. doi: 10.1016/j.joi.2016.10.006. 
18. Huang T, Wu H, Yang S, Su B, Tang K, Quan Z, et al. Global trends of researches on sacral fracture surgery: a bibliometric study based on VOSviewer. Spine (Phila Pa 1976). 2020;45(12):E721-E8. doi: 10.1097/brs.0000000000003381. 
19. Qiu J, Zhao R, Yang S, Dong K. Author distribution of literature information: Lotka’s law. In: Qiu J, Zhao R, Yang S, Dong K, eds. Informetrics: Theory, Methods and Applications. Singapore: Springer; 2017. p. 145-83. doi: 10.1007/978-981- 10-4032-0_6. 
20. Sudhier KG. Application of Bradford’s law of scattering to the physics literature: a study of doctoral theses citations at the Indian Institute of Science. DESIDOC J Libr Inf Technol. 2020;30(2):3-14. doi: 10.14429/djlit.30.3. 
21. van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84(2):523-38. doi: 10.1007/s11192-009-0146-3. 
22. Zhang W, Xie R, Wang Q, Yang Y, Li J. A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback– Leibler divergence. Decis Support Syst. 2022;157:113765. doi: 10.1016/j.dss.2022.113765. 
23. Boyack KW, van Eck NJ, Colavizza G, Waltman L. Characterizing in-text citations in scientific articles: a large-scale analysis. J Informetr. 2018;12(1):59-73. doi: 10.1016/j. joi.2017.11.005. 
24. Zhao Z, Pan X, Hua W. Comparative analysis of the research productivity, publication quality, and collaboration patterns of top ranked library and information science schools in China and the United States. Scientometrics. 2021;126(2):931-50. doi: 10.1007/s11192-020-03796-9. 
25. Meo SA, Al Masri AA, Usmani AM, Memon AN, Zaidi SZ. Impact of GDP, spending on R&D, number of universities and scientific journals on research publications among Asian countries. PLoS One. 2013;8(6):e66449. doi: 10.1371/journal. pone.0066449. 
26. Kreiman G, Maunsell JH. Nine criteria for a measure of scientific output. Front Comput Neurosci. 2011;5:48. doi: 10.3389/fncom.2011.00048. 
27. Albanna B, Handl J, Heeks R. Publication outperformance among global South researchers: an analysis of individual-level and publication-level predictors of positive deviance. Scientometrics. 2021;126(10):8375-431. doi: 10.1007/ s11192-021-04128-1. 
28. George TT, Obilana AO, Oyenihi AB, Rautenbach FG. Moringa oleifera through the years: a bibliometric analysis of scientific research (2000-2020). S Afr J Bot. 2021;141:12-24. doi: 10.1016/j.sajb.2021.04.025. 
29. Dou H, Kister J. Research and development on Moringa oleifera–comparison between academic research and patents. World Pat Inf. 2016;47:21-33. doi: 10.1016/j. wpi.2016.09.001. 
30. Ullah M, Shahid A, ud Din I, Roman M, Assam M, Fayaz M, et al. Analyzing interdisciplinary research using co-authorship networks. Complexity. 2022;2022:2524491. doi: 10.1155/2022/2524491. 
31. Zyoud SH, Zyoud AH. Coronavirus disease-19 in environmental fields: a bibliometric and visualization mapping analysis. Environ Dev Sustain. 2021;23(6):8895-923. doi: 10.1007/s10668-020-01004-5. 
32. Smith DR. Historical development of the journal impact factor and its relevance for occupational health. Ind Health. 2007;45(6):730-42. doi: 10.2486/indhealth.45.730. 
33. van Nunen K, Li J, Reniers G, Ponnet K. Bibliometric analysis of safety culture research. Saf Sci. 2018;108:248-58. doi: 10.1016/j.ssci.2017.08.011. 
34. Ioannidis JPA, Bendavid E, Salholz-Hillel M, Boyack KW, Baas J. Massive Covidization of Research Citations and the Citation Elite. medRxiv [Preprint]. January 25, 2022 [Cited 2022 May 15]. Available from: content/10.1101/2022.01.24.22269775v1.full. 
35. Walter G, Bloch S, Hunt G, Fisher K. Counting on citations: a flawed way to measure quality. Med J Aust. 2003;178(6):280- 1. doi: 10.5694/j.1326-5377.2003.tb05196.x. 
36. Chua SK, Qureshi AM, Krishnan V, Pai DR, Kamal LB, Gunasegaran S, et al. The impact factor of an open access journal does not contribute to an article’s citations. F1000Res. 2017;6:208. doi: 10.12688/f1000research.10892.1. 
37. Whipple EC, Dixon BE, McGowan JJ. Linking health information technology to patient safety and quality outcomes: a bibliometric analysis and review. Inform Health Soc Care. 2013;38(1):1-14. doi: 10.3109/17538157.2012.678451. 
38. Mahomoodally MF. Traditional medicines in Africa: an appraisal of ten potent African medicinal plants. Evid Based Complement Alternat Med. 2013;2013:617459. doi: 10.1155/2013/617459. 
39. Mulani MS, Kamble EE, Kumkar SN, Tawre MS, Pardesi KR. Emerging strategies to combat ESKAPE pathogens in the era of antimicrobial resistance: a review. Front Microbiol. 2019;10:539. doi: 10.3389/fmicb.2019.00539. 
40. Gourama H. Foodborne pathogens. In: Demirci A, Feng H, Krishnamurthy K, eds. Food Safety Engineering. Cham: Springer; 2020. p. 25-49. doi: 10.1007/978-3-030-42660-6_2. 
41. Bhatnagar N. Phytonanotechnology for curbing the menace of MDR bacteria: a review. Mater Today Proc. 2021;43:3322-4. doi: 10.1016/j.matpr.2021.02.422. 
42. Fisher MC, Alastruey-Izquierdo A, Berman J, Bicanic T, Bignell EM, Bowyer P, et al. Tackling the emerging threat of antifungal resistance to human health. Nat Rev Microbiol. 2022;20(9):557-71. doi: 10.1038/s41579-022-00720-1. 
43. Tungmunnithum D, Drouet S, Kabra A, Hano C. Enrichment in antioxidant flavonoids of stamen extracts from Nymphaea lotus L. using ultrasonic-assisted extraction and macroporous resin adsorption. Antioxidants (Basel). 2020;9(7):576. doi: 10.3390/antiox9070576. 
44. Tabe NN, Ushie OA, Jones BB, Kendenson AC, Muktar M, Ojeka CU. Phytochemical analysis of methanolic extract of mistletoe leaf. Int J Adv Res Chem Sci. 2018;5(7):7-11. doi: 10.20431/2349-0403.0507002. 
45. Sarker SD, Nahar L. An introduction to computational phytochemistry. In: Sarker SD, Nahar L, eds. Computational Phytochemistry. Elsevier; 2018. p. 1-41. doi: 10.1016/b978- 0-12-812364-5.00001-8.