ANTIBACTERIAL STUDIES OF ESSENTIAL OIL FROM THE FRESH LEAF OF LEMON GRASS CYMBOPOGON CITRATUS
DOI:
https://doi.org/10.33003/fjs-2024-0806-3098Keywords:
Lemon grass, Essential oil, Organisms, Bacteria, Zone of inhibitionAbstract
Cymbopogon citratus, belongs to Gramineae family. This study used a microwave-assisted hydro-distillation process to extract lemongrass essential oil and examined its antimicrobial qualities. Physical characteristics of the essential oils included a yellow color, a yield percentage of 4.67%, solubility in trichloromethane, and a lemony aroma. The disc diffusion method was used to assess the oil's effectiveness. Pseudomonas aeruginosa, Klebsiella oxytoca, Staphylococcus aureus, and Escherichia coli were all susceptible to the oil's concentration-dependent antibacterial qualities. 5.72 mmL/disc was the oil's most effective concentration against E. coli, while 1.43 mmL/disc was its least effective. The zone of inhibition shrank as the concentration of oil per disc dropped, indicating that the oil's activity against all species was concentration-dependent. At concentrations of 5.72 mmL/disc, 2.86 mmL/disc, and 1.43 mmL/disc, respectively, the zone of inhibition for E. coli was 24, 11.3, and 7.7 mm. For other creatures, the pattern is the same. Pseudomonas aeruginosa had the smallest zone of inhibition, measuring 7.0, 7.0, and 5.7 mm at concentrations of 5.72 mmL/disc, 2.86 mmL/disc, and 1.43 mmL/disc, respectively. Staphylococcus aureus was 13.3, 10.3, and 9.0 mm at concentrations of 5.72 mmL/disc, 2.86 mmL/disc, and 1.43 mmL/disc, while Klebsiella oxytoca was 11.3, 9.7, and 9.0 mm at concentrations of 5.72 mmL/disc, 2.86 mmL/disc, and 1.43 mmL/disc, respectively. These results imply that lemongrass essential oil may be a viable natural substitute for synthetic antibiotics, with potential uses in medical and food preservation. It is advised that additional bioassays be conducted and contrasted with the results obtained from alternative...
References
Abinaya, R., Aishwaryaa, P., Baavana, S., and Selvi, N. T. (2016, July). Automatic sentiment analysis of user reviews. In 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 158-162. IEEE.
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., and Passonneau, R. J. (2011). Sentiment analysis of twitter data. In Proceedings of the workshop on language in social media (LSM 2011), 30-38.
Dey, L., Chakraborty, S., Biswas, A., Bose, B., and Tiwari, S. (2016). Sentiment analysis of review datasets using naive bayes and k-nn classifier. International Journal of Information Engineering and Electronic Business (IJIEEB), 8(4), 54-62, https://doi.org/10.5815/ijieeb.2016.04.07
Fang, X., and Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big data, 2, 1-14.
Hemalatha, I., Varma, G. P. S., and Govardhan, A. (2014). Automated sentiment analysis system using machine learning algorithms. IJRCCT, 3(3), 300-303.
Korde, V., and Mahender, C. N. (2012). Text classification and classifiers: A survey. International Journal of Artificial Intelligence and Applications, 3(2), 85.
Liu, Bing. (2012). Sentiment analysis and opinion mining, Morgan and Claypool publishers, May 2012.
Mohan, V., and Venu, S. H. (2016). Sentiment analysis applied to airline feedback to boost customers Endearment. International Journal of Applied and Physical Sciences, 2(2), 51-58
Narayanan, V., Arora, I., and Bhatia, A. (2013). Fast and accurate sentiment classification using an enhanced Naive Bayes model. In Intelligent Data Engineering and Automated LearningIDEAL 2013: 14th International Conference, IDEAL 2013, Hefei, China, October 20-23, 2013. Proceedings 14, 194-201, Springer Berlin Heidelberg.
Orie C.E, Egwali A.O. Amadin F. I. (2023). Machine Learning Based Sentiment of Tweet Data. 5th Annual Conference of the Society for Forensic and Analytical Scientist Nigeria.
Pang, B., and Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in information retrieval, 2(12), 1-135.
Pang, B., Lee, L., and Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques, Proc. 2002 Conf. on Empirical Methods in Natural Language Processing (EMNLP).
Prof. (Mr.) Prashant Kanade, Lakhan Rangwani, Pritee Wadhwa, Jitesh Watwani and Nitin Hazarani (2019). Automatic Sentiment Analysis of User Reviews, International Journal of Information and Computation Technology, 9(1), 5-10
Rai, A., and Jaiswal, K. (2017). Sentiment analysis by using unsupervised comment summarization. International Research Journal of Engineering and Technology, 4(5), 1105-1109.
Ramdhani, S. L., Andreswari, R., and Hasibuan, M. A. (2018, November). Sentiment analysis of product reviews using naive bayes algorithm: A case study. In 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT), 123-127, IEEE.
Shah, A. (2021). Sentiment analysis of product reviews using supervised learning. Reliability: Theory and Applications, 16(1), 243-253.
Shukla, R., and Mishra, N. (2016). Framework for Sentiment Analysis of Twitter Post. International Journal of Innovative Research in Science, Engineering and Technology, 5(3).
Singla, Z., Randhawa, S., and Jain, S. (2017). Sentiment analysis of customer product reviews using machine learning. In 2017 international conference on intelligent computing and control (I2C2), 1-5, IEEE.
Suchdev, R., Kotkar, P., Ravindran, R., and Swamy, S. (2014). Twitter sentiment analysis using machine learning and knowledge-based approach. International Journal of Computer Applications, 103(4).
Umarani, V., Julian, A. and Deepa, J. (2021). Sentiment Analysis using various Machine Learning and Deep Learning Techniques. Journal of the Nigerian Society of Physical Sciences. Volume 3, issue 4, pp 385-394.
Vidhya, K. A., and Aghila, G. (2010, February). Hybrid text mining model for document classification. In 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), 1, 210-214, IEEE.
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences
