NUTRITIONAL EVALUATION AND FUNCTIONAL PROPERTIES OF Cassia alata FLOWER

Authors

  • O. R. Adebayo
  • M. O. Oyewale
  • B. M. Adegoke
  • O. O. Efunwole
  • A. A. Adedokun
  • G. O. Ogunsola

DOI:

https://doi.org/10.33003/fjs-2021-0503-753

Keywords:

Cassia alata, Infra red characterization, therapeutic, medicinal plant, nutrients

Abstract

Medicinal plant is a part of plant or the whole plant that possesses healing properties. It’s of utmost importance in many areas of life. Cassia alata belongs to this class of plants. The present study was carried out to evaluate the mineral elements, proximate, vitamins, phytochemical compositions and infra-red spectra of the flower of Cassia alata plant. The analyses were carried out using standard analytical techniques. The proximate analysis (%) showed that the flower contained ash (7.060±0.082), moisture (8.489 ±0.151), crude fat (12.319 ± 0.292), crude fibre (16.055 ±0.756), protein (10.447±0.06) and carbohydrate (45.630± 0.120). Elemental analysis (ppm) showed the presence of zinc (0.719 ±0.006), copper (0.071 ±0.002), nickel (0.0064 ±0.001) manganese (0.059±0.001) and iron (0.061 ±0.002) in moderate quantity with magnesium (26.577 ±0.005) and calcium (37.302 ± 0.020) while phosphorus (106.400 ± 0.001) was found in large amount. The result from the vitamin analysis (mg/g) revealed vitamin C to be the most abundant vitamin with (37.853±0.039mg/g) while the composition of vitamin B1 was (0.244±0.002mg/g) and vitamin B2 (0.473±0.0009mg/g). Phytochemicals (mg/g) were detected; phenols (14.319±0.064), saponins (14.692±0.653), flavonoids (13.940±0.017) and tannins (1.247±0.050). Also, Infra-red characterization of the flower part of the plant indicated some functional properties which are of medicinal benefits to man and animals. This study proposes that the flower of Cassia alata  can serve as good source of nutrients and with potentials as therapeutics

References

Chai, Z. Y., Li, Y. L., Han, Y. M., & Zhu, S. F. (2019). Recommendation System Based on Singular Value Decomposition and Multi-Objective Immune Optimization. IEEE Access, 7, 6060-6071.

Chiu, S. M., Chen, Y. C., Chang, T. Y., Hsu, Y. L., Su, H. Y., Chen, H. M., & Lin, T. Y. (2016, July). A fast way for finding similar friends in social networks by using neuro-fuzzy networks. In 2016 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 2, pp. 541-545). IEEE.

Dhruv, A., Kamath, A., Powar, A., & Gaikwad, K. (2019). Artist Recommendation System Using Hybrid Method: A Novel Approach. In Emerging Research in Computing, Information, Communication and Applications (pp. 527-542). Springer, Singapore.

Farooq, U., Kannampallil, T.G., Song, Y., Ganoe, C.H., Carroll, J.M., & Giles, C.L. (2007). Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics. In Gross, T., & Inkpen, K. (Eds.), Proceedings of the 2007 International ACM SIGGROUP Conference on Supporting Group Work, GROUP 2007, Sanibel Island, Florida, USA, November 4-7, 2007 (pp. 351–360): ACM.

Fessahaye, F., Perez, L., Zhan, T., Zhang, R., Fossier, C., Markarian, R., ... & Oh, P. (2019, January). T-RECSYS: A Novel Music Recommendation System Using Deep Learning. In 2019 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-6). IEEE.

Guy, I., Chen, L., & Zhou, M.X. (2013). Introduction to the special section on social recommender systems. ACM TIST, 4(1), 7.

Hasan, M. M., Shaon, N. H., Al Marouf, A., Hasan, M. K., Mahmud, H., & Khan, M. M. (2015, December). Friend recommendation framework for social networking sites using user's online behavior. In 2015 18th International Conference on Computer and Information Technology (ICCIT) (pp. 539-543). IEEE.

Hasan, M. M., Shaon, N. H., Al Marouf, A., Hasan, M. K., Mahmud, H., & Khan, M. M. (2015, December). Friend recommendation framework for social networking sites using user's online behavior. In 2015 18th International Conference on Computer and Information Technology (ICCIT) (pp. 539-543). IEEE.

Hasan, M. M., Shaon, N. H., Al Marouf, A., Hasan, M. K., Mahmud, H., & Khan, M. M. (2015, December). Friend recommendation framework for social networking sites using user's online behavior. In 2015 18th International Conference on Computer and Information Technology (ICCIT) (pp. 539-543). IEEE.

Hassannia, R., Vatankhah Barenji, A., Li, Z., & Alipour, H. (2019). Web-Based Recommendation System for Smart Tourism: Multiagent Technology. Sustainability, 11(2), 323

Hassannia, R., Vatankhah Barenji, A., Li, Z., & Alipour, H. (2019). Web-Based Recommendation System for Smart Tourism: Multiagent Technology. Sustainability, 11(2), 323.

Kaveri, V. V., & Maheswari, V. (2016). A Model based Resource Recommender System on Social Tagging Data. Indian Journal of Science and Technology, 9, 25.

Kavin K., Ponvimal M., Nithya L., Vishnu Priya B., & Boopathi Rajan P. (2017, February). A Life Style Based Friend Recommendation System. International Journal for Research in Applied Science & Engineering Technology (IJRASET). Volume 5 Issue II, ISSN: 2321-9653.

Kavin K., Ponvimal M., Nithya L., Vishnu Priya B., & Boopathi Rajan P. (2017, February). A Life Style Based Friend Recommendation System. International Journal for Research in Applied Science & Engineering Technology (IJRASET). Volume 5 Issue II, ISSN: 2321-9653.

Khosravi-Farsani, H., Nematbaksh, M., & Lausen, G. (2013). Structure/attribute computation of similarities between nodes of a RDF graph with application to linked data clustering. Intelligent Data Analysis, 17(2), 179-194.

Koerner, C.—Benz, D.—Strohamaier, M.—Hotho, A.—Stumme, G.: Stop Thinking, Start Tagging – Tag Semantics Emerge from Collaborative Verbosity. Proceedings of the 19th International World Wide Web Conference (WWW’10), Raleigh, NC, USA, ACM, 2010, pp. 521–530, doi: 10.1145/1772690.1772744.

Linden, G., Smith, B., & Com, J. Y. A. (2003). Industry report: Amazon. com recommendations: Item-to-item collaborative filtering. In IEEE Distributed Systems Online.

Manca, M., Boratto, L., & Carta, S. (2014, August). Using Behavioral Data Mining to Produce Friend Recommendations in a Social Bookmarking System. In International Conference on Data Management Technologies and Applications (pp. 99-116). Springer, Cham.

Naruchitparames, J., Güneş, M. H., & Louis, S. J. (2011, June). Friend recommendations in social networks using genetic algorithms and network topology. In 2011 IEEE Congress of Evolutionary Computation (CEC) (pp. 2207-2214). IEEE.

Narvekar, M., & Syed, S. F. (2015). An optimized algorithm for association rule mining using FP tree. Procedia Computer Science, 45, 101-110.

Neehal, N., & Mottalib, M. A. (2019, February). Prediction of Preferred Personality for Friend Recommendation in Social Networks using Artificial Neural Network. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-6). IEEE.

NING, L. J., & DUAN, H. Y. (2014). An algorithm for friend-recommendation of social networking sites based on SimRank and ant colony optimization. The Journal of China Universities of Posts and Telecommunications, 21, 79-87.

Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert systems with applications, 39(11), 10059-10072.

Peng, H., Ying, C., Tan, S., Hu, B., & Sun, Z. (2018). An Improved Feature Selection Algorithm Based on Ant Colony Optimization. IEEE Access, 6, 69203-69209.

Ren, Y., & Chi, C. (2018, May). Research on Recommender System based on Social Trust. In 2018 8th International Conference on Social science and Education Research (SSER 2018). Atlantis Press.

Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1-35). Springer, Boston, MA.

Shehu, S. (2017) ACohesion Based Friend Recommendaton System. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 7,(5).

Simon, H.A. (1971). Designing organizations for an information rich world. In Greenberger, M. (Ed.), Computers, communications, and the public interest (pp. 37–72). Baltimore: Johns Hopkins Press.

Wang, K., Xu, L., Huang, L., Wang, C. D., & Lai, J. H. (2019). SDDRS: Stacked Discriminative Denoising Auto-Encoder based Recommender System. Cognitive Systems Research, 55, 164-174.

Wu, B. X., Xiao, J., & Chen, J. M. (2015, August). Friend recommendation by user similarity graph based on interest in social tagging systems. In International Conference on Intelligent Computing (pp. 375-386). Springer, Cham.

Published

2021-11-03

How to Cite

Adebayo, O. R., Oyewale, M. O., Adegoke, B. M., Efunwole, O. O., Adedokun, A. A., & Ogunsola, G. O. (2021). NUTRITIONAL EVALUATION AND FUNCTIONAL PROPERTIES OF Cassia alata FLOWER. FUDMA JOURNAL OF SCIENCES, 5(3), 294 - 298. https://doi.org/10.33003/fjs-2021-0503-753

Most read articles by the same author(s)