REVIEW OF PROXIMATE ANALYSIS OF HEAVY METALS IN ACTION BITTER ALCOHOLIC HERBAL DRINKS CONSUMED IN NIGERIA

Authors

  • Ambrose E. Ekevwe
  • Hadiza Jibril Abdullahi
  • Funmilayo O. Olatunji
  • Aisha Garba Magashi
  • Hafsat Nababa Abdulmumin

DOI:

https://doi.org/10.33003/fjs-2023-0701-2068

Keywords:

Atomic Absorption Spectrophotometer, Action Bitter, alcoholic herbal

Abstract

Consumption of alcoholic herbal products from beverages or medicinal drinks contaminated with heavy metals can cause serious consequences on human health. This is a major concern for traditional and herbal medicine. The present study was carried out to analyze and quantify the levels of seven potentially toxic heavy metals namely Magnesium, lead, cadmium, copper, iron, chromium and nickel in Action Bitter alcoholic herbal bitter. Twenty one ACTION BITTER alcoholic bitter samples were previously pretreated and homogenized were digested and analyzed to obtain a concentration of  Cadmium (Cd), Chromium (Cr),Copper (Cu), Iron (Fe), Magnesium (Mg), Nickel (Ni) and lead (Pb)  using atomic absorption spectrophotometer equipped with graphite tube atomizer. The concentration obtained are Cadmium (0.017 mg/l),Chromium (0.061 mg/l),Copper (0.056 mg/l), Iron (0.223 mg/l), Magnesium (1.118 mg/l), Nickel (0.112 mg/l) and lead (-0.073 mg/l). The analysis of heavy metals can be useful to evaluate the dosage of herbal drinks prepared from these plants. Therefore, it is of great importance to establish universal standards and quality requirements for hazardous elements in herbal drinks so that this natural resource can continue and expand further, to benefit health globally.

References

Adi Alhudhaif a, Kemal Polat b,(2021) Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images, Expert Systems With Applications 180 (2021) 115141

Adigun J O, O D Fenwa, E O Omidiora, O Oladipo, SO Olabiyisi, M. M Rufai. (2015): “Development of a Genetic based Neural Network System for Online Character. Recognition”, International Journal of Applied Information Systems (IJAIS) – ISSN: 22490868 Foundation of Computer Science FCS, New York, USA,Volume 9 – No.3

Adigun Oyeranmi, Babatunde Ronke, Rufai Mohammed and Aigbokhan Edwin. (2020): “Detection of Fracture Bones in X-ray Images Categorization”,35(4): 1-11, 2020; Article no. JAMCS.57620

Afreen Khan and Swaleha Zubair. (2018): “Machine Learning Tools and Toolkits in the Exploration of Big Data”, international journal of computer sciences and engineering, 6(12):570-575 DOI:10.26438.

Aha D.W., Kibler D and Albert M (1991):” Instance-based learning algorithms”, Mach Learn,6(1):37–66.

Ahmed Hamed, Ahmed Sobhy and Hamed Nassar (2020): “Accurate Classification of COVID19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm”.

Amit Y and Geman D. (1997): “Shape quantization and recognition with randomized trees”, Neural Comput.,9(7):1545–88.

Anshuman Elhence, Manas Vaishnav and Shalimar. (2020): “Coronavirus Disease-2019 (COVID-19)”.

Ashkan Shakarami, Mohammad Bagher Menhaj, Hadis Tarrah (2021) Diagnosing COVID-19 disease using an efficient CAD system Optik – International Journal for Light and Electron Optics 241 (2021) 167199 pp 1-12 Corresponding author. journal homepage: www.elsevier.com/locate/ij

Ashraf E., Abdallah A. and El-Sayed Atlam. (2021): “The COVID-19 pandemic: prediction study based on machine learning models”.

Bracis, C.; Burns, E.; Moore, M.; Swan, D.; Reeves, D.B.; Schiffer, J.T.; Dimitrov, D. Widespread testing, case isolation, and contact tracing may allow safe school reopening with continued moderate physical distancing: A modeling analysis of King County, WA data. Infect. Dis. Model. 2021, 6, 24–35.

Cao L. (2017): “Data science: a comprehensive overview”, ACM Comput Surv (CSUR),50(3):43.

Dianbo L, Leonardo C, Canelle P et al. (2020) A machine learning methodology for real-time forecasting of the 2019–2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models.

Elflein, J. Coronavirus (COVID-19) Disease Pandemic- Statistics & Facts|Statista. 2021. Available online: https://www.statista.com/topics/5994/the-coronavirus-disease-covid-19-outbreak/ (accessed on 30 April 2021).

Published

2023-11-29

How to Cite

Ekevwe, A. E., Abdullahi, H. J., Olatunji, F. O., Magashi, A. G., & Abdulmumin, H. N. (2023). REVIEW OF PROXIMATE ANALYSIS OF HEAVY METALS IN ACTION BITTER ALCOHOLIC HERBAL DRINKS CONSUMED IN NIGERIA. FUDMA JOURNAL OF SCIENCES, 7(1), 319 - 322. https://doi.org/10.33003/fjs-2023-0701-2068

Most read articles by the same author(s)