journal article·2022-09-20Open access

UV-Visible Spectroscopic Technique-Data Mining Tool as a Reliable, Fast, and Cost-Effective Method for the Prediction of Total Polyphenol Contents: Validation in a Bunch of Medicinal Plant Extracts

Fathi Guemari, Salah Eddine Laouini, Abdelkrim Rebiai, Abderrhmane Bouafia, Souhaila Meneceur, Ali Tliba, Kamlah Ali Majrashi, Sohad Abdulkaleg Alshareef, Farid Menaa, Ahmed Barhoum

Applied Sciences· Volume 12· Issue 19

Abstract

Medicinal plants extracts are a rich natural source of bioactive phytochemicals (mainly polyphenols). This study aims at determining the total polyphenols content (TPC) of nine medicinal plants extracted using the UV-visible (UV-Vis) spectroscopic method, along with the Orange Data Mining Tool (ODMT). The TPC for the selected medicinal plant extracts (i.e., Daucus carota L. root, Ruta Chalepensis L. Leaves, Anisosciadium DC. Leaves, Thymus vulgaris L. Leaves, Senna alexandrina leaves, Myrtus communis L. leaves, Silybum Marianum L. Flower, Silybum marianum L. Leaves, and Rosa moschata Flower) was measured using gallic acid (GA) as a standard. The intended method requires a maximum of 1 mg of GA and only 1 mg of the plant extract. The wavelength range of the maximum absorption in the UV-vis spectrum was about 270 nm. For polyphenols, the purposed method linear dynamic concertation range (44.67 to 334.7 mg GA equivalent (GAE)/g dry weight (DW)) with a recovery percentage range of 95.3% to 104.3%, and the good regression value, was found to be R2 = 0.999. This method was easy, fast, accurate, and less expensive than the conventional Folin–Ciocalteu method.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Bee Products Chemical Analysis · Phytochemicals and Antioxidant Activities · Essential Oils and Antimicrobial Activity

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Process Engineering and Petrochemistry, Faculty of Technology, University of El Oued, El-Oued 39000, Algeria

Laboratory of Biotechnology Biomaterial and Condensed Matter, Faculté de la Technologie, Université of El Oued Algérie, El-Oued 39000, Algeria

Department of Chemistry, Faculty of Exact Sciences, University of El Oued, El-Oued 39000, Algeria

Lab. VTRS, Faculty of Technology, University of El Oued, El-Oued 39000, Algeria

Biological Sciences Department, College of Science & Arts, King Abdulaziz University, Rabigh 21911, Saudi Arabia

Department of Biology, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia

Department of Nanomedicine and Advanced Technologies, California Innovations Corporation-Fluorotronics, Inc., San Diego, CA 92037, USA

NanoStruc Research Group, Chemistry Department, Faculty of Science, Helwan University, Helwan 11795, Cairo, Egypt

School of Chemical Sciences, Fraunhofer Project Centre, Dublin City University, D09 V209 Dublin, Ireland

References (33)

Show source-provided references
1. Fruit and Vegetable Consumption, Nutritional Knowledge and Beliefs in Mothers and Children10.1006/appe.1998.0180 ↗
2. Nutrition in the adult years
3. Phytochemicals: Biochemistry and physiology
4. Andersen, O.M., and Markham, K.R. (2005). Flavonoids: Chemistry, Biochemistry and Applications, CRC Press.10.1201/9781420039443 ↗
5. Phenolic compounds: From plants to foods10.1007/s11101-012-9242-8 ↗
6. High-Performance Liquid Chromatographic Methods for the Analysis of Explosives10.1080/10408340701244698 ↗
7. Solís-Oviedo, R.L., and De La Cruz Pech-Canul, Á (2019). The Role of UV-Visible Spectroscopy for Phenolic Compounds Quantification in Winemaking. Frontiers and New Trends in the Science of Fermented Food and Beverages, IntechOpen. Chapter 3.10.5772/intechopen.73404 ↗
8. Giusti, M.M., and Wrolstad, R.E. (2001). Characterization and Measurement of Anthocyanins by UV-Visible Spectroscopy. Curr. Protoc. Food Anal. Chem., F1.2.1–F1.2.13.10.1002/0471142913.faf0102s00 ↗
9. Phenolic acid contents in fruits of aubergine (Solanum melongena L.)
10. Quantitative analysis by ultraviolet spectrophotometry: The cis-trans-stilbene system10.1021/ed034p496 ↗
11. Spectrophotometric Quantification of Flavonoids in Herbal Material, Crude Extract, and Fractions from Leaves of Eugenia uniflora Linn10.4103/pr.pr_143_16 ↗
12. UV–Vis Spectrometry for Quantitative Study of Tannin and Flavonoid Rich Dyes from Plant Sources10.1007/s42250-020-00135-6 ↗
13. Ultrasound-assisted extraction (UAE) of bioactives from arecanut (Areca catechu L.) and optimization study using response surface methodology10.1016/j.ifset.2012.10.001 ↗
14. Prediction of heart disease using data mining techniques10.17485/ijst/2016/v9i47/106827 ↗
15. Gutiérrez, S., Tardaguila, J., Fernández-Novales, J., and Diago, M.P. (2016). Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions. Sensors, 16.10.3390/s16020236 ↗
16. Dey, U.K., Masud, A.H., and Uddin, M.N. (2017, January 16–18). Rice yield prediction model using data mining. Proceedings of the 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’s Bazar, Bangladesh.10.1109/ECACE.2017.7912925 ↗
17. Application and Analysis of the Folin Ciocalteu Method for the Determination of the Total Phenolic Content from Limonium Brasiliense L10.3390/molecules18066852 ↗
18. Analysis of condensed tannins: A review10.1016/S0377-8401(01)00228-0 ↗
19. A reproducible, rapid and inexpensive Folin–Ciocalteu micro-method in determining phenolics of plant methanol extracts10.1016/j.microc.2008.08.011 ↗
20. Development of a UV/Vis spectrophotometric method for analysis of total polyphenols from Caesalpinia peltophoroides Benth10.1590/S0100-40422012000400031 ↗
21. A predictive approach to the antioxidant capacity assessment of green and black tea infusions10.1007/s11694-020-00727-3 ↗
22. Analysis of total phenolic, flavonoids, anthocyanins and tannins content in Romanian red wines: Prediction of antioxidant activities and classification of wines using artificial neural networks10.1016/j.foodchem.2013.10.153 ↗
23. Modelling through artificial neural networks of the phenolic compounds and antioxidant activity of blueberries
24. Spectrophotometric Analysis of Phenolic Compounds in Grapes and Wines10.1021/acs.jafc.7b01724 ↗
25. Watson, R.R. (2019). Chapter 7—Characterization and Quantification of Polyphenols in Fruits. Polyphenols in Plants, Academic Press. [2nd ed.].
26. Phytochemical screening and Identification of Polyphenols, Evaluation of Antioxidant activity and study of Biological properties of extract Silybum marianum (L.)10.5958/0974-4150.2020.00037.1 ↗
27. Extraction of sunflower oil using ethanol as solvent10.1016/j.jfoodeng.2016.01.020 ↗
28. Colorimetry of Total Phenolics with Phosphomolybdic-Phosphotungstic Acid Reagents10.5344/ajev.1965.16.3.144 ↗
29. Folin-ciocalteau micro method for total phenol in wine
30. Záková, M., Podpecan, V., Zelezný, F., and Lavrac, N. (2009). Advancing data mining workflow construction: A framework and cases using the orange toolkit. Proc. 2nd Intl. Wshop. Third Gener. Data Min. Towar. Serv. Knowl. Discov., 39–52.
31. Vaishnav, D., and Rao, B.R. (2018, January 15–16). Comparison of Machine Learning Algorithms and Fruit Classification using Orange Data Mining Tool. Proceedings of the 2018 3rd International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India.10.1109/ICICT43934.2018.9034442 ↗
32. Open-Source Tools for Data Mining10.1016/j.cll.2007.10.002 ↗
33. Comparison of Model Prediction for Tile Production in Tabanan Regency with Orange Data Mining Tool