Journal of the Korean Chemical Society (대한화학회지)

Korean Chemical Society (대한화학회)
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Simultaneous Spectrometric Determination of Caffeic Acid, Gallic Acid, and Quercetin in Some Aromatic Herbs, Using Chemometric Tools

  • Kachbi, Abdelmalek (Laboratoire des Procedes Membranaires et des Techniques de Separation et de Recuperation, Faculty of Technology, Universite de Bejaia) ;
  • Abdelfettah-Kara, Dalila (Laboratoire des Procedes Membranaires et des Techniques de Separation et de Recuperation, Faculty of Technology, Universite de Bejaia) ;
  • Benamor, Mohamed (Laboratoire des Procedes Membranaires et des Techniques de Separation et de Recuperation, Faculty of Technology, Universite de Bejaia) ;
  • Senhadji-Kebiche, Ounissa (Laboratoire des Procedes Membranaires et des Techniques de Separation et de Recuperation, Faculty of Technology, Universite de Bejaia)
  • Received : 2020.10.14
  • Accepted : 2021.05.21
  • Published : 2021.08.20
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Abstract

The purpose of this work is the development of a method for an effective, less expensive, rapid, and simultaneous determination of three phenolic compounds (caffeic acid, gallic acid, and quercetin) widely present in food resources and known for their antioxidant powers. The method relies on partial least squares (PLS) calibration of UV-visible spectroscopic data. This model was applied to simultaneously determine, the concentrations of caffeic acid (CA), gallic acid (GA), and quercetin (Q) in six herb infusion extracts: basil, chive, laurel, mint, parsley, and thyme. A wavelength range (250-400) nm, and an experimental calibration matrix with 21 samples of ternary mixtures composed of CA (6.0-21.0 mg/L), GA (10.0-35.2 mg/L), and Q (6.4-17.5 mg/L) were chosen. Spectroscopic data were mean-centered before calibration. Two latent variables were determined using the contiguous block cross-validation procedure after calculating the root mean square error cross-validation RMSECV. Other statistic parameters: RMSEP, R2, and Recovery (%) were used to determine the predictive ability of the model. The results obtained demonstrated that UV-visible spectrometry and PLS regression were successfully applied to simultaneously quantify the three phenolic compounds in synthetic ternary mixtures. Moreover, the concentrations of CA, GA and Q in herb infusion extracts were easily predicted and found to be 3.918-18.055, 9.014-23.825, and 9.040-13.350 mg/g of dry sample, respectively.

Keywords

Acknowledgement

The authors are grateful to the Algerian MESRS for the financial support in the CNEPRU program no. A16N01UN060120140005. Publication cost of this paper was supported by the Korean Chemical Society.

References

  1. Diaz-Garcia, M. C.; Obon, J. M.; Castellar, M. R.; Collado, J.; Alacid, M. Food Chem. 2013, 138, 938. https://doi.org/10.1016/j.foodchem.2012.11.061
  2. Belajova, E. J. Food Nutr. Res. 2012, 51, 117.
  3. Marti, R.; Valcarcel, M.; Herrero-Martinez, J. M.; Cebolla-Cornejo, J.; Rosello, S. Food Chem. 2015, 169, 169. https://doi.org/10.1016/j.foodchem.2014.07.151
  4. Benkaci-Ali, F.; Baaliouamer, A.; Wathelet, J. P.; Marlier, M. Chem. Nat. Comp. 2012, 47, 925. https://doi.org/10.1007/s10600-012-0106-7
  5. Shamuratov, B. A.; Mavlyanov, S. M.; Dalimov, D. N.; Allaniyazova, M. K. Chem. Nat. Comp. 2003, 39, 597. https://doi.org/10.1023/B:CONC.0000018119.19021.18
  6. Pedroza, M. A.; Carmona, M.; Pardo, F.; Salinas, M. R.; Zalacain, A. CyTA-J. Food 2012, 10, 225. https://doi.org/10.1080/19476337.2011.633243
  7. Boggia, R.; Casolino, M. C.; Hysenaj, V.; Oliveri, P.; Zunin, P. Food Chem. 2013, 140, 735. https://doi.org/10.1016/j.foodchem.2012.11.020
  8. Amelin, V. G.; Andoralov, A. M. J. Anal. Chem. 2016, 71, 82. https://doi.org/10.1134/S1061934815120035
  9. Barros, L.; Duenas, M.; Pinela, J.; Carvalho, A. M.; Buelga, C. S. Plant Foods Hum. Nutr. 2012, 67, 229. https://doi.org/10.1007/s11130-012-0307-z
  10. De Paepe, D.; Sarvaes, K.; Noten, B.; Diels, L.; De Loose, M. Food Chem. 2013, 136, 368. https://doi.org/10.1016/j.foodchem.2012.08.062
  11. Helmja, K.; Vaher, M.; Pussa, T.; Raudsepp, P.; Kaljurand, M. Electrophoresis 2008, 29, 3980. https://doi.org/10.1002/elps.200800012
  12. Wang, Y.; Li, B.; Ni, Y.; Kokot, S. Anal. Methods 2013, 5, 6051. https://doi.org/10.1039/c3ay40985f
  13. Barbosa, S.; Campmajo, G.; Saurina, J.; Puignou, L.; Nunez, O. J. Agric. Food Chem. 2020, 68, 591. https://doi.org/10.1021/acs.jafc.9b06054
  14. Sakakibara, H.; Honda, Y.; Nakagawa, S.; Ashida, H.; Kanazawa, K. Food Chem. 2003, 51, 571. https://doi.org/10.1021/jf020926l
  15. Rebwar, O. H.; Hunar, Y. M.; Hijran, S. J. J. Iran. Chem. Soc. 2018, 15, 1603. https://doi.org/10.1007/s13738-018-1358-3
  16. Abbasi-Tarighat, M.; Shahbazi, E.; Niknam, K. Food Chem. 2013, 138, 991. https://doi.org/10.1016/j.foodchem.2012.09.099
  17. Aguerssif, N.; Benamor, M.; Kachbi, A.; Draa, M. T., J. Trace Elem. Med. Biol. 2008, 22, 175. https://doi.org/10.1016/j.jtemb.2007.12.004
  18. De Carvalho, B. M. A.; De Carvalho, L. M.; Dos Reis Coimbra, J. S.; Minim, L. A.; De Souza Barcellos E. Food Chem. 2015, 174, 1. https://doi.org/10.1016/j.foodchem.2014.11.003
  19. Ivanovic, M.; Razborsek, M. I.; Kolar, M. Acta Chim. Slov. 2016, 63, 661. https://doi.org/10.17344/acsi.2016.2534
  20. Kachbi, A.; Benamor, M.; Aguerssif, N. Curr. Anal. Chem. 2010, 6, 88. https://doi.org/10.2174/157341110790069709
  21. Kavsek, D.; Jeric,T.; Le Marechal, A.M.; Vajnhand, S.; Bednarova, A. Acta Chim. Slov. 2013, 60, 375.
  22. Divya, O.; Shinde, M. J. Appl. Spectrosc. 2013, 80, 326. https://doi.org/10.1007/s10812-013-9768-6
  23. Korany, M. A.; Abdine, H. H.; Ragab, M. A. A.; Aboras, S. I. Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 2015, 143, 281. https://doi.org/10.1016/j.saa.2015.01.076
  24. Moghadam, M. R.; Shabani, A. M. H.; Dadfarnia, S. Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 2015, 135, 929. https://doi.org/10.1016/j.saa.2014.08.007
  25. Zolgharnein, J.; Asanjarani, N.; Azimi, G.; Ghasemi, J. J. Anal. Chem. 2015, 70, 148. https://doi.org/10.1134/S1061934815020070
  26. Chanda, S.; Hazarika, A. K.; Choudhury, N.; Islam, S. A.; Manna, R.; Sabhapondit, S.; Tudu, B.; Bandyopadhyay, R. J. Chemom. 2019, 33, 1. https://doi.org/10.1002/cem.3172
  27. De Jong, S. Chemom. Intell. Lab. Syst. 1993, 18, 251. https://doi.org/10.1016/0169-7439(93)85002-X
  28. Geladi, P.; Kowalski, B. R. Anal. Chim. Acta 1986, 185, 1. https://doi.org/10.1016/0003-2670(86)80028-9
  29. Meloun, M.; Militky, J.; Hill, M.; Brereton, R. G. Analyst. 2002, 127, 433. https://doi.org/10.1039/b110779h
  30. Wise, B. M.; Gallagher, N. B.; Bro, R.; Shaver, J. M.; Windig, W. Eigenvector Research, Wenatchee 2006, 137.
  31. Wold, S.; Sjostrom, M.; Eriksson, L. Chemom. Intell. Lab. Syst. 2001, 58, 109. https://doi.org/10.1016/S0169-7439(01)00155-1
  32. Cornell, J. A., Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data, 3rd ed.; John Wiley, and Sons: New York, U.S.A. 2002; Chapter 1, p. 1.
  33. Ghasemi, J.; Shahabadi, N.; Seraji, H. R. Anal. Chim. Acta 2004, 510, 121. https://doi.org/10.1016/j.aca.2003.12.053
  34. Proestos, C.; Boziaris, I. S.; Nychas, G. J. E.; Komaitis, M. Food Chem. 2006, 95, 664. https://doi.org/10.1016/j.foodchem.2005.01.049
  35. Wang, H.; Provan, G. J.; Helliwell, K. Food Chem. 2004, 87, 307. https://doi.org/10.1016/j.foodchem.2003.12.029
  36. Bilyk, A.; Sapers, G. M. J. Agric. Food Chem. 1985, 33, 226. https://doi.org/10.1021/jf00062a017
  37. Lugast, A.; Hovari, J. Acta Alim. 2000, 29, 345. https://doi.org/10.1556/AAlim.29.2000.4.4
  38. Justesen, U.; Knuthsen, P. Food Chem. 2001, 73, 245. https://doi.org/10.1016/S0308-8146(01)00114-5
  39. Trichopoulou, A.; Vasilopoulou, E.; Hollman, P.; Chamalides, C.; Foufa, E. Food Chem. 2000, 70, 319. https://doi.org/10.1016/S0308-8146(00)00091-1