Determination of Total Flavonoid Content in Medicinal Plant Leaves Powder Using Infrared Spectroscopy and Chemometrics

Lestyo Wulandari(1*), Bayu Dwi Permana(2), Nia Kristiningrum(3)

(1) Pharmaceutical Analysis and Chemometrics Research Group, Faculty of Pharmacy, University of Jember, Jl. Kalimantan 37, Jember 68121, East Java, Indonesia
(2) Pharmaceutical Analysis and Chemometrics Research Group, Faculty of Pharmacy, University of Jember, Jl. Kalimantan 37, Jember 68121, East Java, Indonesia
(3) Pharmaceutical Analysis and Chemometrics Research Group, Faculty of Pharmacy, University of Jember, Jl. Kalimantan 37, Jember 68121, East Java, Indonesia
(*) Corresponding Author


Flavonoid is phenolic compounds consisting of fifteen carbon atoms and is commonly found in plants. Infrared (IR) spectroscopy combined with chemometrics, has been developed for a simple analysis of flavonoid in the medicinal plant leaves powder. IR spectra of selected medicinal plant powder were correlated with flavonoid content using chemometrics. The chemometric methods used for calibration analysis were Partial Least Square (PLS), Principal Component Regression (PCR), and Support Vector Regression (SVR). After the calibration model was formed, it was then validated using Leave-One-Out-Cross-Validation (LOOCV) and 2-fold cross-validation. In this study, the PLS of the Near-infrared (NIR) calibration model showed the best calibration with R-Square and RMSEC values of 0.9676524 and 0.0978202, respectively. The LOOCV of PLS of the NIR calibration model has the R-square and RMSE values of 0.9850164 and 0.067663, respectively. The 2-fold cross-validation gave the R-square and RMSE values of 0.9857071 and 0.2104665, respectively. PLS of the NIR calibration model was further used to predict unknown flavonoid content in commercial samples. The significance of flavonoid content that has been measured by NIR and UV-Vis spectrophotometry was evaluated with paired samples T-test. The flavonoid content that has been measured with both methods gave no significant difference.


medicinal plant leaves powder; total flavonoid content; NIR; FTIR; chemometric

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[1] Yuan, H., Ma, Q., Ye, L., and Piao, G., 2016, The traditional medicine and modern medicine from natural products, Molecules, 21 (5), 559.

[2] Torri, M.C., 2013, Traditional jamu versus industrial jamu: Perceptions and beliefs of consumers in the city of Yogyakarta: What future for traditional herbal medicine in urban Indonesia?, IJESB, 19 (1), 1–20.

[3] Yusro, F., Mariani, F., Diba, F., and Ohtani, K., 2014, Inventory of medicinal plants for fever used by four Dayak sub ethnic in West Kalimantan, Indonesia, Kuroshio Sci., 8 (1), 33–38.

[4] Kumar, S., and Pandey. A.K., 2013, Chemistry and biological activities of flavonoids: An overview, Sci. World J., 2013, 162750.

[5] Brodowska, K.M., 2017, Natural flavonoids: Classification, potential role, and application of flavonoid analogues, Eur. J. Biol. Res., 7 (2), 108–123.

[6] Siddiqui, M.R., AlOthman, Z.A., and Rahman, N., 2017, Analytical techniques in pharmaceutical analysis: A review, Arabian J. Chem., 10 (Suppl. 1), S1409–S1421.

[7] Haas, J., and Mizaikoff, B., 2016, Advances in mid-infrared spectroscopy for chemical analysis, Annu. Rev. Anal. Chem., 9, 45–68.

[8] Gad, H.A., El-Ahmady, S.H., Abou-Shoer, M.I., and Al-Azizi, M.M., 2013, Application of chemometrics in authentication of herbal medicines: A review, Phytochem. Anal., 24 (1), 1–24.

[9] Ozaki, Y., 2012, Near-infrared spectroscopy-its versatility in analytical chemistry, Anal. Sci., 28 (6), 545–563.

[10] Morellos, A., Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R., Tziotzios, G., Wiebensohn, J., Bill, R., and Mouazen, A.M., 2016, Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy, Biosyst. Eng., 152, 104–116.

[11] Hunt Jr., E.R., Daughtry, C.S.T., and Li, L., 2016, Feasibility of estimating leaf water content using spectral indices from worldview-3’s near-infrared and shortwave infrared bands, Int. J. Remote Sens., 37 (2), 388–402.

[12] Wulandari, L., Retnaningtyas, Y., and Lukman, H., 2016, Analysis of flavonoid in medicinal plant extract using infrared spectroscopy and chemometrics, J. Anal. Methods Chem., 2016, 4696803.

[13] Mathur, R., and Vijayvergia, R., 2017, Determination of total flavonoid and phenol content in Mimusops elengi, Int. J. Pharm. Sci. Res., 8 (12), 5282–5285.

[14] Mantanus, J., 2012, New pharmaceutical applications involving near-infrared spectroscopy as a pat compliant process analyzer, Dissertation, Faculty of Medicine, University of Liege, Belgium.

[15] Lengkey, L.C.E.C., Budiastra, I.W., Seminar, K.B., and Purwoko, B.S., 2013, Determination of chemical properties in Jatropha curcas L. seed IP-3P by partial least-squares regression and near-infrared reflectance spectroscopy, Int. J. Agric. Innov. Res., 2 (1), 41–48.

[16] Gu, B., Sheng, V.S., Wang, Z., Ho, D., Osman, S., and Li, S., 2015, Incremental learning for ν-support vector regression, Neural Networks, 67, 140–150.

[17] Ritz, M., Vaculikova, L., and Plevova, E., 2011, Application of infrared spectroscopy and chemometric methods to identification of selected minerals, Acta Geodyn. Geomater., 8 (1), 47–58.

[18] Vasishth, S., and Nicenboim, B., 2016, Statistical methods for linguistic research: Foundational ideas – Part I, Lang. Linguist. Compass, 10 (8), 349–369.

[19] Kumar, R., and Kumar, V., 2015, Discrimination of various paper types using diffuse reflectance ultraviolet–visible near-infrared (UV-Vis-NIR) spectroscopy: Forensic application to questioned documents, Appl. Spectrosc., 69 (6), 714–720.

[20] Georgieva, M., Nebojan, I., Mihalev, K., Yoncheva, N., Kljusurić, J.G., and Kurtanjek, Z., 2013, Application of NIR spectroscopy and chemometrics in quality control of wild berry fruit extracts during storage, Croatian J. Food Technol. Biotechnol. Nutr., 8 (3-4), 67–73.

[21] Cheng, H., Garrick, D.J., and Fernando, R.L., 2017, Efficient strategies for leave-one-out cross-validation for genomic best linear unbiased prediction, J. Anim. Sci. Biotechnol., 8, 38.


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