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. 2024 Jul 4:9:100803.
doi: 10.1016/j.crfs.2024.100803. eCollection 2024.

Chemometric discrimination of eight citrus plants utilizing chromatographic and spectroscopic techniques and insights into their biological potentials

Sameh S Elhady  1   2   3 , Fadia S Youssef  4 , Manar O Lashkar  5 , Dalia I Hamdan  6 , Mohamed L Ashour  4   7 , Gokhan Zengin  8 , Mariam I Gamal El-Din  4
Affiliations

Chemometric discrimination of eight citrus plants utilizing chromatographic and spectroscopic techniques and insights into their biological potentials

Sameh S Elhady et al. Curr Res Food Sci. .

Abstract

Citrus sinensis balady orange, C. sinensis navel orange, C. paradisi, C. limon, C. sinensis bloody orange, C. sinensis sweet orange, C. aurantium var. amara and C. reticulata were successfully discriminated using chromatographic and spectroscopic techniques coupled with chemometrics. Ultraviolet spectroscopy (UV), and nuclear magnetic resonance spectroscopy (NMR) managed to discriminate the alcohol extract samples to six and five clusters respectively on exposing the obtained data to Principle component analysis (PCA). High performance liquid chromatography (HPLC) was utilized for differentiating the different samples based upon their rutin content where C. aurantium demonstrated the highest rutin content (0.795 mg/mL). LC-ESI-MS led to the identification of 35 compounds belonging mainly to flavonoids and limonoids. In vitro biological investigations including DDPH, ABTS, FRAP and enzyme inhibitory activities revealed the promising antioxidant, neuroprotective, anti-hyperglycaemic and skin-lightning potentials of citrus samples that were correlated with the total phenol and flavonoid contents. In silico ADME/TOPKAT reflected the acceptable pharmacokinetic, pharmacodynamic and toxicity properties of the identified secondary metabolites.

Keywords: Antioxidant; Chemometric analysis; Citrus; Drug discovery; Enzyme inhibition; HPLC; LC-MS; NMR; Public health; Ultraviolet spectroscopy.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image 1
Graphical abstract
Fig. 1
Fig. 1
UV-based multivariate data analysis of eight Citrus plants. (A) Score plot; (B) overlapped UV-spectra; x-axis represents wavelengths in nm and y-axis represents the absorbances (C) HCA.
Fig. 2
Fig. 2
1H-NMR-based multivariate data analyses of eight Citrus plants. (A) overlapped 1H-NMR -spectra; x-axis represents chemical shift in ppm and y-axis represent the relative signal intensity (B) Score plot; x-axis represents PC-1 that constitutes 39% of total variance and y-axis represents PC-2 that constitutes 31% of total variance; (C) HCA; each sample is the mean of three replicates.
Fig. 3
Fig. 3
LC/MS-based multivariate data analyses of eight Citrus plants. (A) score plot; (B) loading plot; where x-axis represents PC-1 that constitutes 32% of total variance and y-axis represents PC-2 that constitutes 17% of total variance; each sample is the mean of three replicates.
Fig. 4
Fig. 4
Pearson correlation between total bioactive compounds and biological activities of samples (TPC: Total phenolic content; TFC: Total flavonoid content; MCA: Metal chelating assay; PBD: Phosphomolybdenum) (A); biological assays-based multivariate data analyses of eight Citrus plants (B) score plot; x-axis represents PC-1 that constitutes 71% of total variance and y-axis represents PC-2 that constitutes 18% of total variance (C) loading plot; x-axis represents PC-1 that constitutes 71% of total variance and y-axis represents PC-2 that constitutes 18% of total variance; each sample is the mean of three replicates.
Fig. 5
Fig. 5
ADMET Plot of major secondary metabolites identified from the leaves of Citrus species methanol extracts revealing 95% and 99% confidence limit ellipses regarding human intestinal absorption and the blood-brain barrier (BBB) model.
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