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Phytochemical Analysis 2017-Jul

Application of NIR Spectroscopy Coupled with PLS Regression for Quantification of Total Polyphenol Contents from the Fruit and Aerial Parts of Citrullus colocynthis.

רק משתמשים רשומים יכולים לתרגם מאמרים
התחבר הרשם
הקישור נשמר בלוח
Tania S Rizvi
Fazal Mabood
Liaqat Ali
Mohammed Al-Broumi
Hamida K M Al Rabani
Javid Hussain
Farah Jabeen
Suryyia Manzoor
Ahmed Al-Harrasi

מילות מפתח

תַקצִיר

BACKGROUND

Citrullus colocynthis (L.) Schrad is extensively used to treat diabetes, obesity, fever, cancer, amenorrhea, jaundice, leukemia, rheumatism, and respiratory diseases. Chemical studies have indicated the presence of several cucurbitacins, flavones, and other polyphenols in this plant. These phytochemical constituents are responsible for the interesting antioxidant and other biological activities of C. colocynthis.

OBJECTIVE

In the present study, for the first time, near infrared (NIR) spectroscopy coupled with partial least square (PLS) regression analysis was used to quantify the polyphenolic phytochemicals of C. colocynthis.

METHODS

The fruit and aerial parts of the C. colocynthis were extracted individually in methanol followed by fractionation in n-hexane, chloroform, ethyl acetate, n-butanol, and water. Near infrared (NIR) spectra were obtained in absorption mode in the wavelength range 700-2500 nm. The PLS regression model was then built from the obtained spectral data to quantify the total polyphenol contents in the selected plant samples.

RESULTS

The PLS regression model obtained had a R2 value of 99% with a 0.98 correlationship value and a good prediction with a root mean square error of prediction (RMSEP) value of 1.89% and correlation of 0.98. These results were further confirmed through UV-vis spectroscopy and it is found that the ethyl acetate fraction has the maximum value for polyphenol contents (101.7 mg/100 g; NIR, 100.4 mg/100 g; UV-vis).

CONCLUSIONS

The polyphenolic phytochemicals of the fruit and aerial parts of C. colocynthis have been quantified successfully by using multivariate analysis in a non-destructive, economical, precise, and highly sensitive method, which uses very simple sample preparation. Copyright © 2017 John Wiley & Sons, Ltd.

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