Rapid quality control of Sutherlandia frutescens leaf material through the quantification of SU1 using vibrational spectroscopy in conjunction with chemometric data analysis. (June 2018)

• Record Type: Journal Article
• Authors: Mavimbela, Thandazile; Vermaak, Ilze; Chen, Weiyang; Viljoen, Alvaro
• Is Part Of: Phytochemistry letters. Volume 25(2018)
• Page Start: 184
• Page End: 190
• Publication Date: 2018-06
• Subjects: Sutherlandia frutescens -- Cancer bush -- Quality control -- Chemometrics -- SU1 -- Sutherlandioside B; Botanical chemistry -- Periodicals; Chimie végétale -- Périodiques; 572.205
• DOI: 10.1016/j.phytol.2018.03.003
• Languages: English
• ISSNs: 1874-3900

Abstract

Graphical abstract: Highlights: UHPLC–MS analysis showed high SU1 variation in Sutherlandia frutescens leaf material. PLS regression models were developed based on NIR and MIR spectroscopic data. The NIR and MIR calibration models developed accurately predicted the SU1 content. Abstract: Sutherlandia frutescens, commonly known as cancer bush, is a popular herbal remedy which has been used traditionally for the treatment of internal cancers, stress, influenza, fever, diabetes, stomach ailments and as a tonic. SU1 (syn = sutherlandioside B) is a major cycloartane-type triterpene glycoside in the leaf material and it has been used as a biomarker for quality control. Vibrational spectroscopy in combination with chemometric data analysis has been successfully used to quantify target compounds in various materials and is considered a green alternative method to traditional analytical methods. In this study, the variation in SU1 content in S. frutescens wild-type samples was investigated using ultra high performance liquid chromatography coupled to mass spectrometery (UHPLC–MS). This UHPLC–MS reference dataset was used in conjunction with vibrational spectroscopy and chemometric modeling to develop a rapid alternative method to quantify SU1 in powdered leaf material. Partial least squares (PLS) regression models based on near- and mid-infrared spectroscopy data were developed using SIMCA-P+ 13.0 chemometric software and validated by inserting external prediction sets into the model. The concentration of SU1 showed high variation ranging from 0.04 to 10.10 μg/mg of dried leaf material as determined using UHPLC–MS. Calibration models with good model statistics were developed and used to predict the content of the major compound SU1. External datasets introduced into the model resulted in the accurate prediction of the SU1 content. NIR and MIR spectroscopy in combination with chemometric data analysis were shown to be viable rapid alternative quantification methods for SU1 in powdered leaf material.