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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
Comprehensive micellar / reversed-phase two-dimensional liquid chromatography for separation and identification of
Qian-Xue Shen1, Yi-Wen Wu1, Xin-Ran Ma1
1College of Material Chemistry and Chemical Engineering, Hangzhou Normal University, Hangzhou, 311121, PR China.
Background:
Comprehensive two-dimensional liquid chromatography (LC × LC) is an advanced chromatographic technique that considerably enhances peak capacity and separation ability through combining two distinct separation mechanisms. Currently, a micellar mobile phase provides a dynamic chemical environment for chromatographic separation. This dynamic characteristic may result in a separation mechanism different from that of the traditional static mobile phase, thereby enabling more optimal separation conditions. Herein, an innovative and efficient LC × LC method was presented that combined micellar and reversed-phase liquid chromatography for the determination of bioactive compounds in herbal tea.
Results:
A concentration of 10 mM sodium dodecyl sulfate was identified as optimal for the micellar mobile phase. Under optimal LC × LC conditions, Eclipse Plus C18 (2.1 mm × 100 mm, 3.5 μm, Agilent) and Poroshell 120 SB-C18 (3.0 mm × 50 mm, 2.7 μm, Agilent) columns were used for first-dimension (1D) and second-dimension (2D) separations, respectively. Flow rates of 0.1 mL/min (1D) and 2.4 mL/min (2D) yielded improved resolution and peak shape of the analytes. In addition, the shifted gradient elution mode was adopted to enhance the chromatographic resolution. At the 1D stage, the developed method reduced organic solvent consumption by 9.71 mL per injection compared to traditional reversed-phase liquid chromatography, while peak capacity increased from 10 to 20. The resolution was also significantly improved; for example, catechin and procyanidin A2 increased from 0.86 to 3.18 and 1.34 to 7.23, respectively. Principal component analysis (PCA), with first three principal components explaining 60.53 % of the variance; partial least squares discriminant analysis (PLS-DA), 73.3 % model accuracy; and hierarchical cluster analysis (HCA) revealed significant differences between Rosa chinensis varieties from different regions.
Significance:
The use of micellar mobile phase systems reduces the need for conventional organic solvents while increasing the peak capacity and separation efficiency for compounds. This method indicates that LC × LC, in conjunction with PCA, PLS-DA, and HCA, is effective for the classification and quality control of Rosa chinensis. This provides an effective solution for future separation technologies for natural complex matrices.
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