Green comprehensive two-dimensional liquid chromatography (LC × LC) for the analysis of phenolic compounds in grape juices and wine.

Abstract

Grape juices and wines are rich in numerous groups of polyphenolic compounds which require a dedicated separation technique for such complex samples. LC × LC is considered the best technique for the analysis of such samples as it can achieve better resolution and higher peak capacity compared to 1D LC. The ever-growing demand for protecting the environment necessitates reducing or eliminating hazardous solvents to improve the environmental friendliness of analytical procedures. In this study, propylene carbonate was used as an eco-friendly mobile phase component in comprehensive two-dimensional liquid chromatography to analyze phenolic compounds in grape juices and a dealcoholized wine sample. Novel green RPLC × RPLC-DAD and RPLC × RPLC-MS methods were developed for the first time to identify phenolic compounds in five samples (two red grape juice samples, two white grape juice samples, and one dealcoholized wine sample). Four different RPLC × RPLC systems were developed; three systems were connected to a diode array detector (RPLC × RPLC-DAD), while the fourth system was connected to DAD and MS detectors (RPLC × RPLC-DAD-ESI-MS). Solvent X (propylene carbonate:ethanol, 60:40) was adopted as a green organic modifier in the first dimension (1D) and methanol in the second dimension (2D). The practical peak capacity and the surface coverage were calculated as metrics to measure the separation performance of all proposed systems. The orthogonality values for the setups ranged from 0.64 to 0.92 when calculated by the convex hull method, and from 0.54 to 0.80 when calculated by the asterisk equations method. The practical peak capacity production rate ranged from 14.58 to 22.52 peaks/min. The results revealed that the phenolic compounds were separated efficiently with good coverage of the 2D separation space and high peak capacity. A total of 70 phenolic compounds were detected based on MS data and information from the literature.