Quantification of Macular Carotenoids over a Wide Dynamic Range in Plant Matrices and Caco-2 Cells Using a Single Transferable Analytical Method

TL;DR

This paper introduces a new method to accurately measure lutein and zeaxanthin in plants and cells, which is important for understanding their health benefits.

Contribution

A unified chromatographic method with alkaline hydrolysis is developed for quantifying macular carotenoids across diverse matrices.

Findings

The method enables separation and quantification of lutein and zeaxanthin in food, digesta, and Caco-2 cells.

The technique uses common solvents and a C30 column, achieving high recoveries and low RSDs.

It supports high-throughput analysis and transferability between different sample types.

Abstract

Lutein and zeaxanthin are macular carotenoids known for their protective role against major eye diseases. The bio-accessibility of these macular carotenoids is extremely low, with a limited amount synthesised in plants. Quantifying these compounds in plants/biological samples is challenging because of their structural similarity. Although numerous methods have been reported for quantifying macular carotenoids, there is currently no unified chromatographic technique that can be applied for the separation and quantification of these carotenoids across diverse matrices over a broad dynamic range while also incorporating an effective extraction step. Biochemical processes during digestion and absorption further lower carotenoid levels in the body (bioavailability), making precise measurement of their esterified forms necessary. Here, we incorporate an alkaline hydrolysis extraction and present a single liquid chromatographic method applicable to both PDA and MS detection for the separation and quantification of lutein and zeaxanthin across various matrices (food, digesta, and Caco-2 cells) and concentration ranges. It utilises common solvents for the mobile phase system and a C30 column. The reverse-phase method achieved excellent recoveries in spiked samples, acceptable relative standard deviations (RSDs) for validation parameters, and offers potential for high-throughput analysis while being transferable between matrices (from plant to Caco-2 cells).

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