Improved metabolomic approach for evaluation of phytochemicals in mustard, kale, and broccoli microgreens under different controlled environment agriculture conditions

Abstract

The fast-growing field of controlled environment agriculture (CEA) offers unprecedented opportunities for targeted improvement in concentrations of bioactive compounds in fresh produce achieved through precise modulation of production conditions. To gain full sight of the phytochemical profiles of vegetables grown under different conditions, a rapid analytical strategy is needed for the evaluation of different CEA growing conditions. In this study, Brassica microgreens including ruby streaks mustard (B. juncea), red kale (B. oleracea), and broccoli (B. oleracea) were used as model plants for the evaluation of CEA conditions. Analysis of two first leaves (cotyledons) in microgreens with minimum sample extraction and ultra-high performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) based metabolomic approach was applied for phytochemical analysis for evaluation of the brassica microgreens grown under four different light sources, namely white (W), dark (D), white and far-red (WF) and far-red (F) and fertilizer conditions (CaCl2 and K2SO4). An image-based normalization method using leaf area coupled with chemometrics-based strategies including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) was performed for the post-acquisition data analysis. The method successfully distinguished between Brassica microgreens grown under different CEA settings in a shortened cycle with less organic solvent and labor, which is more environmentally friendly and sustainable. Marker compounds that are responsible for differentiating the Brassica microgreens under various CEA conditions were tentatively identified. Among the tentatively identified marker compounds, gingerglycolipid A was first reported in red kale and broccoli. The results from the present study may serve as a scientific foundation for the rapid and simple assessment to optimize CEA conditions.

Keywords

• Metabolomics
• Microgreen
• Controlled environment agriculture (CEA)
• Image-based normalization
• Chemometrics