Unfolding the Nutraceutical Potentials of Dendrobium Nobile Lindl Orchid Flowers Based on Their Color Variation: A...

Dendrobium nobile is an edible orchid with diverse therapeutic properties. In northeastern Himalayan states of India, the extract of this flower is consumed by tribal populations for the treatment of diabetes, cancer, and cardiovascular diseases. However, the profile of biologically active compounds in the colored varieties of D. nobile orchid flowers cultivated in the northeastern Himalayan region is not well established, necessitating a thorough investigation.

This study aims to establish and compare the metabolite profile of three types of D. nobile flowers (white, light pink, and dark pink) using liquid chromatography-high-resolution mass spectrometry (LC-HRMS).

The homogenized and cryoground flower samples were extracted with aqueous methanol. After LC-MS data acquisition through full-scan (untargeted) and MS/MS (targeted) modes, data processing involved peak alignment, adduct identification, and integration. Results were compared by multivariate statistics using independent component analysis.

A total of 73 metabolites was identified, each with mass error of less than 5 ppm for both precursor and fragment ions. From anthocyanin classes, the chemometric analysis revealed 11 distinguishing biomarker metabolites, with variable influence on projection values above 1. Across the test accessions, three compounds, viz. cyanidin-3-diglucoside, delphinidin-3-sophoroside, and delphinidin-3-gentiobioside, revealed their discriminatory presence.

The study identified the therapeutically important anthocyanins for metabolomic discrimination of three different D. nobile flower accessions. The study will be useful for authenticating D. nobile accessions, derived products, and selecting candidate traits for future breeding programs for varietal improvements.

This study identified and profiled the bioactive anthocyanin compounds in D. nobile orchid flowers grown in the northeastern Himalayan region of India, based on color variations using a non-target metabolomics approach.