Gas chromatography–mass spectrometry molecular structural analysis of Amaranth leaf metabolites under drought stress

Conventional high-throughput metabolomics tools often struggle with the unambiguous annotation of complex plant metabolites and structural isomers. To address this limitation, this study presents an advanced analytical approach that combines high-resolution gas chromatography–mass spectrometry (HR-GC–MS), computational quantum chemistry, and chemometrics with innovative stochastic dynamics mass spectrometry (SD-MS) model equations capable of providing precise 3D structural analysis. The practical applicability and robustness of this framework were demonstrated by mapping the metabolomic shifts in the ethanol extracts of A. tricolor L. (cv. Valentina) and A. cruentus L. (cv. Krepysh) leaves under drought stress. The newly developed mathematical tool successfully resolved ambiguities for metabolites previously characterized by low omics-identification scores. Specifically, 3D structural determinations were achieved for the di(2-[( tert -butyldimethylsilyl)oxy]-3-methyl)-derivative of 2-hydroxy-3-methylbutyric acid, apigenin, and several silylated organic acids (fumaric, oxalic, glycolic, and succinic), demonstrating exceptional correlation coefficients of |r| = 0.99958–0.99998. Furthermore, the universal applicability of the SD-MS equations was cross-verified using literature-derived ESI, APCI, and MALDI mass spectrometry reference data. Compared to traditional omics approaches, the developed framework significantly enhances analytical selectivity and sensitivity, offering a reliable methodology for the precise 3D molecular annotation of complex plant metabolomes.