Metabolomics Driven Standardization of Medicinal Plants: Impact of Vegetation Period, Environmental Factors, and Pre-Analytical Handling on Metabolite Profiles

Medicinal plant metabolomics is rapidly expanding as a sensitive approach to elucidate phytochemical complexity and dynamic metabolic shifts across vegetation periods, enabling discovery of bioactive compounds, evaluation of activity metabolite relationships, detection of adulteration, and strengthened quality control of herbal materials. This review aims to synthesize advances in analytical platforms, chemometrics, and sample-preparation strategies, and to clarify how phenological and environmental drivers (growth stage, season, geography, climate, soil, humidity, altitude, stress, infection, herbivory, genetic variability, and post-harvest processing) shape metabolite variability with direct consequences for standardization. The methodology is a narrative integration of established and emerging metabolomics workflows spanning pre-analytical handling (stage-appropriate harvesting, rapid quenching via dry ice or liquid nitrogen, removal of contaminants, controlled drying and storage) and extraction design (solvent selection across polarity space, solvent mixtures or sequential extraction, optimization of temperature, pH, time, agitation, cleanup/fractionation) coupled to NMR, GC-MS, and LC-MS, and newer rapid/spatial tools such as DESI-MS, DART-MS, and mass spectral imaging, alongside multivariate statistics (PCA, PLS-DA, clustering) for high-dimensional interpretation. Across studies, major findings indicate that minor deviations in collection, derivatization, drying and storage (short-term at −80°C or liquid nitrogen; long-term discouraged; dark storage for light-sensitive compounds) can markedly alter observed metabolomes and reproducibility, while phenology can change the presence/absence and abundance of metabolites that underpin biological activity. Metabolomics-driven fingerprinting combined with PCA/PLS-DA enables species authentication, adulteration detection, and batch-to-batch consistency assessment, but standardization remains constrained by limited reference standards and unknown-metabolite annotation.