Multi-analytical dataset on Lekhaniya Mahakashaya: HRLC-MS/MS Orbitrap profiling, HPTLC fingerprinting with marker estimation, and FTIR spectroscopy

This dataset provides a comprehensive, multidimensional phytochemical characterization of <i>Lekhniya Mahakashaya</i> (LMK), a classical Ayurvedic formulation used for the Treatment of obesity and metabolic disorders. Three complementary analytical platforms were employed: High-Resolution Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (HRLC-MS/MS) Orbitrap, High-Performance Thin Layer Chromatography (HPTLC), and Fourier-Transform Infrared (FTIR) spectroscopy. For HRLC-MS/MS analysis, Hydroalcoholic extracts of LMK were prepared and analysed in both Positive and negative ionisation modes using an Orbitrap mass spectrometer. The dataset includes 2034 metabolomics-identified compounds: 1712 in positive ion mode and 322 in negative ion mode, with detailed retention times, molecular weights, and fragmentation patterns, suitable for compound annotation, metabolite networking, and cheminformatics-based correlation studies. HPTLC fingerprinting was performed using methanolic extracts (2-10 µL) on silica gel 60 F₂₅₄ plates, which yielded 7-8 reproducible peaks across the Rf range 0.12-0.89 under 254 nm, 366 nm, and 540 nm, confirming LMK's polyherbal complexity. Marker-based quantification revealed that berberine (0.24 % w/w) and curcumin (0.31 % w/w) were performed using validated HPTLC protocols, and calibration curves are included for reproducibility. FTIR Spectroscopic data encompass 19 absorption peaks (3278-0468 cm⁻¹), representing hydroxyl, aliphatic, unsaturated, sulfur-, nitrogen-, and halogen-containing functional groups, which highlights LMK's diverse phytochemical matrix. This dataset is structured for pharmacological exploration, quality control, and phytochemical standardisation of LMK and associated Ayurvedic formulations. This dataset is a reference resource. Additionally, the dataset can be used for molecular docking validation, network pharmacology mapping, metabolomics comparisons, and future drug discovery. To promote transparency, encourage computational or experimental reuse, and support integrative research on traditional medicine, all raw chromatograms, spectrum files, and processed data tables are made available in widely accessible formats.