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However, comparative analyses across different biological models are often limited by dominant baseline variability arising from intrinsic metabolic differences and distinct culture conditions. This variability may obscure treatment\u2010related signatures and compromise both multivariate and univariate statistical analyses. Here, we propose a data\u2010processing strategy based on difference spectra as input for statistical modeling. For each treatment condition, difference spectra are first calculated by subtracting each paired control binned spectrum from its corresponding treated binned spectrum. These replicate\u2010level binned difference spectra are then averaged to reduce intersample variability while enhancing the detection of treatment\u2010induced metabolic changes within an untargeted framework. The strategy was validated by testing four structurally diverse gold(I) compounds in two ovarian cancer cell lines characterized by significant differences in basal metabolism and grown in distinct media. While conventional multivariate analysis of individual spectra failed to extract shared drug\u2010related signatures, the difference\u2010spectra approach enabled the identification of conserved metabolic perturbations. Altered pathways across cell models were then evaluated based on univariate analysis. 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