The coefficient plot showed that high ammonium hydroxide concentration and ACN positively enhanced the derivative responses (Figure ). In contrast, the responses were adversely affected by high water content, MeOH, and interactive factors (NH4OH*H2O, NH4OH*MeOH). The desirability plot (Figure ) showed that increasing water content when the ammonium hydroxide concentration is below 0.006% could descend the average derivative responses. Adding water increases the solubility of organic acids in the extractant and enhances the extraction efficiency. However, excessive water molecules may hydrolyze EDC into 1-(3-(dimethylamino)propyl)-3-ethylurea, which dramatically lowers the derivatization efficiency at 60 ᵒC in the next stage. A previous study found that 10% of water in the reaction solution should be an optimal condition to dissolve analytes and derivatives, which benefits the reactions. Also, the extractant pH should be considered carefully by adjusting the ammonium hydroxide concentration due to the influence on the partitioning of acidic species. Hemmingsen et al. observed exclusively lowmolecular weight acid species (m/z < 400) in pH 7 EtOH:H2O fraction and intermediate-to high-molecular-weight species (400 < m/z < 600 and m/z > 600) in pH 10 fraction. Thus, the optimal LLE condition for carboxylic acids from crude oil is using a 10% aqueous-acetonitrile solution with 0.01% ammonium hydroxide (Figure ). The optimal extraction and labeling conditions were defined based on the DOE prediction for model compounds and applied to presented carboxylic acids in crude oil. ) were on the diagonal line, indicating good model predictions. The optimal conditions were experimentally examined with three replicates to evaluate the DOEs prediction, which responses were identical to predicted values. A series of calibration standards mixtures (1 -50 µM/acid) in 10% aqueous ACN solutions were derivatized and analyzed using the optimized protocol. Typical LC chromatograms of 7 carboxylic derivatives are shown in Figure for the optimized method. Calibration curves (Figure ) for the standard derivatives were established to determine the limit of detection (LODs in Table ), utilizing the method for the characterization of unknown carboxylic acids. The LODs were calculated using the ratio of 3.3 times the intercept's standard deviation and the slope of calibration functions. For selected acids, the sensitivities were different, ranging from 0.9 × 10 6 to 5.5 × 10 6 arbitrary peak area units per µM. All compounds observed linearity in signal response up to 50 µM. The LODs for carboxylic acids ranged from 1.8 to 33 ng g -1 or 8.6 to 101 nM (Table ). A spiked crude oil at 50 µM each acid and a standard mixture were analyzed to estimate the matrix effect. The matrix effect (ME %) was evaluated by comparison of carboxylic-BNMA response in spiked oil and a mixture of standards in aqueous ACN (Table ). As observed, the ME (%) values ranging between 9% and 36% for seven carboxylic acids suggested low-moderate matrix effects of crude oil on this method. Overall, the results suggest that the optimized method is applicable for characterizing unknown carboxylic acids in crude oil. We also evaluated the proposed identification workflow (Figure ) by analyzing the spiked crude oil consisting of carboxylic acid standards. All standards were identified with carboxylic-BNMA mass annotation and tag removal approach.