Nitrate contamination in water sources poses a substantial environmental and health risk. However, accurate detection of nitrate in water, particularly in the presence of dissolved organic carbon (DOC) interference, remains a significant analytical challenge. This study investigates a novel approach for the reliable detection of nitrate in water samples with varying levels of DOC interference based on the equivalent concentration offset method. The characteristic wavelengths of DOC were determined based on the first-order derivatives, and a nitrate concentration prediction model based on partial least squares (PLS) was established using the absorption spectra of nitrate solutions. Subsequently, the absorption spectra of the nitrate solutions were subtracted from that of the nitrate-DOC mixed solutions to obtain the difference spectra. These difference spectra were introduced into the nitrate prediction model to calculate the equivalent concentration offset values caused by DOC. Finally, a DOC interference correction model was established based on a binary linear regression between the absorbances at the DOC characteristic wavelengths and the DOC-induced equivalent concentration offset values of nitrate. Additionally, a modeling wavelength selection algorithm based on a sliding window was proposed to ensure the accuracy of the nitrate concentration prediction model and the equivalent concentration offset model. The experimental results demonstrated that by correcting the DOC-induced offsets, the relative error of nitrate prediction was reduced from 94.44% to 3.36%, and the root mean square error of prediction was reduced from 1.6108 mg L⁻¹ to 0.1037 mg L⁻¹, which is a significant correction effect. The proposed method applied to predict nitrate concentrations in samples from two different water sources shows a certain degree of comparability with the standard method. It proves that this method can effectively correct the deviations in nitrate measurements caused by DOC and improve the accuracy of nitrate measurement.