This study proposes a method for detecting chemical oxygen demand (COD) in water using fluorescence emission spectroscopy.The spectral data were preprocessed using multiple techniques,including multiplicative scatter correction (MSC),first-order derivative,standard normal variate transformation (SNV),max-min normalization,and Savitzky-Golay (SG) smoothing.Key feature bands were selected using backward interval partial least squares (BiPLS) and synergy interval partial least squares (SiPLS).A prediction model was then developed using partial least square (PLS) to improve spectral processing performance and prediction accuracy.Experimental results demonstrated that SG smoothing provided the best preprocessing performance,while BiPLS showed superior selectivity for feature extraction.At an excitation wavelength (Ex) of 310 nm,the PLS model,optimized by combining SG smoothing and BiPLS feature extraction,achieved optimal performance,with the validation set correlation coefficient () of 0.919 ,the root mean square error of prediction () of 3.348 8 mg/L,and the prediction Bias of -0.283 5 mg/L.This method offers a practical approach for rapid COD detection in water quality assessment.