The present research was focused on determination of the pH valueonline by visible and near-infrared spectroscopy. In the part of data gathering,fresh pork longissimus dorsi was moving at the constant velocity of 0.25 m?s-1on the conveyor belt, and the visible and near-infrared diffuse reflectancespectrum (350-1 000 nm) was captured. In the part of data processing, band of510-980 nm of the spectra was chosen to calibrate reflex distance, then to set uponline detection model of pH value in fresh pork by partial least squaresregression (PLSR). Kennard-stone algorithm was applied to divide the samples tothe calibration set and validation set. The performances of several PLSR modelsemploying various preprocessing methods including multiple scatter correction,derivative and both of them combined were compared. Further, the best performancemodel was optimized by interval PLSR to decrease the modeling variables ofwavelength. The results indicated that the PLSR model based on preprocessing ofmultiple scatter correction (MSC) combined with first derivative gave the bestperformance with 0.905 of the correlation coefficient for validation set and0.051 of the root of mean square errors for validation set. For the best PLSRmodel performance, the correlation coefficient of validation set increased to0.926 and the root of mean square errors for validation set to 0.045 in theoptimization interval PLSR model. However, only half of variables were used. Theresearch demonstrates that using visible and near-infrared spectroscopy todetermine fresh pork pH online is feasible.On-line determination; Fresh pork; pH(Y307441)资助