Aiming at the nonlinear correlation characteristic of visible/near infrared spectra and the corresponding acidity of bayberry juice,one mixed algorithm was presented to predict the acidity of bayberry juice with partial least squares(PLS) and artificial neural network(ANN).The values of correlation coefficient(r),the root mean squared error of prediction(RMSEP),and bias were used to estimate the mixed model.PLS was used to find some sensitive spectra related to acidity in juice,and the values of spectral absorptance corresponding to them were regarded as the input neurons of ANN.Remnant values by subtracting standard values and validation values were regarded as the output neurons of ANN.The calibration equation developed from them was used to predict the constituent values for the independent spectra of 30 samples.The results indicate that the observed results by using PLS-ANN(r=0.939,RMSEP=0.218,Bias=-0.121) are better than those obtained by PLS(r=0.921,RMSEP=0.228,Bias=-0.132).