Measurement of sugar content of white vinegars using vis/near-infrared spectroscopy and back propagation neural networks

Visible and near infrared (VIS/NIR) spectroscopy combined with different calibration models was applied to predict the sugar content of white vinegars. The calibration set was composed of 240 samples, whereas 80 samples in the validation set. Partial least squares (PLS) models with or without pretreatments were developed and certain latent variables (LVs) were extracted by PLS analysis. The selected LVs were used as the inputs of BP neural network (BPNN) model. Finally, three models were developed. The prediction results indicated that PLS model with no pretreatment was better than that with pretreatments, and the best performance was obtained by BPNN model. The correlation coefficient, RMSEP and bias for validation set by BPNN model were 0.995, 0.135 and 0.035, respectively. The overall results indicated that VIS/NIR spectroscopy could be used as an alternative approach for the prediction of sugar content, and the BPNN models achieved the optimal prediction accuracy.