Polyphenol is one of important factors that cause the changes of taste and color in fruit-wine. To ensure the quality of fruit-wine, it is necessary to develop a fast measurement that monitors the change of polyphenol content during the fermentation. The ripe blueberry and mulberry were collected from different harvest batches. They were crushed respectively into juice, and their mixed juice was also mixed in certain ratio for fermentation in the small fermentation tanks. Those fermenting liquors from the different fermenting periods were collected through the off-line sampling access. The supernate was obtained by centrifugation pretreatment and totally 48 fermenting samples were preserved in the brown bottles for later use. The supernate were injected into three paralleled cuvettes, whose transmission spectrums were scanned by FT-NIR spectrometer, and their repeated readings were averaged for the spectral signals. Then, the total phenol content was measured by the national standard method (i.e. the standard curve was established between the absorbance value and the standard solution), and all samples were divided into the calibration and prediction set in a ratio of 2∶1 by duplex algorithm, which was used to calculate the spectral distance from the divided sample to the center of the rest samples. Interval partial least square (iPLS) was used to construct series of quantitative models between the transmission spectra and the total phenol contents in the training set, and the number of intervals was successively changed from 2 to 60. The innovate point in this work was that the consensual rule was used to integrate the calibrated member models (here referring to the iPLS model) into a consensus model and distribute the weighting coefficients. The linear combinations of member models were optimized to minimize the mean squared error (MSE) in the consensus model through the residual errors from the cross validation and their correlations. The weighted coefficient of each member model was solved by Lagrange multiplier method, so as to minimize the root mean square error of the consensus model. Compared with the global model of partial least squares (PLS), interval partial least-squares (iPLS) model with different number of spectral intervals, the consensual iPLS (C_iPLS) model commonly obtained a better performance. When the full spectra were divided into 39 intervals, the C_iPLS model, composed of three iPLS members models (those were 14th, 16th, 18th iPLS model respectively), got the minimum root mean squared error of cross validation (RMSECV) of 124.2, as well as the correlation coefficient of cross validation (Rcv) of 0.944, and the samples in prediction set were tested well with root mean square of prediction (RMSEP) of 163.4, as well as the correlation coefficient (Rp) of prediction of 0.931. In addition, the successive projection algorithm and the uninformative variable elimination were used to optimize the spectral model, but the predictive performances were not better than the proposed consensus model. By analyzing the correlation between the predicted residuals of each iPLS model, it was found that the consensus model commonly screened these member models featured with high prediction performance and low correlation between member models. Results showed that the spectral analysis technology combined with the consensus method could improve the prediction accuracy of the regression model, reduce the modeling number of variables, and could be employed off-line for the rapid detection of total phenol content in fruit wine.