Abstract：The sugar content and the matrix always are being changed during cider-making fermentation. In order to measure and monitor sugar content accurately and rapidly, it is necessary for the spectra to be sorted. Calibration models were established at different fermentation stages based on near infrared spectroscopy with artificial neural network. NIR spectral data were collected in the spectral region of 12 000-4 000 cm-1 for the next analysis. After the different conditions for modeling sugar content were analyzed and discussed, the results indicated that the calibration models developed by the spectral data pretreatment of straight line subtraction(SLS) in the characteristic absorption spectra ranges of 7 502-6 472.1 cm-1 at stageI and 6 102-5 446.2 cm-1 at stageⅡ were the best for sugar content. The result of comparison of different data pretreatment methods for establishing calibration model showed that the correlation coefficients of the models (R2) for stageI and Ⅱ were 98.93% and 99.34% respectively, and the root mean square errors of cross validation(RMSECV) for stageI and Ⅱ were 4.42 and 1.21 g·L-1 respectively. Then the models were tested and the results showed that the root mean square error of prediction (RMSEP) was 4.07 g·L-1 and 1.13 g·L-1 respectively. These demonstrated that the models the authors established are very well and can be applied to quick determination and monitoring of sugar content during cider-making fermentation.
Key words：Near infrared spectroscopy;Cider;Sugar content;Artificial neural network;Partial least square
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