Prediction of soluble solids content, firmness and pH of pear by signals of electronic nose sensors

The objective of this study was to investigate the predictability of an electronic nose for fruit quality indices. Responses signal of sensor array in electronic nose were employed to establish quality indices model for “xueqing” pear. The relationships were established between signal of electronic nose and the quality indices of fruit (firmness, soluble solids content (SSC) and pH) by multiple linear regressions (MLR) and artificial neural network (ANN). The prediction models for firmness and soluble solids content indicated a good prediction performance. The SSC model by ANN had a standard error of prediction (SEP) of 0.41 and correlation coefficient 0.93 between predicted and measured values, the model by ANN for the penetrating force (CF) had a 3.12 SEP and 0.94 coefficient, respectively. The results imply that it is possible to predict “xueqing” pear quality characteristics from signal of E-nose.