Predictions of acidity, soluble solids and firmness of pear using electronic nose technique

In this paper, responses of sensor array in electronic nose were employed to establish quality indices model able to describe the different picking dates of “xueqing” pear. The multivariate calibration methods, multiple linear regression (MLR), principal component regression (PCR) and partial least-squares regressions (PLS) were applied to predict the quality indices of “xueqing” pear from different picking dates based on the signal of electronic nose. All models for firmness and soluble solids content show a good prediction performance. However the acidity, there was a very poor correlation with the signal of the electronic nose. It was found that MLR led to more precise predictions than the other multivariate calibration methods. The results indicate that it is possible to use this non-destructive technique for measuring “xueqing” pear quality characteristics. The methods have the potential to estimate chemical and physical properties of pear from signal of electronic nose.