Abstract
In this study, responses of a sensor array were employed to establish a quality index model able to describe the different picking date of peaches. The principal component regression (PCR) and partial least-squares regressions (PLS) model represent very good ability in describing the quality indices of the selected three sets of peaches in calibration and prediction. The results showed that the PLS model represents a good ability in predicting quality index, with high correlation coefficients (R = 0.86 for penetrating force [CF]; R = 0.83 for sugar content [SC]; R = 0.83 for pH) and relatively low standard error of prediction (SEP; 8.77 N, 0.299 °Brix, and 0.2 for CF, SC, and pH, respectively). The PCR model had high correlation coefficients (R = 0.84, 0.82, 0.78 for CF, SC, and pH, respectively) between predicted and measured values and a relatively low SEP (7.33 N, 0.44 °Brix, 0.21 for CF, SC, and pH, respectively) for prediction. These results prove that the electronic noses have the potential to assess fruit quality indices.
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The authors acknowledge the financial support of the Chinese National Foundation of Nature and Science through project 30771246 and the National High Technology Research and Development Program of China through project 2006AA10Z212.
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Zhang, H., Wang, J., Ye, S. et al. Application of Electronic Nose and Statistical Analysis to Predict Quality Indices of Peach. Food Bioprocess Technol 5, 65–72 (2012). https://doi.org/10.1007/s11947-009-0295-7
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DOI: https://doi.org/10.1007/s11947-009-0295-7