Application of Electronic Nose and Statistical Analysis to Predict Quality Indices of Peach

详细信息

下载全文

推荐本文 |

• 作者：Hongmei Zhang (12)
  Jun Wang (1) jwang@zju.edu.cn
  Sheng Ye (1)
  Mingxun Chang (3)
• 关键词：Peach &#8211 ; Quality &#8211 ; Principal component regression (PCR) &#8211 ; Partial least ; squares regressions (PLS) &#8211 ; Electronic nose
• 刊名：Food and Bioprocess Technology
• 出版时间：January 2012
• 出版年：2012
• 期刊代码：pc332_1935-5130
• 类别：ch
• 卷：5
• 期：1
• 页码：65-72
• 数据来源：sp

摘要

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 掳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 掳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.