Study of peach freshness predictive method based on electronic nose
- 作者：Hui Guohua ; deliver1982@gmail.com ; ghui@zjgsu.edu.cn ; Wu Yuling ; Ye Dandan ; Ding Wenwen ; Zhu Linshan ; Wang Lvye
- 关键词：Electronic nose ; Peach freshness prediction ; Principal component analysis ; Stochastic resonance ; Signal-to-noise ratio
- 刊名：Food Control
- 出版年：2012
- 期刊代码：49_09567135
- 类别：abs
- 出版时间：November, 2012
- 卷：28
- 期：1
- 页码：25-32
- 文件大小：1028 K
摘要
An electronic nose (E-nose) technique based peach freshness predictive model is discussed in this paper. Peaches are measured by a self-developed E-nose system with eight metal oxide semiconductors gas sensor array. Principal component analysis (PCA) and stochastic resonance (SR) are used for measurement data analysis. Results show that the E-nose can distinguish peaches between fresh and stale conditions. Microbiology, peach firmness and contents of total soluble solids (TSS) indices are measured to determine the peach freshness. The primary volatile gases emitted by peaches are characterized by gas chromatography-mass spectrometry (GC-MS) method. Signal-to-noise ratio (SNR) spectrum of peach E-nose measurement data is calculated through SR. The peach freshness predicting model is developed based on SNR maximums (Max-SNR) linear fitting regression. Validating experiments results demonstrate that the predicting accuracy of this model is 85%. The method takes some advantages including easy operation, rapid detection, high accuracy, good repeatability, etc.