Study of peach freshness predictive method based on electronic nose

被引：87

作者：

Hui Guohua ^{[1
]}

Wu Yuling ^{[1
]}

Ye Dandan ^{[1
]}

Ding Wenwen ^{[1
]}

Zhu Linshan ^{[1
]}

Wang Lvye ^{[1
]}

机构：

[1] Zhejiang Gongshang Univ, SSL, Coll Food Sci & Biotechnol, Food Safety Key Lab Zhejiang Prov, Hangzhou 310035, Zhejiang, Peoples R China

来源：

FOOD CONTROL | 2012年 / 28卷 / 01期

关键词：

Electronic nose; Peach freshness prediction; Principal component analysis; Stochastic resonance; Signal-to-noise ratio; STORAGE SHELF-LIFE; VOLATILE CONSTITUENTS; STOCHASTIC RESONANCE; OLIVE OILS; HARVEST; FRUIT; DISCRIMINATION; IDENTIFICATION; NECTARINES; MATURITY;

D O I：

10.1016/j.foodcont.2012.04.025

中图分类号：

TS2 [食品工业];

学科分类号：

0832 ;

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. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：25 / 32

页数：8

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