电子鼻
重复性
主成分分析
化学
分析化学(期刊)
传感器阵列
主成分回归
生物系统
色谱法
数学
人工智能
计算机科学
统计
生物
作者
Guohua Hui,Yu-Ling Wu,Dandan Ye,Wenwen Ding,Zhu Linshan,Wang Lvye
出处
期刊:Food Control
[Elsevier]
日期:2012-11-01
卷期号:28 (1): 25-32
被引量:58
标识
DOI:10.1016/j.foodcont.2012.04.025
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI