Detection of germination rate of tomato seeds by electronic nose

被引：0

作者：

Cheng S. ^{[1
]}

Wang J. ^{[1
]}

Ma Y. ^{[2
]}

Wang Y. ^{[1
]}

Wei Z. ^{[1
]}

机构：

[1] College of Bio-systems Engineering and Food Science, Zhejiang University

[2] College of Information, Zhejiang University of Science and Technology

来源：

Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2011年 / 27卷 / 12期

关键词：

Electronic nose; Neural networks; Nondestructive examination; Principal component analysis; SVM; Tomato seeds;

D O I：

10.3969/j.issn.1002-6819.2011.12.025

中图分类号：

学科分类号：

摘要：

In order to find out a fast nondestructive examination method for germination rate of tomato seeds, different samples of tomato seeds with six kinds of germination rates were analyzed by electronic nose, and which were classified through principal component analysis (PCA) and linear discrimination analysis (LDA). The result shows that the electronic nose could distinguish the tomato seeds with germination rate of 90%, 80%, 50%-70%and un-germination seeds. However, samples with germination rate of 50%-70% were overlapped. Based on PCA and LDA, BP neural network (BPNN) and support vector machine (SVM) were introduced in the classification. The results showed that the recognition rates for germination rate of tomato seeds by the two methods reached to 93.6% and 97.4% respectively with training set, and 65.2% and 72.7% respectively with forecast set. Compared to BPNN, SVM method has less predicting errors, which has better forecasting performance.

引用

页码：132 / 135

页数：3

共 13 条

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