Name
Title | ||
|---|---|---|
Quantitative analysis of the varieties of apple using near infrared spectroscopy by principal component analysis and BP model |
Abstract | ||
|---|---|---|
Artificial neural networks (ANN) combined with PCA are being used in a growing number of applications. In this study, the fingerprint wavebands of apple were got through principal component analysis (PCA). The 2-dimensions plot was drawn with the scores of the first and the second principal components. It appeared to provide the best clustering of the varieties of apple. The several variables compressed by PCA were applied as inputs to a back propagation neural network with one hidden layer. This BP model had been used to predict the varieties of 15 unknown samples; the recognition rate of 100% was achieved. This model is reliable and practicable. So a PCA-BP model can be used to exactly distinguish the varieties of apple. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11589990_139 | Australian Conference on Artificial Intelligence |
Keywords | Field | DocType |
principal component analysis,infrared spectroscopy,best clustering,hidden layer,quantitative analysis,bp model,propagation neural network,principal component,pca-bp model,artificial neural network,fingerprint wavebands,2-dimensions plot,near infrared spectroscopy | Pattern recognition,Computer science,Near-infrared spectroscopy,Back propagation neural network,Fingerprint,Artificial intelligence,Backpropagation,Artificial neural network,Cluster analysis,Machine learning,Principal component analysis,Distributed computing | Conference |
Volume | ISSN | ISBN |
3809 | 0302-9743 | 3-540-30462-2 |
Citations | PageRank | References |
4 | 1.08 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong He | 1 | 78 | 12.64 |
| Xiaoli Li | 2 | 32 | 5.79 |
| Yongni Shao | 3 | 6 | 2.21 |