Treffer: Quantitative analysis of the varieties of apple using near infrared spectroscopy by principal component analysis and BP model
Title:
Quantitative analysis of the varieties of apple using near infrared spectroscopy by principal component analysis and BP model
Authors:
Source:
AI 2005 (advances in artificial intelligence)Lecture notes in computer science. :1053-1056
Publisher Information:
Berlin: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 6 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Analyse amas, Cluster analysis, Analisis cluster, Analyse composante principale, Principal component analysis, Análisis componente principal, Analyse quantitative, Quantitative analysis, Análisis cuantitativo, Classification, Clasificación, Empreinte digitale, Fingerprint, Huella digital, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Modélisation, Modeling, Modelización, Réseau neuronal, Neural network, Red neuronal, Spectrométrie IR, Infrared spectrometry, Espectrometría IR
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310029, China
ISSN:
0302-9743
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17345144
Database:
PASCAL Archive
Weitere Informationen
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.