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Classification of Orange Growing Locations Based on the Near-infrared Spectroscopy Using Data Mining



The classification of growing locations is very important for quality control in the orange industries, which is also challenging work, because of its complex chemical composition and varies of taste and sizes. The traditional ways to classify them by human2019s sense are time consuming and at high cost. In this paper, a new general classification framework based on the Near-Infrared Reflection (NIR) spectroscopy using data mining technology was proposed. First, the raw NIR spectra data were reduced by the principal components analysis (PCA), and then an attribution selection method was applied to find the best feature subset. An evolution process was also introduced to test the performance of five classifiers (Decision Tree, KNN, Naive Bayesian, SVM and ANN) used in this paper. The proposed classification framework was verified on three NIR spectra datasets, which were collected from the different part of oranges (including two parts of fruit surface and juice) from 15 different places in china. The experimental results demonstrated that the juice NIR spectra is the most suitable data-set for identifying the orange growing locations, and the decision tree is the best and most stable classifier, which could achieve the highest average prediction rate of 96.66%.



Total Pages: 8
Pages: 229-236


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Volume: 22
Issue: 2
Year: 2015

Cite this document


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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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