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

文献类型： 外文期刊

第一作者： Dan, Songjian

作者： Dan, Songjian;Tian, Fengchun;Yang, Simon X.;Dan, Songjian;Den, Lie

作者机构：

关键词： Growing location classification;NIR;data mining

期刊名称：INTELLIGENT AUTOMATION AND SOFT COMPUTING （ 影响因子：1.647； 五年影响因子：1.469 ）

ISSN： 1079-8587

年卷期： 2016 年 22 卷 2 期

页码：

收录情况： SCI

摘要： 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 human's 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%.

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