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

机译：基于数据挖掘的近红外光谱法对橘子生长地点的分类

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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%.
机译：生长地点的分类对于橙子行业的质量控制非常重要，由于其复杂的化学成分以及口味和大小的变化，这对工作也具有挑战性。按人类的感觉对它们进行分类的传统方法既费时又费钱。本文提出了一种新的基于近红外反射光谱技术的通用分类框架，该框架使用了数据挖掘技术。首先，通过主成分分析（PCA）减少原始NIR光谱数据，然后应用属性选择方法找到最佳特征子集。还引入了一种进化过程来测试本文中使用的五个分类器（决策树，KNN，朴素贝叶斯，SVM和ANN）的性能。在三个近红外光谱数据集上验证了提出的分类框架，该数据集来自中国15个不同地方的橙子的不同部分（包括水果表面和果汁的两个部分）。实验结果表明，果汁的近红外光谱是识别橙子生长位置的最合适的数据集，决策树是最好，最稳定的分类器，可以达到最高的平均预测率96.66％。

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