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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Improved Algorithms for the Classification of Rough Rice Using a Bionic Electronic Nose Based on PCA and the Wilks Distribution
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Improved Algorithms for the Classification of Rough Rice Using a Bionic Electronic Nose Based on PCA and the Wilks Distribution

机译：基于PCA和Wilks分布的仿生电子鼻分类的改进算法。

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Principal Component Analysis (PCA) is one of the main methods used for electronic nose pattern recognition. However, poor classification performance is common in classification and recognition when using regular PCA. This paper aims to improve the classification performance of regular PCA based on the existing Wilks Λ-statistic (i.e., combined PCA with the Wilks distribution). The improved algorithms, which combine regular PCA with the Wilks Λ-statistic, were developed after analysing the functionality and defects of PCA. Verification tests were conducted using a PEN3 electronic nose. The collected samples consisted of the volatiles of six varieties of rough rice (Zhongxiang1, Xiangwan13, Yaopingxiang, WufengyouT025, Pin 36, and Youyou122), grown in same area and season. The first two principal components used as analysis vectors cannot perform the rough rice varieties classification task based on a regular PCA. Using the improved algorithms, which combine the regular PCA with the Wilks Λ-statistic, many different principal components were selected as analysis vectors. The set of data points of the Mahalanobis distance between each of the varieties of rough rice was selected to estimate the performance of the classification. The result illustrates that the rough rice varieties classification task is achieved well using the improved algorithm. A Probabilistic Neural Networks (PNN) was also established to test the effectiveness of the improved algorithms. The first two principal components (namely PC1 and PC2) and the first and fifth principal component (namely PC1 and PC5) were selected as the inputs of PNN for the classification of the six rough rice varieties. The results indicate that the classification accuracy based on the improved algorithm was improved by 6.67% compared to the results of the regular method. These results prove the effectiveness of using the Wilks Λ-statistic to improve the classification accuracy of the regular PCA approach. The results also indicate that the electronic nose provides a non-destructive and rapid classification method for rough rice.
机译：主成分分析（PCA）是用于电子鼻模式识别的主要方法之一。但是，使用常规PCA时，分类和识别中的分类性能很差。本文旨在基于现有的WilksΛ统计量（即，将PCA与Wilks分布相结合）提高常规PCA的分类性能。在分析了PCA的功能和缺陷之后，开发了将常规PCA与WilksΛ统计信息相结合的改进算法。使用PEN3电子鼻进行验证测试。采集的样品由在同一地区和同一季节种植的六种糙米（中香1，香湾13，瑶坪香，五峰油T025，品36和油油122）的挥发物组成。用作分析向量的前两个主成分不能执行基于常规PCA的糙米品种分类任务。使用将常规PCA与WilksΛ统计量相结合的改进算法，选择了许多不同的主成分作为分析向量。选择每个糙米品种之间的马氏距离的数据点集来估计分类的性能。结果表明，改进算法可以很好地完成水稻粗粒品种的分类任务。还建立了概率神经网络（PNN），以测试改进算法的有效性。选择前两个主成分（分别为PC1和PC2）以及第一个和第五个主成分（分别为PC1和PC5）作为PNN的输入，以对六个糙米品种进行分类。结果表明，与常规方法相比，改进算法的分类精度提高了6.67％。这些结果证明了使用WilksΛ统计量来提高常规PCA方法的分类准确性的有效性。结果还表明，电子鼻为糙米提供了一种非破坏性的快速分类方法。

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