Feature extraction of SERS spectrum of honey using Principal Component Analysis

机译：基于主成分分析的蜂蜜SERS光谱特征提取

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Honey purity can be identified using combinations of Surface-Enhanced Raman Spectroscopy (SERS), digital signal processing and Principal Component Analysis. This paper focuses on the revelation of honey composition and extraction of honey features. The honey was diluted before it was passed to Surface Enhanced Raman Spectroscopy to enhance its spectrum. After the spectrum was processed by removing background interferences, the honey features were then extracted and analyzed using Principal Component Analysis. The significant features were selected for future classification using eigenvalue one criterion. It was found that coefficients 1 to 9 of all honey samples are significant and can be used as input to a classifier.

机译：可以使用表面增强拉曼光谱（SERS），数字信号处理和主成分分析的组合来识别蜂蜜的纯度。本文重点介绍了蜂蜜成分的启示和蜂蜜特征的提取。在将蜂蜜通过表面增强拉曼光谱法以增强其光谱之前将其稀释。通过消除背景干扰对光谱进行处理后，然后提取蜂蜜特征并使用主成分分析进行分析。使用特征值一准则选择显着特征用于将来的分类。发现所有蜂蜜样品的系数1到9都很重要，可以用作分类器的输入。

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来源
《2014 IEEE Conference on Biomedical Engineering and Sciences》|2014年|502-504|共3页
会议地点 Miri(MY)
作者
Raduan M.F.; Mansor W.; Lee Khuan Y.; Radzol /A/. R. Mohd;
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作者单位

Fac. of Electr. Eng., Univ. Teknol. MARA, Shah Alam, Malaysia;

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原文格式 PDF
正文语种 eng
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关键词
Raman spectra; eigenvalues and eigenfunctions; feature extraction; feature selection; food products; principal component analysis; production engineering computing; signal processing; SERS spectrum; digital signal processing; eigenvalue one criterion; feature extraction; feature selection; honey purity; principal component analysis; surface-enhanced Raman spectroscopy; Biomedical engineering; Conferences; Eigenvalues and eigenfunctions; Feature extraction; Principal component analysis; Raman scattering; Sugar;

机译：拉曼光谱;特征值和特征函数;特征提取;特征选择;食品;主要成分分析;生产工程计算;信号处理; SERS光谱;数字信号处理;特征值一准则;特征提取;特征选择;蜂蜜纯度;主要成分分析表面增强拉曼光谱;生物医学工程学;会议;特征值和特征函数;特征提取;主成分分析;拉曼散射;糖;;

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1. Multiscale 2-D Singular Spectrum Analysis and Principal Component Analysis for Spatial–Spectral Noise-Robust Feature Extraction and Classification of Hyperspectral Images [J] . Ping Ma, Jinchang Ren, Huimin Zhao, Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of . 2021,第1期

机译：多尺度2-D奇异谱分析和空间谱稳健功能提取和超光谱分类的主成分分析
2. The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform [J] . Renping Shao, Wentao Hu, Yayun Wang, Measurement . 2014,第Null期

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3. Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis [J] . Arjon Turnip, Keum-Shik Hong, Myung-Yung Jeong BioMedical Engineering OnLine . 2011,第1期

机译：基于自适应非线性主成分分析的P300成分实时特征提取
4. Feature extraction of SERS spectrum of honey using Principal Component Analysis [C] . Raduan M.F., Mansor W., Lee Khuan Y., IEEE Conference on Biomedical Engineering and Sciences . 2014

机译：用主成分分析，蜂蜜SERS光谱特征提取
5. Principal component analysis based feature extraction methods applied to biomedical and communication network data. [D] . Xie, Shengkun. 2010

机译：基于主成分分析的特征提取方法应用于生物医学和通信网络数据。
6. Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis [O] . Arjon Turnip, Keum-Shik Hong, Myung-Yung Jeong 2011

机译：基于自适应非线性主成分分析的P300成分实时特征提取
7. Multiscale 2-D Singular Spectrum Analysis and Principal Component Analysis for Spatial–Spectral Noise-Robust Feature Extraction and Classification of Hyperspectral Images [O] . Ping Ma, Jinchang Ren, Huimin Zhao, 2021

机译：多尺度2-D奇异谱分析和空间谱稳健功能提取和超光谱分类的主成分分析

Feature extraction of SERS spectrum of honey using Principal Component Analysis

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