利用太赫兹时域光谱定性鉴别不同品种的苜蓿的研究（英文）
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摘要
这项研究是利用太赫兹时域光谱(THz-TDS)技术结合多元统计方法,对14种外表看起来极其类似的不同苜蓿牧草品种进行鉴定识别的可行性研究。通过实验测试获得苜蓿牧草品种在0.1～1.5THz有效波段的吸收系数和折射率等光谱参数,并且测试光谱揭示不同种类的苜蓿牧草在时间延迟、吸收强度和折射率等物理参量的平均值上都有所不同。尽管以上提到的这些太赫兹特征差异意味着太赫兹时域光谱(THzTDS)鉴定识别牧草品种是可行的,但是,由于没有特征吸收峰作为指纹谱识别依据,因此,本文利用多元统计方法聚类分析(CA)和主成分分析(PCA)在光谱参数和不同品种的苜蓿草种之间建立模型用以进行辅助验证,通过CA方法计算得到牧草间的欧氏距离以及通过PCA方法获得牧草的任何两个样本的PC1分值显示CA和PCA之间存在着很好的一致性,说明CA和PCA两种多样统计方法均能反映牧草间的差异。因此,太赫兹时域光谱技术结合多元统计方法能够成为一种有效的快速检测识别不同苜蓿牧草品种的方法,进而为将来建立牧草品种太赫兹光谱数据库奠定基础。
In this study,terahertz time-domain spectroscopy(THz-TDS)and multivariate statistical methods were used to demonstrate the feasibility of identifying fourteen alfalfa forage varieties that look extremely similar.THz spectra parameters,such as refractive index and absorption coefficient,were calculated from 0.1 to-1.5 THz,and the test spectrum revealed that different kinds of alfalfa grass seeds are different in time delay,absorption intensity and average refractive index.Although these characteristics differences mentioned above mean that the THz-TDS are feasible to identify alfalfa forage varieties,the statistical methods,including cluster analysis(CA)and principal component analysis(PCA),were used to build models between THz parameters and different alfalfa forage varieties because there was no characteristics absorption peak as fingerprint identification basis.The Euclidean distances of CA between forage grasses,and the scores of the first principal component(PC1)in PCA method reflect the forage-dependent differences,indicating the consistency between CA and PCA.Consequently,the combination of THz technology and statistical methods can be an effective method for the rapid identification of alfalfa forage with different properties.Furthermore,this combination method also provides a favorable basis for establishing the THz spectrum database of forage species in the future.
In this study,terahertz time-domain spectroscopy(THz-TDS)and multivariate statistical methods were used to demonstrate the feasibility of identifying fourteen alfalfa forage varieties that look extremely similar.THz spectra parameters,such as refractive index and absorption coefficient,were calculated from 0.1 to-1.5 THz,and the test spectrum revealed that different kinds of alfalfa grass seeds are different in time delay,absorption intensity and average refractive index.Although these characteristics differences mentioned above mean that the THz-TDS are feasible to identify alfalfa forage varieties,the statistical methods,including cluster analysis(CA)and principal component analysis(PCA),were used to build models between THz parameters and different alfalfa forage varieties because there was no characteristics absorption peak as fingerprint identification basis.The Euclidean distances of CA between forage grasses,and the scores of the first principal component(PC1)in PCA method reflect the forage-dependent differences,indicating the consistency between CA and PCA.Consequently,the combination of THz technology and statistical methods can be an effective method for the rapid identification of alfalfa forage with different properties.Furthermore,this combination method also provides a favorable basis for establishing the THz spectrum database of forage species in the future.
引文
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