摘要
利用傅里叶变换红外光谱仪收集6个不同产地黄柏的红外光谱,原始光谱经过多点基线校正、五点平滑预处理后,统计各个产地黄柏的平均光谱,结果显示,6个产地黄柏的平均红外光谱整体上相似;在指纹特征区域1 800~500 cm-1,选取经过归一化的红外光谱,建立主成分分析鉴别模型。建模结果表明,三个主成分能代表98%的指纹区变量信息,样本在主成分空间中聚集成为不同的类别,基本实现不同产地黄柏的鉴别,此外,在一定程度上,样本分布疏密度反映样本的亲缘关系。提取模型的载荷因子,分析显示,6个产地黄柏的差异主要在蛋白质、糖类、脂类、生物碱类、黄柏甾醇类、黄柏内酯、黄柏酮、黄柏酮酸八种物质成分上体现。因此,红外光谱结合主成分分析法可以快速、无损地鉴别不同产地黄柏,并且能反映不同产地黄柏物质成分含量的差异。
Fourier transform infrared spectrometer was used to collect infrared spectra of Cortex Phellodendri from six different regions.Original spectra were preprocessed by carrying out appropriate baseline correction and five-points smoothing,and the averaged spectra of Cortex Phellodendri from the six origins were analyzed.As a result,the averaged spectra looked quite similar.The normalized spectra were selected to construct principal component analysis model in the range of fingerprint region 1 800~500 cm-1,and according to the model,the first three principal components accounted for 98% of the variance information in the fingerprint region,and each sample was able to form distinct cluster in the principal component space,then the identification of Cortex Phellodendri from the six regions was basically achieved;besides,to some extent,the sparse density of the samples distribution reflected the genetic relationship.The loading factors of the model were analyzed,and the results indicated that the differences between Cortex Phellodendri samples mostly depended on the contents of protein,carbohydrates,lipids,alkaloids,sterols,obaculactone,oba-cunone,and obacunonlc acid.On the whole,combined with principal component analysis,FTIR provides an effective way to evaluate the herbal Cortex Phellodendri rapidly and nondestructively,which also reflects the content difference of material composition.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2011年第5期1258-1261,共4页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(31060128)
广西自然科学基金项目(桂科自0991078)
广西自然科学基金项目(桂科回0731013)
广西研究生教育创新基金项目(2010106020702M44)资助
关键词
红外光谱法
黄柏
主成分分析
载荷因子分析
Infrared spectroscopy
Cortex Phellodendri
Principal component analysis
Loading factor analysis