中文摘要：

采用表面解吸常压化学电离质谱（SDAPCI-MS）技术直接对5种化学型的樟树叶粉末片剂进行分析,获得其化学指纹谱图信息.采用主成分分析（PCA）、聚类分析（CA）和反向传输人工神经网络（BP-ANN）对谱图信息进行分析,获得各化学型樟树叶粉末片剂的特征质谱信息,进而对不同化学型样品进行判别.结果表明,在正离子模式下,SDAPCI-MS能快速获取樟树的化学指纹谱图;PCA分析中的PC1,PC2和PC3贡献率分别为79.9%,12.9%和4.2%,共计97.0%.SDAPCI-MS结合CA和BP-ANN测试样本准确率均为100%,能够快速、有效地判别出樟树化学型.

英文摘要：

Surface desorption atmospheric pressure chemical ionization mass spectrometry（ SDAPCI-MS） was selected to detect five chemotypes of C. camphora leaves powder and the raw mass spectral fingerprints of the powder samples were obtained. Principal component analysis（ PCA）,cluster analysis（ CA） and the back propagation artificial neural network technology（ BP-ANN） were used to analyze the spectral information. The results showed that the SDAPCI-MS technique could got mass spectral fingerprints of C. camphora quickly in positive ion mode. The contribution rates of PC1,PC2,PC3 were 79. 9%,12. 9% and 4. 2%,respectively,with a total of 97. 0% in PCA. The accuracy of discrimination of CA and BP-ANN of SDAPCI-MS was 100%.