为了实现对肝癌的早期实时和在体探测，基于前期搭建的光纤共聚焦后向散射（FCBS）光谱仪获取肝癌细胞的显微后向散射光谱，分别使用主成分分析（PCA）和支持向量机（SVM）两种算法，对获得的正常肝细胞株（L02）、低转移潜能肝癌细胞株（MHCC97-L）和高转移潜能肝癌细胞株（HCCLM3）三种细胞的后向散射光谱进行分类。使用PCA对获得的三种细胞光谱数据进行降维分析，得到的前两个主成分综合了全部信息的95.4% ，由主成分1和主成分2的得分图可以观察到，三种细胞在直观上有明显的区分；对同一数据集选取69例对象通过SVM机器学习算法训练分类模型，随机抽取50例作为训练集，19例作为预测集，最终分类的准确度达到了94.7%。实验结果表明：使用光纤共聚焦后向散射（FCBS）光谱仪获取的细胞显微后向散射光谱可以分别通过PCA和SVM对不同转移潜能的肝癌细胞进行自动分类，这将为研究活检提供必要的检测手段。

In order to realize the clinical detection of hepatocellular carcinoma (HCC) in vivo, real time and earlier, a normal liver cell line L02, a low-metastatic-potential hepatocellular carcinoma cell line MHCC97-L and a high-metastatic-potential hepatocellular carcinoma cell line HCCLM3 were measured, respectively, based on the established fiber confocal back scattering micro-spectrometer (FCBS). The principal component analysis (PCA) and the support vector machine (SVM) algorithm were used to classify the acquired spectrums, respectively. The PCA was used to study the spectrum in wavelength range of 500-900 nm. The first two of the principal components have taken 95.4% of the whole information; therefore, the three kinds of cell distribution were distinguished obviously on the scores diagram of principal component. 69 object data were chosen randomly to train the SVM classification model. 50 sets of these data were used as training sets and 19 sets were used as testing sets. The classification accuracy of the model has reached 94.7%. These results have indicated that the back-scattering micro-spectra of cells measured by fiber confocal back scattering micro-spectrometer (FCBS) combined PCA or SVM could classify liver cancer cells with different metastatic potential automatically. This will provide the necessary testing tools for the research of hepatocellular carcinoma cell in vivo and real time.