利用生物分子独特的拉曼光谱特征进行生物组织分类研究。利用研制的拉曼探针穿刺生物组织, 获得生物分子的拉曼光谱信号数据, 并对数据进行基线校正和滤波预处理; 利用主成分分析法, 提取拉曼光谱数据的关键特征; 通过反向传播(BP)神经网络算法对这些特征进行组织分类; 利用动物组织样品上采集的拉曼光谱数据, 进行自动分类实验研究。结果表明, BP神经网络能够实现不同生物组织的分类, 且准确率达到95%。

Biological tissues are identified based on unique features of their Raman spectra. Raman signal data of biological tissues is acquired by a self-designed Raman probe, and preprocessed to rectify the baseline through filtering noises and stray light. The principal component analysis method is used to extract the critical Raman signal features of biological tissues, and then a back propagation (BP) neural network algorithm is used to classify the biological tissues by using these features. Automatic classification is implemented with the Raman spectrum data from the animal tissue phantoms. Experimental results show that the BP neural network is efficient to identify different animal tissues, and the accuracy rate reaches nearly 95%.