应用激光镊子拉曼光谱(LTRS)技术，俘获单个红细胞并收集其拉曼光谱。将主成分分析(PCA)算法和反向传播BP网络预测模型相结合，进行地中海贫血(简称地贫)红细胞类型的判别。PCA结果显示，正常对照与中间型α-地贫(HbH-CS)基本可区分，但正常对照与重型β-地贫，HbH-CS与重型β-地贫间差异不明显。将归一化处理的前5个主成分进行BP网络训练及预测，结果发现，正常对照与HbH-CS间预测正确率高达97.90%，正常对照与重型β-地贫，HbH-CS与重型β-地贫间预测正确率分别为90.72%和86.28%。该结果与平均拉曼光谱及主成分分析结果基本吻合。取不同的实验条件下收集的光谱进行同样的分析，3种组合的预测正确率略有不同，分别为95.28%，92.08%，91.85%，但呈现基本相同的规律。

The thalassemias are a group of anemias result from inherited defects in the production of hemoglobin. The current techniques for screening and diagnosis of thalassemia are time consuming and complex. A laser tweezers Raman spectroscopy (LTRS) setup was used to trap single erythrocyte from patients with thalassemias and normal donors, and to collect the Raman scatting of trapped cell. Blood samples obtained from 11 patients with non-deletional HbH disease (HbH-CS), 11 patients with β-thalassemia major, and 11 normal controls, were tested. Principal component analysis (PCA) algorithm combined with back-propagation neural network predictive model was performed to distinguish abnormal erythrocyte. The PCA results reveale that the difference between normal controls and HbH-CSs is significant with the predictive accuracy of BP network as high as 97.90 %. The predictive accuracy between normal controls and β-thalassemias major is 90.72 %, and 86.28 % between HbH-CSs and β-thalassemias major. These results tally closely with the corresponding averaged Raman spectra. Under different experimental condition, the predictive accuracy showes similar results. This pilot study can serve as a useful probe for developing a rapid, simple, reagent-free method for distinguishing of thalassemia erythrocytes.