为了探讨基于舌诊的疾病快速筛查，运用可见和近红外光谱仪，采集149名志愿者舌尖的反射光谱并且进行反射率归一化处理。根据临床诊断结果将样本分为4组：健康组、高粘血症倾向组、脂肪肝患者组和冠心病患者组。运用主成分分析(PCA)结合人工神经网络(ANN)方法、偏最小二乘(PLS)方法和间隔偏最小二乘(iPLS)方法3种方法建立分类预测模型。预测准确率分别为75%，75%和85%。实验结果表明，在3种建模方法中，iPLS预测效果最好，与可见光波段相比，近红外波段含有更多与疾病分类相关的光谱信息。实验的结果表明，光谱法用于某些疾病的快速诊断具有较高的可行性。

To screen disease which based on tongue inspection rapidly, the reflection spectrum on the tongue tips of 149 volunteers were collected by visible and near-infrared spectrometer and then the normalized reflectivity was calculated. Samples were divided into four classes according to the clinical diagnosis information: healthy, hyperviscosity, fatty liver, and coronary heart disease groups. Spectra were then subjected to three different analysis methods: principle component analysis (PCA) combined with artificial neural network (ANN), partial least squares (PLS), and interval PLS (iPLS). The classification accuracy of each model are 75%, 75%, and 85%, respectively. The results show that iPLS method sees more robust than the others. And the results also show that near-infrared region including more disease information than visible region. Experimental results show that the application of the spectra for disease diagnosis is promising.