应用近红外光谱技术对茯苓药材进行定性定量检测研究

Qualitative and quantitative detection of Poria cocos by near infrared reflectance spectroscop

目的:利用傅里叶变换近红外漫反射光谱结合化学计量学方法对茯苓不同部位进行定性判别建模,并建立茯苓多糖的定量检测模型和茯苓多糖定量分析。方法:采用紫外分光光度法测定茯苓多糖含量,漫反射方式采集样品近红外光谱,采用一阶导数+矢量归一化法处理近红外光谱图,运用偏最小二乘法(PLS)建立光谱数据与多糖的定量校正模型,运用主成分分析(PCA)法建立茯苓定性模型,结果:偏最小二乘定量校正模型R为0.9440,RMSEC为0.072 1,RMSEP为0.076 2;定性分析模型对10个预测样品的判错数为0。结论:利用傅里叶变换近红外漫反射光谱快速判别不同部位茯苓的方法是可行的,多糖含量PLS定量分析模型从预测精度、稳定性及适应性考虑均具一定的通用性,具有良好的市场应用前景。

Objective： The present study is concerning qualitative and quantitative detection of Poria cocos quality based on FT-near infrared （FT-NIR）spectroscopy combined with chemometrics. Method： The Poria cocos polysaccharides contents were determined by UV. Transmission mode was used in the collection of NIR spectral samples. The pretreatment method was first derivation and vector normalization. Then principal component analysis （PCA） was used to build classification model and partialleast square （PLS） to build the calibration model. Result： The results showed that conventional criteria such as the R, root mean square error of calibration （ RM- SEC ） , and the root mean square error of prediction （RMSEP） are 0. 944 0, 0. 072 1 and 0. 076 2, respectively, the misclassifiedsam- pie is 0 using the qualitative model built by PCA. Conclusion： The prediction models based on NIR have a better performance with high precision, good stability and adaptability and can be used to predict the polysaecharose content of Poria cocos rapidly, which can provide a fast approach to discriminate the different parts of Poria cocos.