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 partial least square (PLS) to build the calibration model.
Result: The results showed that conventional criteria such as the R, root mean square error of calibration (RMSEC), and the root mean square error of prediction (RMSEP) are 0.944 0, 0.072 1 and 0.076 2, respectively. The misclassified sample 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 polysaccharose content of Poria cocos rapidly, which can provide a fast approach to discriminate the different parts of Poria cocos.