Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set - Zhisheng Wu; Min Du; Xinyuan Shi; Bing Xu; Yanjiang Qiao (Journal of Analytical Methods in Chemistry)
This study demonstrated particle size effect on the measurement of saikosaponin A in Bupleurum chinense DC. by near infrared reflectance (NIR) spectroscopy. Four types of granularity were prepared including powder samples passed through 40-mesh, 65-mesh, 80-mesh, and 100-mesh sieve. Effects of granularity on NIR spectra were investigated, which showed to be wavelength dependent. NIR intensity was proportional to particle size in the first combination-overtone and combination region. Local partial least squares model was constructed separately for every kind of samples, and data-preprocessing techniques were performed to optimize calibration model. The 65-mesh model exhibited the best prediction ability with root mean of square error of prediction (RMSEP) = 0.492 mg·g−1, correlation coefficient RP=0.9221, and relative predictive determinant (RPD) = 2.58. Furthermore, a granularity-hybrid calibration model was developed by incorporating granularity variation. Granularity-hybrid model showed better performance than local model. The model performance with 65-mesh samples was still the most accurate with RMSEP = 0.481 mg·g−1, RP=0.9279, and RPD = 2.64. All the results presented the guidance for construction of a robust model coupled with granularity-hybrid calibration set.