Robust PLS prediction model for saikosaponin a in Bupleurum chinense DC. Coupled with granularity-hybrid calibration set

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 r p = 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,r p = 0.9279,and rpd = 2.64. all the results presented the guidance for construction of a robust model coupled with granularity-hybrid calibration set. © 2015 zhisheng wu et al.