Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set

Abstract EN

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.