Optimization of PLS modeling parameters via quality by design concept for Gardenia jasminoides Ellis using online NIR sensor.

Abstract

This paper discussed the process parameters optimization of partial least-square (PLS) modeling according to quality by design (QbD) concept. D-optimal design and online near-infrared (NIR) sensor were proposed to analysis the Geniposide in Gardenia jasminoides Ellis using above process parameters to achieve robustness PLS model. Four critical model parameters (CMPs) were identified to construct a D-optimal design, which included the selection of sample set, spectra pre-processing, latent variables and variable selection methods. NIR sensor dataset was obtained under a pilot scale system. The D-optimal design optimization strategy resulted in a robust PLS model with the optimal parameters, 1/2 samples for calibration sets through Baseline spectra pre-processing with SiPLS-selecting variables under 8 factors. The critical evaluation attributes (CEAs) of PLS model were recommended as follows: the RMSEC and Rcal2 of the calibration set were 0.005901 and 0.9983. The RMSEP and Rpre2 of the validation set were 0.02002 and 0.9845. The multivariate detection limit (MDL) was 1.143 × 10-3. Therefore, design space of CMPs which affected CEAs of PLS model was established. The result demonstrated that the proposed method was beneficial for the robustness of PLS model, which also showed a significant guideline for the design and development of PLS model.