Assessment of the human albumin in acid precipitation process using NIRS and multi-variable selection methods combined with SPA

Abstract

In this study, human albumin (HA) content was determined by near infrared spectroscopy (NIRS), which was an process analytical technology (PAT) tool, to realize process monitoring during acid precipitation. Eight batches of acid precipitation process samples were simulated in laboratory, and HA contents were determined by bromocresol green (BCG) method. Five batches were selected as calibration set and three batches were selected as validation set to build partial least squares regression (PLSR) model of HA content. Before the establishment of PLSR model, partial samples and different variable selection methods were compared in details and 35 variables were finally selected. The results showed that the successive projection algorithm (SPA) combined with the multi-variable selection methods (CC, UVE, CARS, SCARS, FiPLS, MWPLS, BiPLS) could obtain more useful information than the single method. The RMSECV and RMSEP values of the optimal PLSR model were 0.7038 g/L and 0.5893 g/L, respectively. Meanwhile, the results indicated that NIRS was an effective tool and could be successfully used for quantitative monitoring during acid precipitation process. To some extent, it could provide theoretical supports for quality control with a fast, nondestructive and low-cost NIRS in monitoring the complex biological production process.