NIR analysis for batch process of ethanol precipitation coupled with a new calibration model updating strategy

Ethanol precipitation plays a major role in the pretreatment of Flos Lonicerae Japonicae of Qingkailing injection, and is also one of the most popular purification techniques in Chinese herbal medicines. In order to monitor and have a better understanding of the ethanol precipitation process, a PLS model was built based on NIR spectroscopy and HPLC analysis of chlorogenic acid content within the framework of FDA's PAT initiative. Nevertheless, due to the complex mechanism of and the raw materials’ natural variability introduced into the ethanol precipitation process, it was unable to foresee the variations in new batches which may jeopardize the robustness of the established model. Therefore, based on the simple interval calculation (SIC) theory, a new model expansion updating strategy which could continuously expand the variation coverage of the calibration model along with the batch proceeding of ethanol precipitation process was proposed. Effects of model updating were validated by an individual batch with 60 samples. After two times of updating, the root mean squared error of prediction (RMSEP) decreased from 0.268 mg mL−1 to 0.199 mg mL−1, while the insiders in the object status plot (OSP) increased from 44 to 58, demonstrating the good performance of the proposed approach.

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► NIR analysis for low content of API in ethanol precipitation process is verified.
► A dynamic PLS model updating strategy is brought forward.
► Simple interval calculation (SIC) theory is coupled into the updating strategy.
► Effectiveness of model updating is visualized by object status plot (OSP).