Abstract
Model performance of the partial least squares method (PLS) alone and bagging-PLS was investigated in online near-infrared (NIR) sensor monitoring of pilot-scale extraction process in Fructus aurantii. High-performance liquid chromatography (HPLC) was used as a reference method to identify the active pharmaceutical ingredients: naringin, hesperidin and neohesperidin. Several preprocessing methods and synergy interval partial least squares (SiPLS) and moving window partial least squares (MWPLS) variable selection methods were compared. Single quantification models (PLS) and ensemble methods combined with partial least squares (bagging-PLS) were developed for quantitative analysis of naringin, hesperidin and neohesperidin. SiPLS was compared to SiPLS combined with bagging-PLS. Final results showed the root mean square error of prediction (RMSEP) of bagging-PLS to be lower than that of PLS regression alone. For this reason, an ensemble method of online NIR sensor is here proposed as a means of monitoring the pilot-scale extraction process in Fructus aurantii, which may also constitute a suitable strategy for online NIR monitoring of CHM.
Highlights
In 2004, the U.S Food and Drug Administration issued its “Process Analytical Technology (PAT)Industry Guide,” which encourages pharmaceutical companies to develop innovative drugs and ensure quality during manufacturing [1]
The purpose of the present paper is to compare the performance of partial least squares method (PLS) to that of bagging-PLS using online NIR sensors regarding the monitoring of the pilot-scale Fructus aurantii extraction process
The results showed that all the root mean square error of prediction (RMSEP) values of bagging-PLS using ensemble method, to the hesperidin, naringin and neohesperidin, were lower than those of the full-spectrum PLS model
Summary
In 2004, the U.S Food and Drug Administration issued its “Process Analytical Technology (PAT). Industry Guide,” which encourages pharmaceutical companies to develop innovative drugs and ensure quality during manufacturing [1]. Its purpose is to collect real-time information on all aspects of critical processes and to guide the process towards its desired state, ensuring the quality of the final product. Online technology is mentioned several times in the guide. Online near-infrared (NIR) sensors have been proven to be one of most efficient and advanced tools available for monitoring and controlling the production and processing of food, agricultural products, pharmaceuticals and petroleum. Killner et al utilized a NIR sensor for online monitoring of the progress of the catalyzed transesterification reactions of soybean oil that produced biodiesel [2]
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