Journal of Innovative Optical Health Sciences, 2020, 13 (4): 2050015, Published Online: Aug. 7, 2020  

Spectra selection methods: A novel optimization way for treating dynamic spectra and in-line near infrared modeling

Author Affiliations
1 School of Pharmaceutical Sciences, Shandong University, Wenhuaxi Road, 44 Jinan 250012, P. R. China
2 Shandong SMA Pharmatech Co., Ltd., 165, Huabei Rd., High & New Technology Zone, Zibo Shandong 0533, P. R. China
3 National Glycoengineering Research Center, Shandong University, Wenhuaxi Road 44 Jinan 250012, P. R. China
Abstract
Near infrared (NIR) spectroscopy is now widely used in fluidized bed granulation. However, there are still some demerits that should be overcome in practice. Valid spectra selection during modeling process is now a hard nut to crack. In this study, a novel NIR sensor and a cosine distance method were introduced to solve this problem in order to make the fluidized process into "visualization". A NIR sensor was fixed on the side of the expansion chamber to acquire the NIR spectra. Then valid spectra were selected based on a cosine distance method to reduce the influence of dynamic disturbances. Finally, spectral pretreatment and wavelength selection methods were investigated to establish partial least squares (PLS) models to monitor the moisture content. The results showed that the root mean square error of prediction (RMSEP) was 0.124% for moisture content model, which was much lower than that without valid spectra selection treatment. All results demonstrated that with the help of valid spectra selection treatment, NIR sensor could be used for real-time determination of critical quality attributes (CQAs) more accurately. It makes the manufacturing easier to understand than the process parameter control.

Haiyan Wang, Ronghua Liu, Lei Nie, Dongbo Xu, Wenping Yin, Lian Li, Hengchang Zang. Spectra selection methods: A novel optimization way for treating dynamic spectra and in-line near infrared modeling[J]. Journal of Innovative Optical Health Sciences, 2020, 13(4): 2050015.

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