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NIR quantitative model trans-scale calibration from small scale to pilot scale via directed DOSC-SBC algorithm.

Xinyuan Zhang1, Pei Yang1, Yinxue Hao1

  • 1School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|December 1, 2022
PubMed
Summary
This summary is machine-generated.

A new Directed DOSC-SBC algorithm enables accurate Near-Infrared (NIR) quantitative model predictions across different scales. This method successfully transfers models from small to pilot scale, improving real-time monitoring in pharmaceutical processes.

Keywords:
Model calibrationNIR quantitative modelPilot scaleSmall scaleTCM preparation

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Pharmaceutical Technology

Background:

  • Near-Infrared (NIR) quantitative models often fail when applied to data from different scales due to spectral variations.
  • Direct Orthogonal Signal Correction combined with Slope/Bias Correction (DOSC-SBC) is a known method for spectral preprocessing.
  • Effective model transfer across scales is crucial for robust process analytical technology (PAT).

Purpose of the Study:

  • To develop and validate a novel algorithm, Directed DOSC-SBC (DDOSC-SBC), for overcoming scale-related limitations in NIR quantitative modeling.
  • To enable accurate trans-scale prediction of critical quality attributes (CQAs) in pharmaceutical manufacturing.
  • To demonstrate the applicability of the DDOSC-SBC algorithm for real-time NIR monitoring in Chinese herbal medicine preparation.

Main Methods:

  • The proposed Directed DOSC-SBC (DDOSC-SBC) algorithm was developed by integrating Direct Orthogonal Signal Correction (DOSC) with Slope/Bias Correction (SBC).
  • Representative samples from the test set were selected to determine optimal spectral matrix transfer parameters.
  • Spectral systematic errors between modeling and test sets were corrected using SBC to facilitate trans-scale prediction.
  • NIR data and CQAs were collected during small-scale and pilot-scale fluidized bed granulation of dextrin and honeysuckle extraction.

Main Results:

  • A small-scale NIR quantitative model, calibrated using the DDOSC-SBC algorithm guided by pilot-scale samples, accurately predicted pilot-scale test samples.
  • Successful trans-scale calibration of the NIR quantitative model from small to pilot scale was achieved.
  • The model transfer demonstrated a high level of performance with a Ratio of Performance to Deviation (RPD) value greater than 3.5 and a Residual Error of Prediction (RSEP) value below 10%.

Conclusions:

  • The DDOSC-SBC algorithm is an effective method for trans-scale calibration of NIR quantitative models.
  • This approach significantly improves the applicability and accuracy of NIR models across different manufacturing scales.
  • DDOSC-SBC offers a viable solution for real-time monitoring of CQAs in pharmaceutical processes, particularly for Chinese herbal medicine preparation.