Correlation Engine 2.0
Clear Search sequence regions

  • humans (1)
  • least squares (4)
  • near (5)
  • partial (2)
  • random (1)
  • root (1)
  • talc (2)
  • weight gain (5)
  • Sizes of these terms reflect their relevance to your search.

    This study was conducted to develop an in-line near-infrared (NIR) spectroscopy approach that allows real time quantitative analysis of the coating weight gain on a moving tablet surface during a coating process where talc is used. A holder directly inserting a diffuse reflectance probe into a coating pan was designed, and the optimal measurement conditions were identified using the design of experiments (DoE). The surface of the probe was kept clean of coating droplets at a maximum distance between the probe and the holder of 272.5 mm, leading to the acquisition of accurate spectral data. Under this condition, partial least squares regression (PLSR) was developed using the spectra from 7197 to 6233 cm-1, which covers the specific peaks for the core tablet and the coating solution. Under the same conditions, least squares regression (LSR) was developed using the univariate predictive analysis of the single absorption spectrum of talc at 7181 cm-1. In a comparison of the accuracy of the two models, PLSR was found to be more accurate as a result of testing the significance of differences between these distributions in terms of the root mean square errors of prediction (RMSEP) using a randomization t-test. Additionally, it confirmed that the predicted weight gain using NIR spectroscopy was correlated with the coating thickness measured using micro-CT. In conclusion, this study developed an in-line NIR measurement approach for the real-time monitoring of the coating weight gain of tablets and optimized the conditions by evaluating the effect of various factors.


    Byungsuk Kim, Young-Ah Woo. Optimization of in-line near-infrared measurement for practical real time monitoring of coating weight gain using design of experiments. Drug development and industrial pharmacy. 2021 Jan;47(1):72-82

    Expand section icon Mesh Tags

    Expand section icon Substances

    PMID: 33325254

    View Full Text