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<p>To validate the feasibility of using near-infrared (NIR) spectroscopy for real-time monitoring of multiple active pharmaceutical ingredients dissolution, this study focused on Guizhi Fuling capsules and tablets. The NIR spectroscopy fiber probe was inserted into the dissolution apparatus and connected to a Fourier transform near-infrared spectrometer (FT-NIR) to capture spectral data. During the dissolution tests, dissolution behavior curves for seven components, gallic acid (GA), alibiflorin (ALI), paeoniflorin (PF), paeonol (PAE), amygdalin (AMY), cinnamaldehyde (CL), and cinnamic acid (CA) in the capsules, were obtained by sampling from the dissolution cups at specific time intervals. Linear regression was applied to models corrected using various pre-process techniques with the partial least squares (PLS) algorithm. Additionally, an artificial neural network (ANN), a nonlinear regression algorithm, was utilized to explore the complex relationship between spectra and multicomponent dissolution. Ultimately, the ANN model achieved a lower prediction mean square error (RMSEP) and relative error compared to the PLS model, with significantly higher correlation coefficient (<i>R</i><sub>p</sub>) for the validation set. The highest <i>R</i><sub>p</sub> value reached 0.8825. The paired t-test results also indicated no significant difference between predicted and measured values. Furthermore, the ANN model demonstrated the best predictive performance in the tablet experiments, achieving an <i>R</i><sub>p</sub> of 0.8134. The findings indicate that real-time monitoring of multicomponent drug dissolution using NIR spectroscopy combined with chemometric methods is feasible, offering a promising new direction to replace traditional dissolution testing.</p>

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On-Line Monitoring of Guizhi Fuling Capsules and Tablets Dissolution Behavior Using Near-Infrared Spectroscopy Combined with Chemometrics

  • 期刊论文

  • 发表日期：

    2025年3月5日

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  • Wenliang Dong,
  • Xi Wang,
  • Zhaobo Huang,
  • Cheng Ye,
  • Tuanjie Wang,
  • Hongda Zhang,
  • Zhenzhong Wang,
  • Wenlong Li

摘要

To validate the feasibility of using near-infrared (NIR) spectroscopy for real-time monitoring of multiple active pharmaceutical ingredients dissolution, this study focused on Guizhi Fuling capsules and tablets. The NIR spectroscopy fiber probe was inserted into the dissolution apparatus and connected to a Fourier transform near-infrared spectrometer (FT-NIR) to capture spectral data. During the dissolution tests, dissolution behavior curves for seven components, gallic acid (GA), alibiflorin (ALI), paeoniflorin (PF), paeonol (PAE), amygdalin (AMY), cinnamaldehyde (CL), and cinnamic acid (CA) in the capsules, were obtained by sampling from the dissolution cups at specific time intervals. Linear regression was applied to models corrected using various pre-process techniques with the partial least squares (PLS) algorithm. Additionally, an artificial neural network (ANN), a nonlinear regression algorithm, was utilized to explore the complex relationship between spectra and multicomponent dissolution. Ultimately, the ANN model achieved a lower prediction mean square error (RMSEP) and relative error compared to the PLS model, with significantly higher correlation coefficient (Rp) for the validation set. The highest Rp value reached 0.8825. The paired t-test results also indicated no significant difference between predicted and measured values. Furthermore, the ANN model demonstrated the best predictive performance in the tablet experiments, achieving an Rp of 0.8134. The findings indicate that real-time monitoring of multicomponent drug dissolution using NIR spectroscopy combined with chemometric methods is feasible, offering a promising new direction to replace traditional dissolution testing.