使用NIR-HIS分析优化压缩带的抗拉强度预测模型
Juthamat Wanfueangfu1, Jetsada Posom2, Duchdoune Teerasukaporn3
1Division of Pharmaceutical Technology, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
Heliyon
|November 11, 2024
概括
近红外高光谱成像光谱学 (NIR-HIS) 能够有效地预测压缩的带的拉力强度. 使用较少波长的优化模型减少了药品质量控制的成本和时间.
科学领域:
- 制药技术 制药技术 制药技术
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 拉力强度 (TS) 对于压缩卷带的质量至关重要.
- 准确的TS预测对于制药制造过程控制至关重要.
研究的目的:
- 评估近红外高光谱成像光谱学 (NIR-HIS) 用于预测TS.
- 评估表面曲率,光谱预处理和变量选择对预测模型的影响.
- 开发高效可靠的TS预测模型用于质量控制.
主要方法:
- 使用了部分最小平方回归 (PLSr) 和多重线性回归 (MLR).
- 光谱预处理包括平均中心化 (MC) 和标准正常变量 (SNV).
- 变量选择方法:回归系数 (REG),预测中的变量重要性 (VIP),CARS和GA.
主要成果:
- 表面曲率没有显著影响模型预测性能 (p=0.39).
- 使用CARS和MC预处理的PLSr将波长从182减少到5,实现了高预测精度 (R2pred=0.97,RPD=5.75,RMSEP=7.60%).
- 使用5个选定的波长的MLR模型显示了与PLSr.相比的性能.
结论:
- NIR-HIS是一种可靠的技术,用于预测紧带的拉力强度.
- 使用最小波长的优化模型可以大大减少处理时间和成本.
- 开发的模型是实时制药质量控制的宝贵工具.
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