单一化合物数据的补充,以提高发酵特定的拉曼光谱模型的可转移性
Maarten Klaverdijk1, Marcel Ottens1, Marieke E Klijn2
1Department of Biotechnology, Delft University of Technology, Van Der Maasweg 9, Delft, 2629 HZ, The Netherlands.
Analytical and bioanalytical chemistry
|February 6, 2025
概括
这项研究增强了拉曼光谱模型的实时发酵监测. 用单一化合物光谱补充模型可以提高不同发酵类型的可转移性和准确性.
科学领域:
- 生物技术是生物技术.
- 分析化学 分析化学
- 工艺工程是过程工程.
背景情况:
- 拉曼光谱技术可以使用化学模型在发酵过程中实时量化分析物.
- 目前对多次发酵进行校准的模型缺乏针对工艺变化或不同模式的特异性和可转移性.
- 模型的可转移性差,需要对相关流程进行劳动密集型重新校准.
研究的目的:
- 为了提高拉曼光谱化学测量模型的可转移性和特异性,用于发酵过程.
- 为了在不需要大量重新校准的情况下,提高不同操作模式 (批量 vs. 输入批量) 的实时监控准确性.
- 为了验证单个化合物数据补充的战略,以改善模型.
主要方法:
- 部分最小平方模型 (PLS) 用Saccharomyces cerevisiae批发发酵数据对葡萄糖,乙醇和生物质进行校准.
- 模型被转移到料批次操作中,以评估可转移性.
- 补充了单个化合物光谱数据,以提高模型性能,而无需额外的工艺运行.
主要成果:
- 补充模型表明,目标分析物的特异性增加了.
- 在食批处理过程中,获得了足够的预测准确性 (RMSEP:3.06 mM葡萄糖,8.65 mM乙醇,0.99 g/L生物质).
- 在批量过程中保持了高预测准确度 (RMSEP:1.71 mM葡萄糖,4.20 mM乙醇,0.17 g/L生物质).
结论:
- 处理数据与单一化合物光谱相结合,为拉曼光谱应用提供了快速有效的策略.
- 这种方法可以在相关的发酵过程中实时监控过程,并改善了模型的可转移性.
- 该方法减少了对广泛重新校准的需求,使拉曼光谱对工业应用更加通用.
相关概念视频
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