挖掘红外光谱的全部潜力,用于分析技术性线素
Ivan Sumerskii1, Stefan Böhmdorfer2, Otgontuul Tsetsgee2
1Core Facility "Analysis of Lignocellulosics" (ALICE), BOKU University, Vienna, Konrad-Lorenz-Strasse 24, A-3430, Tulln, Austria.
ChemSusChem
|January 19, 2024
概括
这项研究引入了一种快速,具有成本效益的红外光谱法,用于技术性素表征. 它准确地预测了木质素的功能和摩尔质量,使生物经济能够进行高吞吐量分析.
科学领域:
- 生物质价值化和生物基材料科学.
- 分析化学和光谱学.
- 化学测量和数据分析.
背景情况:
- 技术性素对生物经济至关重要,但缺乏有效的表征方法.
- 需要高吞吐量分析,以满足对素特征的日益增长的需求.
- 现有的方法往往耗时,昂贵,或需要大量的样本.
研究的目的:
- 开发一种快速,低成本,高吞吐率的技术素表征方法.
- 利用基于红外 (IR) 光谱的多变量校准模型来预测素的特性.
- 通过使用广泛的素样本数据库,建立可靠的化学测量模型.
主要方法:
- 开发使用红外 (IR) 光谱的多变量校准模型.
- 化学测量分析应用于一个包含500多个技术素样本的综合数据库.
- 红外光谱学预测与传统方法 (如尺寸排除色谱) 进行比较,以确定分子质量.
主要成果:
- 高质量,统计学上可靠的模型用于预测功能组内容,误差低 (RMSEPs为4-16%).
- 显著节省分析时间 (>7小时),资源和样本质量.
- 提供了关于来自不同来源的 kraft 金和硫酸盐的变异性的见解.
结论:
- 具有多变量校准的红外光谱学为高通量技术素表征提供了有效的解决方案.
- 虽然对于功能组有效,但尺寸排除色谱对于详细的摩尔质量分布分析仍然是最佳的.
- 开发的方法解决了新兴生物经济体对非破坏性,快速和具有成本效益的素分析的关键需求.
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