从光谱数据的可变性到最佳预处理:利用不同粒度的杏仁粉改中的多变量误差
Barbara Giussani1, Manuel Monti2, Jordi Riu3
1Dipartimento Di Scienza e Alta Tecnologia, Università Degli Studi Dell'Insubria, Via Valleggio 9, 22100, Como, Italy. barbara.giussani@uninsubria.it.
Analytical and bioanalytical chemistry
|December 22, 2024
概括
绿色分析化学使用便携式近红外光谱 (NIR) 来准确分析原始样品. 了解原始数据错误是复杂矩阵可靠结果的关键,减少预处理需求.
科学领域:
- 分析化学 分析化学
- 绿色化学 绿色化学
- 频谱学是一种光谱学.
背景情况:
- 对于高效和可持续的分析实践的需求日益增加.
- 通过复杂的矩阵和最小的样本预处理来实现准确性和灵敏性的挑战.
- 需要使用具有较低检测极限的先进技术,符合简化程序和减少溶剂使用.
研究的目的:
- 讨论向绿色分析方法的转变,特别是便携式近红外 (NIR) 光谱.
- 为了说明样品特性 (例如颗粒大小) 和样品与仪器的整合的重要性.
- 突出分析原始数据中的多变量错误的必要性,以改善预处理.
主要方法:
- 专注于近红外 (NIR) 区域的便携式光谱技术.
- 案例研究:使用NIR光谱学预测杏仁粉中的改.
- 在原始数据中的多变量误差的调查.
主要成果:
- 证明了粒子大小和样本仪器集成在NIR分析中的重要性.
- 强调了原始数据分析在增强预处理策略方面的关键作用.
- 展示了适用于各种分析应用的方法.
结论:
- 便携式NIR光谱技术支持绿色分析化学原理.
- 准确分析原始样本需要了解原始数据的特征和潜在的错误.
- 方法论为各种分析挑战提供了一条可靠结果的途径.
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