基于多期数据融合校准方法的LIBS定量分析的长期可重复性改进
Neng Zhang1, Zhongqi Hao2, Li Liu1
1Key Laboratory for Optoelectronic Information Perception and Instrumentation of Jiangxi Province, Nanchang Hangkong University, Nanchang, 330063, China.
Talanta
|November 22, 2024
概括
提高激光诱导分解光谱学 (LIBS) 定量分析的长期可重现性至关重要. 使用基于遗传算法的反向传播人工神经网络 (GA-BP-ANN) 的新型多期数据融合方法显著提高了测量准确性和一致性.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 材料科学 材料科学 材料科学
背景情况:
- 在定量激光诱导分解光谱 (LIBS) 分析中,长期可重现性仍然是一个重大挑战.
- 在长时间内进行准确和一致的测量对于可靠的材料表征至关重要.
研究的目的:
- 开发和验证一种新的校准方法,以提高LIBS定量分析的长期可重复性.
- 通过合并多个实验期间收集的数据来提高LIBS测量的可靠性.
主要方法:
- 为LIBS提出了一个多期数据融合校准策略.
- 校准模型是使用20天内每天收集的标准样本数据建立的.
- 基于遗传算法的逆向传播人工神经网络 (GA-BP-ANN) 模型使用融合数据的性能与传统方法 (IS-1和IS-10) 相比较.
主要成果:
- 纳入多期数据融合的GA-BP-ANN模型表现出卓越的性能.
- 这种先进的模型在预测元素度 (Mn,Ni,Cr,V) 时实现了最低的平均相对误差 (ARE) 和平均标准偏差 (ASD).
- 合并数据方法显著提高了可重复性,而不是在单日或有限期数据上训练的模型.
结论:
- 拟议的多期数据融合策略,特别是与GA-BP-ANN相结合时,为增强LIBS长期可重复性提供了一个强大的解决方案.
- 这种新的方法提供了一种可靠的方法,可以在各种应用中提高定量LIBS测量的准确性和一致性.
- 这些发现突显了先进数据融合技术在克服光谱分析局限性的潜力.
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