通过使用矩阵识别的激光诱导分解光谱学对生物组织进行定量元素映射
Patrick Janovszky1, Albert Kéri1, Dávid J Palásti1
1Department of Inorganic and Analytical Chemistry, University of Szeged, Dóm square 7, Szeged, 6720, Hungary.
Scientific reports
|June 21, 2023
概括
将激光诱导分解光谱 (LIBS) 信号强度图转换为元素度图对于准确的组织分析至关重要. 本研究介绍了一种机器学习方法,用于精确地绘制生物组织中的元素.
科学领域:
- 分析化学 分析化学
- 生物医学工程 生物医学工程
- 频谱学是一种光谱学.
背景情况:
- 激光诱导分解光谱 (LIBS) 可以生成有机组织的信号强度图.
- 直接解释LIBS强度图可能会误解元素分布.
- 精确的元素度测绘对于生物和医学研究至关重要.
研究的目的:
- 为了证明转换LIBS信号强度图到元素度图的必要性.
- 为此转换提出并验证基于机器学习的方法.
- 提高生物组织元素分布评估的准确性.
主要方法:
- 开发一种机器学习方法,使用线性判别分析进行像素对像素矩阵识别.
- 应用矩阵匹配的外部校准用于定量分析.
- 使用猪 (猪) 脑组织样本进行验证.
主要成果:
- 使用自动化矩阵识别,在区分灰色和白色物质方面达到98%的准确性.
- 成功地将LIBS强度图转换为Na,K和Mg的元素度图.
- 获得的度值与文献和参考测量结果一致.
结论:
- 将LIBS强度图转换为元素度图对于准确的组织分析至关重要.
- 拟议的机器学习方法提供了一种可靠的方法,用于定量元素映射.
- 基于LIBS的元素度映射为组织组成提供了宝贵的见解.
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