分析光谱学中的人工智能,第二部分:光谱学中的例子

IF 0.8 4区 化学 Q4 SPECTROSCOPY
Jerry Workman, H. Mark
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引用次数: 1

摘要

在这个由两部分组成的关于人工智能(AI)及其子领域机器学习(ML)的系列文章的第一部分(2023年2月)中,我们介绍了用于比较AI, ML和化学计量学的各种化学计量算法。这些算法包括用于分类、回归、聚类、集成学习、信号处理和成分分析的算法。现在,在第二部分中,我们讨论了人工智能在电子和振动光谱中的应用。我们还涉及了深度学习(DL)的一些应用,这是机器学习的一个子领域,其中使用了具有更多隐藏层的更复杂的人工神经网络(ann)。本专栏文章包括一些精选的参考文献,讨论了人工智能在分析化学和分子光谱学中的应用。我们给出了一些早期和晚期应用于不同振动光谱方法的人工智能和机器学习的例子,如拉曼、红外(FT-IR)、近红外(NIR)和紫外-可见(UV-vis)光谱技术。这篇文章的目的只是作为解决这个问题的众多研究手稿的一个样本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence in Analytical Spectroscopy, Part II: Examples in Spectroscopy
In Part I (February 2023) of this two-part series on artificial intelligence (AI), and its subfield machine learning (ML), we presented the variety of chemometric algorithms used to compare AI, ML, and chemometrics. These algorithms included those used for classification, regression, clustering, ensemble learning, signal processing, and component analysis. Now, in Part II, we discuss the applications of AI to electronic and vibrational spectroscopy. We also touch on some applications of deep learning (DL), which is a subfield of machine learning where more complex artificial neural networks (ANNs) with more hidden layers are used. This column article includes a number of selected references that discuss the application of AI in analytical chemistry and in molecular spectroscopy. We give a few early and late examples of AI and ML as applied to different vibrational spectroscopy methods, such as Raman, infrared (FT-IR), near-infrared (NIR), and ultraviolet–visible (UV-vis) spectroscopic techniques. This article is intended only as a sampling of the numerous research manuscripts addressing this subject.
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来源期刊
Spectroscopy
Spectroscopy 物理-光谱学
CiteScore
1.10
自引率
0.00%
发文量
0
审稿时长
3 months
期刊介绍: Spectroscopy welcomes manuscripts that describe techniques and applications of all forms of spectroscopy and that are of immediate interest to users in industry, academia, and government.
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