Neural networks in multivariate calibration.
Neural networks in multivariate calibration.
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DOI：
10.1039/a805562i
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发表时间：
1998
期刊：
影响因子：
--
通讯作者：
F. Despagne;D. Massart
中科院分区：
文献类型：
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作者：
F. Despagne;D. Massart
正如Zupan和Gasteiger1在他们的综述中提到的应用数量所说明的那样，人工神经网络（NNs）现在已经在许多化学领域得到了认可。在1996年的化学计量学基础综述中，在信号处理、曲线分辨率、校准、参数估计、QSAR、模式识别，当然还有人工智能的章节中报道了2个神经网络的应用。Smits等人3和Svozil等人4提出了关于化学中神经网络的教程（后者包含了神经网络的广泛互联网资源列表），Cirovic综述了神经网络在光谱学中的不同类型应用。5本教程仅限于神经网络在化学数据多元校准中的应用，这是化学计量学出版物的重要来源。神经网络作为多变量校准建模工具的潜力已经确立，现在必须集中精力开发适当的方法，以确保神经网络始终在理想条件下使用；这就是本教程的目标。Bos等人6对定量分析中神经网络的实际方面进行了极好的概述。这些方面的大部分将在这里再次提出，特别是为了建立术语，我们将根据最近在神经网络研究中获得的结果包括一些建议。我们将把自己限制在具有最流行的误差反向传播学习规则的多层前馈型（也称为多层感知机，MLP）的神经网络。本教程组织如下：在第2节中，我们提醒读者神经网络是如何出现的，并解释了它们的基本原理。第3节专门介绍神经网络为分析化学家提供的可能性。我们介绍了神经网络的一些最普遍的方面（灵活性，黑盒方面），并强调了它们的主要局限性。在第4节中，我们考虑了更多的技术方面，并提出了一种使用神经网络开发校准模型的方法。该方法的一个不可忽略的部分是专门用于数据处理的，我们试图概述特定于神经网络建模的陷阱。我们考虑拓扑优化，并引入有助于开发和解释神经网络模型的技术。本教程中讨论的不同方面通过文献中的应用程序示例加以说明。
Artificial neural networks (NNs) have now gained acceptance in numerous areas of chemistry, as illustrated by the number of applications mentioned by Zupan and Gasteiger1 in their review. In the 1996 Chemometrics fundamental review, 2 NN applications were reported in sections concerning signal processing, curve resolution, calibration, parameter estimation, QSAR, pattern recognition and of course artificial intelligence. Tutorials on NNs in chemistry were proposed by Smits et al. 3 and Svozil et al. 4 (the latter contains an extensive list of Internet resources for NNs) and different types of applications of NNs to spectroscopy were reviewed by Cirovic. 5 This tutorial is restricted to the application of NNs for multivariate calibration with chemical data, which is an important source of publications in chemometrics. The potential of NNs as modelling tools for multivariate calibration is well established, and efforts must now focus on developing proper methodologies to ensure that NNs are always used in ideal conditions; this is the goal of this tutorial. Bos et al. 6 presented an excellent overview of practical aspects of NNs in quantitative analysis. Most of these aspects will be presented again here, in particular in order to establish the terminology, and we will include some recommendations according to recent results obtained in NN research. We will restrict ourselves to NNs of the multi-layer feed-forward type (also called multi-layer perceptron, MLP) with the error back-propagation learning rule that is the most popular.The tutorial is organised as follows. In Section 2, we remind readers how NNs came on to the scene and explain their basic principles. Section 3 is dedicated to the possibilities offered by NNs to analytical chemists. We present some of the most general aspects of NNs (flexibility, black-box aspect) and emphasise their main limitations. In Section 4 we consider more technical aspects and propose a methodology for the development of calibration models with NNs. A non-negligible part of this methodology is dedicated to data handling, and we try to outline the pitfalls specific to NN modelling. We consider topology optimisation and introduce techniques that can help in developing and interpreting NN models. The different aspects discussed in the tutorial are illustrated with examples of applications from the literature.