Data Interpretation by some Common Chemometrics Methods
Data Interpretation by some Common Chemometrics Methods
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DOI：
10.1002/(sici)1521-4109(199811)10:16
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发表时间：
1998-11
期刊：
影响因子：
3
通讯作者：
S. Kokot;M. Grigg;H. Panayiotou;Tran Dong Phuong
中科院分区：
文献类型：
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作者：
S. Kokot;M. Grigg;H. Panayiotou;Tran Dong Phuong
这是澳大利亚研究委员会电化学微传感阵列研讨会受邀演讲的一部分。其目的是演示如何使用更常见的化学计量学方法进行数据分析，特别是模式识别。本文的实质是解决为什么使用化学计量学进行多元数据分析有优势的问题？如果一个人决定冒险进入这一领域，如何应对这一挑战。我们通过许多示例来解决“为什么”问题，这些示例强调了模式识别数据分析的一些优点，特别是对于非常相似或复杂的多元测量，例如光谱或循环伏安图。考虑的化学计量学方法是探索性主成分分析（PCA）和相关数据预处理、PC和载荷图以及双图；数据分类模型被认为是模式识别阶段之外可能的有用扩展。这些示例不一定基于电化学问题，但可以容易地理解电化学生物传感器和相关测量的应用。提供的四个示例中的最后一个示例简要描述了一些涉及混合物的差分脉冲极谱测量的预测研究，这些测量产生重叠的极谱图。所采用的化学计量学方法包括多元线性回归（MLR）、主成分回归（PCR）、偏最小二乘法（PLS）和卡尔曼滤波器（KF）方法。简要提及了常用的化学计量学软件包。
This is part of an invited presentation to the Australian Research Committee workshop on Electrochemically Based Microsensing Arrays. Its objective was to demonstrate the use of the more common chemometrics methods for data analysis, especially pattern recognition. The substance of this article was to address the question of why is it advantageous to use chemometrics for multivariate data analysis? And if one decides to venture into this field, how one can approach this challenge. We address the ‘why’ question through a number of examples, which highlight some advantages of pattern recognition data analysis, particularly for very similar or complicated multivariate measurements such as spectra or cyclic voltammograms. The chemometrics method considered is the exploratory principal component analysis(PCA) and the associated data pretreament, PC and loadings plots as well as biplots; data classification models are mentioned as possible useful extensions beyond the pattern recognition stage. The examples are not necessarily based on electrochemical problems but the applications to electrochemical biosensors and associated measurements can be readily appreciated. The last of the four examples provided, briefly describes some prediction studies involving differential pulse polarographic measurements of mixtures, which produce overlapping polarograms. The chemometrics methods employed involve multiple linear regression(MLR), principal component regression(PCR), partial least squares(PLS) and the Kalman filter(KF) approach. Commonly available chemometrics software packages are briefly mentioned.