Comparison of the single channel and multichannel (multivariate) concepts of selectivity in analytical chemistry

Abstract

Cite this article as: Zsanett Dorkó, Tatjana Verbić and George Horvai, Comparison of the single channel and multichannel(multivariate) concepts of selectivity in analytical chemistry, Talanta, http://dx.doi.org/10.1016/j.talanta. 2015.02.030 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. www.elsevier.com This accepted author manuscript is copyrighted and published by Elsevier. AbstractDifferent measures of selectivity are in use for single channel and multichannel linear analytical measurements, respectively. It is important to understand that these two measures express related but still distinctly different features of the respective measurements. These relationships are clarified by introducing new arguments. The most widely used selectivity measure of multichannel linear methods (which is based on the net analyte signal, NAS, concept) expresses the sensitivity to random errors of a determination where all bias from interferents is computationally eliminated using pure component spectra. The conventional selectivity measure of single channel linear measurements, on the other hand, helps to estimate the bias caused by an interferent in a biased measurement. In single channel methods expert knowledge about the samples is used to limit the possible range of interferent concentrations. The same kind of expert knowledge allows improved (lower mean squared error, MSE) analyte determinations also in "classical" multichannel measurements if those are intractable due to perfect collinearity or to high noise inflation. To achieve this goal bias variance tradeoff is employed, hence there remains some bias in the results and therefore the concept of single channel selectivity can be extended in a natural way to multichannel measurements. This extended definition and the resulting selectivity measure can also be applied to the so-called inverse multivariate methods like partial least squares regression (PLSR), principal component regression (PCR) and ridge regression (RR).

Isotherm charts for material selection and method development with molecularly imprinted polymers and other sorbents

“…It can happen for instance, that the IF is high, but the difference between the MIP and the NIP is small in the practical application [41]. Note that the difficulty of adequately defining selectivity is generally observed in analytical chemistry [42,43].…”

Discussion on the Definition and Improving Strategies of Selectivity in Analytical Chemistry

“…1883年，开尔文勋爵说："When you can measure what you are speaking about and express it in numbers, you know something about it; but when you cannot measure it your knowledge is of a meager and unsatisfactory kind" [17] 。分析方法的灵敏度、检出限、线性范围、重现性和准确度均有明确的数学表 达式。IUPAC给出了分析方法选择性的定义，意为干扰越少，选择性越高。IUPAC除给出了离子选 择电极的电位选择性系数的表达式 [6,8] 外，没有其他分析方法选择性的量化表达式。笔者认为这主要 是因为人们希望得到一个与干扰物浓度和分析物浓度无关的选择性的表达式，但实际上很难做到。 关于选择性的估计或评价(量化表示式)已经有许多研究 [19][20][21][22][23] ，在此重点讨论一些简单容易理解的选 择性的量化表达式。 Dorko等人从测量误差和响应面两个方面 [21] 以及从单组分测定和多组分同时测定两个方面 [18] 对 于选择性的表达式进行了研究。在分析化学中，红外光谱和拉曼光谱的测量涉及测量误差 [10] ；在多 组分同时测定中，由于选择性涉及仪器测量噪声相关的误差膨胀因子(error inflation factor)，此方面 较为复杂，在此不进行讨论，我们重点讨论基于响应面的单组分测定的选择性表达式。 我们先理解定量分析的原理。从数学关系上讲，仪器分析方法是基于可测量的物理量(信号y i ，如 吸光度、光强度，电位、电流等)与待测浓度c i 间的函数关系进行定量分析。这种关系在数学上是一 种是"映射(mapping由系统的函数来实现)"。"解析函数"c = f(y)是"校准函数"y = f'(c)的反函数。 校准函数可以通过实验直接获得；样品获得的信号可通过校准曲线将待测物质的信号转变为待测物 的浓度 [5] 。 在此，我们讨论一种最简单的情况，某一样品含有单一组分I和单一干扰物J。假设某分析方法测 定分析物I的灵敏度为S I ，则分析信号(测得的信号中扣除空白信号)…”

Selectivity‐relaxed classical and inverse least squares calibration and selectivity measures with a unified selectivity coefficient

“…Sensitivity and selectivity are 2 figures of merit balanced in forming calibration models . Approaches to measure and quantify sensitivity and selectivity have been provided (previous works and references therein). Regarding selectivity, Ridder and Brown state “a method is fully selective against a particular interferent if the result it renders is independent of the concentration of the interferent.…”

“…To provide accurate predictions from nonselective measurements, each calibration approach sets constraints on the model relative to the non‐analyte components (interferents). The understanding of such constraints has led to the concept of net analyte signal (NAS) and selectivity measures …”