Department of Chemistry, University of Tennessee, 552 Buehler Hall, Knoxville, TN 37996-1600, USA.
Anal Chim Acta. 2012 Oct 9;746:1-14. doi: 10.1016/j.aca.2012.08.002. Epub 2012 Aug 16.
Innovations in chemometrics are required for studies of chemical systems which are governed by nonlinear responses to chemical parameters and/or interdependencies (coupling) among these parameters. Conventional and linear multivariate models have limited use for quantitative and qualitative investigations of such systems because they are based on the assumption that the measured data are simple superpositions of several input parameters. 'Predictor Surfaces' were developed for studies of more chemically complex systems such as biological materials in order to ensure accurate quantitative analyses and proper chemical modeling for in-depth studies of such systems. Predictor Surfaces are based on approximating nonlinear multivariate model functions by multivariate Taylor expansions which inherently introduce the required coupled and higher-order predictor variables. As proof-of-principle for the Predictor Surfaces' capabilities, an application from environmental analytical chemistry was chosen. Microalgae cells are known to sensitively adapt to changes in environmental parameters such as pollution and/or nutrient availability and thus have potential as novel in situ sensors for environmental monitoring. These adaptations of the microalgae cells are reflected in their chemical signatures which were then acquired by means of FT-IR spectroscopy. In this study, the concentrations of three nutrients, namely inorganic carbon and two nitrogen containing ions, were chosen. Biological considerations predict that changes in nutrient availability produce a nonlinear response in the cells' biomass composition; it is also known that microalgae need certain nutrient mixes to thrive. The nonlinear Predictor Surfaces were demonstrated to be more accurate in predicting the values of these nutrients' concentrations than principal component regression. For qualitative chemical studies of biological systems, the Predictor Surfaces themselves are a novel tool as they visualize nonlinearities and more importantly the coupling among predictor variables. Thus, they can serve as a novel tool for studies in bioanalytical chemistry, biology, and ecology.
需要创新的化学计量学方法来研究那些化学系统，这些系统的化学参数具有非线性响应，并且这些参数之间存在相互依赖（耦合）。传统的和线性的多元模型对于这些系统的定量和定性研究的用途有限，因为它们基于这样的假设，即所测量的数据是几个输入参数的简单叠加。为了确保对这些系统进行准确的定量分析和适当的化学建模，以进行深入研究，开发了“预测曲面”来研究更具化学复杂性的系统，如生物材料。预测曲面是通过多元泰勒展开来逼近非线性多元模型函数的，这种展开方法本质上引入了所需的耦合和高阶预测变量。为了证明预测曲面的能力，选择了环境分析化学中的一个应用案例。众所周知，微藻细胞对环境参数（如污染和/或营养物质的可用性）的变化非常敏感，因此它们具有作为新型原位传感器进行环境监测的潜力。这些微藻细胞的适应性反映在它们的化学特征中，然后通过傅里叶变换红外光谱法获得这些特征。在这项研究中，选择了三种营养物质的浓度，即无机碳和两种含氮离子。生物学考虑表明，营养物质可用性的变化会导致细胞生物量组成的非线性响应；同时，也知道微藻需要一定的营养混合物才能茁壮成长。与主成分回归相比，非线性预测曲面被证明在预测这些营养物质浓度值方面更准确。对于生物系统的定性化学研究，预测曲面本身就是一种新颖的工具，因为它们可以可视化非线性以及更重要的是预测变量之间的耦合。因此，它们可以作为生物分析化学、生物学和生态学研究的新工具。
