Journal of research of the National Bureau of Standards (1977)最新文献

O'Dea et al. (1983, J. Phys. Chem. 97, 3911-3918) proposed an empirical procedure for obtaining estimates and confidence intervals for kinetic parameters in a model for pulse voltammetric data. Their goal was to find a procedure that would run in real time, not necessarily one that would have well-defined statistical properties. In this paper we investigate some of the statistical properties of their procedure. We show that their estimation method is equivalent to maximum likelihood estimation, and their confidence intervals, while related to likelihood ratio confidence regions, have a coverage probability that is not fixed and that is potentially quite large. We suggest modifications of their procedure that lead to more traditional confidence intervals. We examine the effect on their procedure of the presence of nuisance paramters. Finally we discuss the possibility of serially correlated errors.

Different chemometric methods to improve calibrations are described. A Kalman filter is applied for processing and predicting slowly varying parameters of a linear calibration graph. The results are used for the evaluation of unknown samples, and for deciding whether to calibrate again or to analyze the next unknown sample. Another approach of the calibration problem, particularly in chromatography, is the use of correlation techniques. The noise reduction property of correlation chromatography is used to extend the calibration graph to very low concentrations. Furthermore, an experimental technique to determine a calibration curve and the unknown sample simultaneously under exactly the same conditions is described.

Most research and development projects require the optimization of a system response as a function of several experimental factors. Familiar chemical examples are the maximization of product yield as a function of reaction time and temperature; the maximization of analytical sensitivity of a wet chemical method as a function of reactant concentration, pH, and detector wavelength; and the minimization of undesirable impurities in a pharamaceutical preparation as a function of numerous process variables. The "classical" approach to research and development involves answering the following three questions in sequence: What are the important factors? (Screening)In what way do these important factors affect the system? (Modeling)What are the optimum levels of the important factors? As R. M. Driver has pointed out, when the goal of research and development is optimization, an alternative strategy is often more efficient: What is the optimum combination of all factor levels? (Optimization)In what way do these factors affect the system? (Modeling in the region of the optimum)What are the important factors? The key to this alternative approach is the use of an efficient experimental design strategy that can optimize a relatively large number of factors in a small number of experiments. For many chemical systems involving continuously variable factors, the sequential simplex method has been found to be a highly efficient experimental design strategy that gives improved response after only a few experiments. It does not involve detailed mathematical or statistical analysis of experimental results. Sequential simplex optimization is an alternative evolutionary operation (EVOP) technique that is not based on traditional factorial designs. It can be used to optimize several factors (not just one or two) in a single study. Some research and development projects exhibit multiple optima. A familiar analytical chemical example is column chromatography which often possesses several sets of locally optimal conditions. EVOP strategies such as the sequential simplex method will operate well in the region of one of these local optima, but they are generally incapable of finding the global or overall optimum. In such situations, the "classical" approach can be used to estimate the general region of the global optimum, after which EVOP methods can be used to "fine tune" the system. For example, in chromatography the Laub and Purnell "window diagram" technique can often be applied to discover the general region of the global optimum, after which the sequential simplex method can be used to "fine tune" the system, if necessary. The theory of these techniques and applications to real situations will be discussed.

Interlaboratory Comparisons using common (reference) materials of known composition are an established means for assessing overall measurement precision and accuracy. Intercomparisons based on common data sets are equally important and informative, when one is dealing with complex chemical patterns or spectra requiring significant numerical modeling and manipulation for component identification and quantification. Two case studies of "Chemometric Intercomparison" using Simulation Test Data (STD) are presented, the one comprising STD vectors as applied to nuclear spectrometry, and the other, STD data matrices as applied to aerosol source apportionment. Generic information gained from these two exercises includes: a) the requisites for a successful STD intercomparison (including the nature and preparation of the simulation test patterns); b) surprising degrees of bias and imprecision associated with the data evaluation process, per se; c) the need for increased attention to implicit assumptions and adequate statements of uncertainty; and d) the importance of STD beyond the Intercomparison-i.e., their value as a chemometric research tool. Open research questions developed from the STD exercises are highlighted, especially the opportunity to explore "Scientific Intuition" which is essential for the solution of the underdetermined, multicollinear inverse problems that characterize modern Analytical Chemistry.