摘要:

AbstractRank annihilation factor analysis (RAFA) is a method for multicomponent calibration using two data matrices simultaneously, one for the unknown and one for the calibration sample. In its most general form, the generalized rank annihilation method (GRAM), an eigenvalue problem has to be solved. In this second paper expressions are derived for predicting the bias and variance in the eigenvalues of GRAM. These expressions are built on the analogies between a reformulation of the eigenvalue problem and the prediction equations of univariate and multivariate calibration. The error analysis will also be performed for Lorber's formulation of RAFA. It will be demonstrated that, depending on the size of the eigenvalue, large differences in performance must be expected. A bias correction technique is proposed that effectively eliminates the bias if the error in the bias estimate is not too large. The derived expressions are evaluated by Monte Carlo simulations. It is shown that the predictions are satisfactory up to the limit of detection. The results are not sensitive to an incorrect choice of the dimension of the factor space.

ISSN:0886-9383    

DOI:10.1002/cem.1180080303

出版商:John Wiley&Sons, Ltd.    

年代:1994

数据来源: WILEY

摘要:

AbstractThis paper presents a method for identifying significant effects in fractional factorial designs. The method is based on comparing functions of the estimated effects with a scale invariant reference distribution. The method is illustrated and compared with alternative techniques on a dataset from processed cheese production. The method corresponded well to other methods (probability plotting and Bayes plot) that do not make use of error estimates. These methods were better thanF‐tests with few (two or four) degrees of freedom for erro

ISSN:0886-9383    

DOI:10.1002/cem.1180080304

出版商:John Wiley&Sons, Ltd.    

年代:1994

数据来源: WILEY

摘要:

AbstractA non‐linear least squares iterative refinement has been implemented which shows high performance on a multiple‐peak spectrum including baseline or background. Constraints as well as links within a range are introduced to drive the mathematical optimization: each peak parameter (i.e. height, position, Gaussian/Lorentzian mixing ratio and HWHM on both left and right sides) has assigned to it an allowed range of variation and can be strained to be correlated with other parameters belonging either to the same peak (symmetrical peaks) or to other peaks (doublets, triplets, etc.). Peak shapes typical of XP spectra are used and applications in the field of XPS are discussed. Through emulated curves with Poisson distributed noise, the accuracy and precision of back‐calculated (refined) parameters have been estimated. Moreover, a confidence level calculated fromX2and degrees of freedom has been suggested to check the overall fitting of experimental curves where the signal‐to‐noise ratio isa prioriunknown. An application to real C ls XP spectra is described as an example and a list of suggestions is given to match operator requirements. Finally, features of NLLSRC are discussed with respect to other a

ISSN:0886-9383    

DOI:10.1002/cem.1180080305

出版商:John Wiley&Sons, Ltd.    

年代:1994

数据来源: WILEY

ISSN:0886-9383    

DOI:10.1002/cem.1180080306

出版商:John Wiley&Sons, Ltd.    

年代:1994

数据来源: WILEY

ISSN:0886-9383    

DOI:10.1002/cem.1180080302

出版商:John Wiley&Sons, Ltd.    

年代:1994

数据来源: WILEY