A practical approach to the direct-derivation method for QPA: use of observed powder patterns of individual components without background subtraction in whole-powder-pattern fitting

2019 ◽  
Vol 52 (3) ◽  
pp. 520-531 ◽  
Author(s):  
Hideo Toraya

The direct-derivation (DD) method for quantitative phase analysis (QPA) can be used to derive weight fractions of individual phases in a mixture from the sums of observed intensities along with the chemical composition data [Toraya (2016). J. Appl. Cryst. 49, 1508–1516]. The whole-powder-pattern fitting (WPPF) technique can be used as one of the tools for deriving the observed intensities of individual phases. In WPPF, the observed powder pattern of a single-phase sample after background (BG) subtraction can be used as the fitting function in combination with the fitting functions widely used in Pawley and Rietveld refinements. The direct fitting of the observed pattern is a very useful technique when the target component is a low-crystallinity or amorphous material [Toraya (2018). J. Appl. Cryst. 51, 446–455]. Technical problems in utilizing the BG-subtracted pattern are the uncertainty associated with the determination of BG height and the parameter interaction between the BG function (BGF) and the BG-subtracted pattern in the least-squares fit. In this study, a practical approach in which single-phase observed patterns are used for the direct fitting without subtracting their BG intensities is proposed. In QPA, the contribution of BG intensities can be neutralized by converting the sum of BG-included intensities into the sum of BG-subtracted intensities by multiplying by a conversion factor. When the magnitudes of the conversion factors are almost identical for all components, they can be canceled out under the normalization condition in deriving weight fractions, and they are not required in QPA. The magnitude of the conversion factor for each component can be determined by one of two experimental techniques: using a single-phase powder of the target component or a mixture containing the target component in a known weight ratio. The theoretical basis of the present procedure is given, and the procedure is experimentally verified. In this procedure, the interaction between the BGF and the BG-included observed pattern is negligibly small. Least-squares fitting with a few adjustable parameters is very fast and stable. Accurate QPA could be conducted, as indicated by the average deviation of 0.05% from weighed values in QPA of α-Al2O3 + γ-Al2O3 mixtures with five different weight ratios and 0.4% in QPA of an α-SiO2 + SiO2 glass mixture

1983 ◽  
Vol 55 (1) ◽  
pp. 201-204 ◽  
Author(s):  
A. D. LeBlanc ◽  
H. J. Evans ◽  
P. C. Johnson ◽  
S. Jhingran

The purpose of this study was to evaluate the effect of deconditioning on the total body calcium in rats. Two separate experiments were performed using female Sprague-Dawley rats, 187-266 days of age. Total body calcium was measured in experimental and control rats during and following several weeks of voluntary exercise. The slope from the least-squares fit of total body calcium with time was used to obtain an average calcium balance for each animal during each study period. In both groups the exercised rats had a significantly decreased calcium balance after cessation of exercise, whereas no significant change was seen in nonexercised controls. In both groups, the exercised animals gained calcium at a significantly greater rate than controls. Our findings indicate that while exercised rats may gain calcium at a faster rate compared with nonexercising controls, the rate of gain following cessation of exercise is less than the controls.


2016 ◽  
Vol 57 (10) ◽  
pp. 2136-2140 ◽  
Author(s):  
Yonghong Zhou ◽  
Qiang Zhu ◽  
David A. Salstein ◽  
Xueqing Xu ◽  
Si Shi ◽  
...  

2018 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Hasih Pratiwi ◽  
Yuliana Susanti ◽  
Sri Sulistijowati Handajani

Linear least-squares estimates can behave badly when the error distribution is not normal, particularly when the errors are heavy-tailed. One remedy is to remove influential observations from the least-squares fit. Another approach, robust regression, is to use a fitting criterion that is not as vulnerable as least squares to unusual data. The most common general method of robust regression is M-estimation. This class of estimators can be regarded as a generalization of maximum-likelihood estimation. In this paper we discuss robust regression model for corn production by using two popular estimators; i.e. Huber estimator and Tukey bisquare estimator.<br />Keywords : robust regression, M-estimation, Huber estimator, Tukey bisquare estimator


1970 ◽  
Vol 14 (04) ◽  
pp. 277-295
Author(s):  
Carl F. Kottler

A systematic investigation was made of the parameters chosen to define the Pierson-Moskowitz wind sea spectral model. The model was generalized and the form was extended to give a better fit of the data. Using the same sets of data as those selected by Pierson and Moskowitz for building their model, a least-squares fit of each set of the co-cumulative data gave a corresponding optimum set of parameters. These unique optimum sets of parameters yielded an eightfold decrease in the standard deviation. From this family of parameter sets, a co-cumulative spectral model was. developed to fix some of the parameters and relate the others to surface wind velocity. This modification and extension show that at least a twofold improvement in accuracy over the associated Pierson-Moskowitz co-cumulative model can be achieved.


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