| 11. |
- Stehlik, M., et al.
(författare)
-
On robust testing for normality in chemometrics
- 2014
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 130, s. 98-108
-
Tidskriftsartikel (refereegranskat)abstract
- The assumption that the data has been generated by a normal distribution underlies many statistical methods used in chemometrics. While such methods can be quite robust to small deviations from normality, for instance caused by a small number of outliers, common tests for normality are not and will often needlessly reject normality. It is therefore better to use tests from the little-known class of robust tests for normality. We illustrate the need for robust normality testing in chemometrics with several examples, review a class of robustified omnibus Jarque-Bera tests and propose a new class of robustified directed Lin-Mudholkar tests. The robustness and power of several tests for normality are compared in a large simulation study. The new tests are robust and have high power in comparison with both classic tests and other robust tests. A new graphical method for assessing normality is also introduced.
|
|
| 12. |
- Öberg, Tomas, et al.
(författare)
-
Extension of a prediction model to estimate vapor pressures of perfluorinated compounds (PFCs)
- 2011
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 107:1, s. 59-64
-
Tidskriftsartikel (refereegranskat)abstract
- Perfluorinated compounds (PFCs) are persistent and have been found globally as environmental contaminants. Release into the environment can occur from manufacturing, industrial and consumer uses. The vapor pressure is an important physical property influencing both the release and the environmental partitioning, but few reliable experimental determinations are available. Here we update a previous PLS regression model to cover also this compound class, using only a few calibration compounds. The recalibration is accomplished by applying a leverage-based weighting scheme that is generally applicable in updating structure–property relationships. The predictive performance is validated with an external validation set and is considerably better than for other standard estimation software, both with regard to accuracy and precision. The model can be given a chemical interpretation and the prediction error for the liquid vapor pressure is within 0.2 log units of Pa. Finally, the model is applied and vapor pressure estimates are reported for more than 200 PFCs where no reliable experimental data are available.
|
|
| 13. |
- Brink, Mattias, et al.
(författare)
-
On-line predictions of the aspen fibre and birch bark content in unbleached hardwood pulp, using NIR spectroscopy and multivariate data analysis
- 2010
-
Ingår i: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - : Elsevier Science B.V., Amsterdam.. - 0169-7439. ; 103:1, s. 53-58
-
Tidskriftsartikel (refereegranskat)abstract
- An on-line fibre-based near-infrared (NIR) spectrometric analyser was adapted for on-site process analysis at an integrated paperboard mill. The analyser uses multivariate techniques for the quantitative predication of the aspen fibre (aspen) and the birch bark contents of sheets of unbleached hardwood pulp. The NIR analyser is a prototype constructed from standard NIR components. The spectroscopic data was processed by using principal component analysis (PCA) and partial least square (PLS) regression. Three sample sets were collected from three experimental designs, each composed of known pulp contents of birch, aspen and birch bark. Sets I and 2 were used for model calibration and set 3 was used to validate the models. The PLS model that produced the best predictions gave an error of prediction (RMSEP) of 13% for aspen and less than 2% for birch bark. Eight components resulted in an (RX)-X-2 of 99.3%, (RY)-Y-2 of 99.6%. and Q(2) of 95.3%. For additional validation of aspen, three unbleached hardwood samples from the mills production were calculated to lie between -7% and +6%, regarding to the PIS model. When vessel cells were counted under a light microscope a value for the aspen content of 4.7% was obtained. The predictive models evaluated were suitable for quality assessments rather than quantitative determination.
|
|
| 14. |
|
|
