Multivariate analysis of stream water chemical data: The use of principal components analysis for the end-member mixing problem

1992 ◽  
Vol 28 (1) ◽  
pp. 99-107 ◽  
Author(s):  
Nils Christophersen ◽  
Richard P. Hooper
Keyword(s):  
Multivariate Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Stream Water ◽  
Chemical Data ◽  
Components Analysis ◽  
Mixing Problem

scholarly journals Implications of measurement error structure on the visualization of multivariate chemical data: hazards and alternatives

2018 ◽  
Vol 96 (7) ◽  
pp. 738-748 ◽  
Author(s):  
Peter D. Wentzell ◽  
Chelsi C. Wicks ◽  
Jez W.B. Braga ◽  
Liz F. Soares ◽  
Tereza C.M. Pastore ◽  
...  
Keyword(s):  
Data Analysis ◽  
Measurement Error ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Measurement Errors ◽  
Alternative Methods ◽  
Chemical Data ◽  
Multidimensional Data ◽  
Chemical Information ◽  
Components Analysis

The analysis of multivariate chemical data is commonplace in fields ranging from metabolomics to forensic classification. Many of these studies rely on exploratory visualization methods that represent the multidimensional data in spaces of lower dimensionality, such as hierarchical cluster analysis (HCA) or principal components analysis (PCA). However, such methods rely on assumptions of independent measurement errors with uniform variance and can fail to reveal important information when these assumptions are violated, as they often are for chemical data. This work demonstrates how two alternative methods, maximum likelihood principal components analysis (MLPCA) and projection pursuit analysis (PPA), can reveal chemical information hidden from more traditional techniques. Experimental data to compare different methods consists of near-infrared (NIR) reflectance spectra from 108 samples of wood that are derived from four different species of Brazilian trees. The measurement error characteristics of the spectra are examined and it is shown that, by incorporating measurement error information into the data analysis (through MLPCA) or using alternative projection criteria (i.e., PPA), samples can be separated by species. These techniques are proposed as powerful tools for multivariate data analysis in chemistry.

Sensory Lexicon for Fresh Squeezed and Processed Orange Juices

2008 ◽  
Vol 14 (5_suppl) ◽  
pp. 131-141 ◽  
Author(s):  
P. Ruiz Pérez-Cacho ◽  
H. Galan-Soldevilla ◽  
K. Mahattanatawee ◽  
A. Elston ◽  
R.L. Rouseff
Keyword(s):  
Multivariate Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Processing Technology ◽  
Sensory Panel ◽  
Geometric Means ◽  
Thermally Processed ◽  
Components Analysis ◽  
Tactile Sensations

The aim of this study was to develop a flavor vocabulary (odor, aroma basic tastes and trigeminal/tactile sensations) to describe both fresh-squeezed and thermally processed (commercial) orange juices. Two independent panels located in different countries (Spain and USA) selected a common lexicon using multivariate analysis. Two sets of samples were selected and evaluated independently: the American sensory panel analyzed 40 orange juices varied in processing technology (pasteurized, refrigerated from concentrated, frozen concentrated and canned juices) and cultivars (Valencia, Temple, Navel, Hamlin, and Amber Sweet). The Spanish panel analyzed 26 samples that included thermally processed juices (pasteurized and refrigerated from concentrated) and unheated, hand squeezed juices (Valencia and Navel). A total of 34 common attributes were selected (15 for odor, 12 for aroma, 3 for basic tastes and 4 for trigeminal/tactile sensations). Data obtained were analyzed by geometric means, principal components analysis (PCA) and by Kruskal-Wallis test. Significant differences between the major categories of commercial juices were observed for all attributes in both countries and were also observed between fresh-squeezed and processed orange juices.

scholarly journals Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks

The Analyst ◽  
2016 ◽  
Vol 141 (1) ◽  
pp. 90-95 ◽  
Author(s):  
S. Van Nuffel ◽  
C. Parmenter ◽  
D. J. Scurr ◽  
N. A. Russell ◽  
M. Zelzer
Keyword(s):  
Multivariate Analysis ◽  
Principal Components Analysis ◽  
Method Validation ◽  
Cell Cultures ◽  
Principal Components ◽  
Neuronal Cell ◽  
Training Set ◽  
Neuronal Cell Cultures ◽  
Tof Sims ◽  
Components Analysis

Here, we demonstrate that by using a training set approach principal components analysis (PCA) can be performed on large 3D ToF-SIMS images of neuronal cell cultures.

Robust Statistics: A Survey and Some Prescriptions

1976 ◽  
Vol 1 (4) ◽  
pp. 285-312 ◽  
Author(s):  
Howard Wainer
Keyword(s):  
Factor Analysis ◽  
Multivariate Analysis ◽  
Standard Deviation ◽  
Principal Components Analysis ◽  
Analysis Of Variance ◽  
Principal Components ◽  
Robust Statistics ◽  
Robust Methods ◽  
The Mean ◽  
Components Analysis

It is noted that the usual estimators that are optimal under a Gaussian assumption are very vulnerable to the effects of outliers. A survey of robust alternatives to the mean, standard deviation, product moment correlation, t-test, and analysis of variance is offered. Robust methods of factor analysis, principal components analysis and multivariate analysis of variance are also surveyed, as are schemes for outlier detection.

A multivariate analysis of rumen papillary size in red deer (Cervus elaphus)

1996 ◽  
Vol 74 (11) ◽  
pp. 2089-2094 ◽  
Author(s):  
Roger G. Lentle ◽  
Ian M. Henderson ◽  
Kevin J. Stafford
Keyword(s):  
Multivariate Analysis ◽  
Principal Components Analysis ◽  
Quantitative Study ◽  
Principal Components ◽  
Cervus Elaphus ◽  
Red Deer ◽  
Anatomical Location ◽  
Interspecies Difference ◽  
Site Specific ◽  
Components Analysis

We measured the length and width of ruminal papillae from six sites in rumens of farmed and wild red (Cervus elaphus) and wild fallow (Cervus dama) deer. A multivariate (principal components) analysis was used to explore papillary response to habitat. Three axes of papillary variation were defined: overall size, shape, and site (anatomical location within the rumen). These axes responded differently according to age, sex, diet, and species. Overall papillary size increased with age in wild but not in farmed stock, was significantly affected by sex, but showed no interspecies difference. Papillary shape was not influenced by age, sex, or diet but was influenced by species. Site-specific development was influenced by diet and species but not by age or sex. These results corroborate previous descriptive work and offer a technique for quantitative study of the interaction between wild ruminants and their dietary environment.

Perceived Effectiveness of Danger Signs: A Multivariate Analysis

1992 ◽  
Vol 36 (13) ◽  
pp. 931-934 ◽  
Author(s):  
Donald J. Polzella ◽  
Michael D. Gravelle ◽  
Ken M. Klauer
Keyword(s):  
Multivariate Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Perceived Effectiveness ◽  
Danger Signs ◽  
Components Analysis ◽  
High Degree ◽  
Univariate Analyses

Fifty-eight subjects were shown randomly-ordered facsimiles of 80 OSHA-standard danger signs and rated the signs on 13 dimensions related to perceived effectiveness. The data were analyzed by means of principal components analysis and a series of multivariate and univariate analyses of variance. Signs containing a hazard label and instructions (e.g., GASOLINE - NO SMOKING) were rated as least likely to be recalled at a later time; however, they were rated as easiest to understand, most informative, and most likely to be complied with. Signs containing a hazard label only (e.g., POISON) were rated as least informative and most difficult to understand; however, they were rated as most likely to be recalled, as depicting a high degree of danger, and likely to be complied with. Signs containing instructions only (DO NOT ENTER) were rated as generally less effective.

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