Chemometrics exploratory analysis

Among the different chemometric methods, exploratory data analysis and pattern recognition are frequently used in the area of food analysis. Exploratory data analysis is focused on the possible relationships between samples and variables, while pattern recognition studies the behavior between samples and variables [95]. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) are the methods most commonly used for exploratory analysis and pattern recognition, respectively. The importance of these statistical tools has been demonstrated by the wide number of works in the field of food science where they have been applied. The majority of the applications are related to the characterization and authentication of olive oil, animal fats, marine and vegetable oils [95], wine [97], fruit juice [98], honey [99], cheese [100,101], and so on, although other important use of statistical tools is the detection of adulterants or frauds [96,102]. 

Hurtado-Fernandez, E., Contreras-Gutierrez, P. K., Cuadros-Rodrfguez, L., Catrasco-Pancorbo, A., and Fernandez-Gutierrez, A. 2013. Merging a sensitive eapiUary eleetro-phoresis-ultraviolet detection method with chemometric exploratory data analysis for the determination of phenolic acids and subsequent characterization of avocado fruit. Food Chem. 141 3492-3503. 

PLSR is an extension of the multiple linear regression model. It is probably the least restrictive of the various multivariate extensions of the multiple linear regression model. This flexibility allows it to be used in situations where the use of traditional multivariate methods is severely limited, such as the case that when there are fewer observations than predictor variables. Furthermore, PLSR can be used as an exploratory analysis tool to select suitable predictor variables and to identify outliers before classical linear regression. Especially in chemometrics, PLSR has become a standard tool for modeling linear relationships between multivariate measurements. 

Exploratory analysis of L vov platform surfaces for electrothermal atomic absorption spectrometry by using three-way chemometric tools. 

While principal components models are used mostly in an unsupervised or exploratory mode, models based on canonical variates are often applied in a supervisory way for the prediction of biological activities from chemical, physicochemical or other biological parameters. In this section we discuss briefly the methods of linear discriminant analysis (LDA) and canonical correlation analysis (CCA). Although there has been an early awareness of these methods in QSAR [7,50], they have not been widely accepted. More recently they have been superseded by the successful introduction of partial least squares analysis (PLS) in QSAR. Nevertheless, the early pattern recognition techniques have prepared the minds for the introduction of modem chemometric approaches. 

Chemometrics tools can be used for a wide variety of tasks, including experimental design, exploratory data analysis, and the development of predictive models. In the context of analytical chemistry, however, chemometrics has been shown to be most effective for two general functions ... 

Regarding point 2, the information extraction function of chemometrics is a very valnable one that is often overlooked, especially in the industrial world. It will be mentioned later in this chapter that this function can be nsed concurrently with the instrument specialization function, rather than relying npon additional exploratory data analysis. 

Autoscaling can also be used when all of the variables have the same units and come from the same instrument. However, it can be detrimental if the total variance information is relevant to the problem being solved. For example, if one wants to do an exploratory chemometric analysis of a series of FTIR (Fourier transform infrared) spectra in order to determine the relative sensitivities of different wavenumbers (X-variables) to a property of interest, then it would be wise to avoid autoscaling and retain the total variance information because this information is relevant for assessing the sensitivities of different X-variables. 

Vong,R.J., Frank,I.E., CharIson,R.J., and Kowalski, B.R., Exploratory Data Analysis of Rainwater Composition, in Environmental Applications of Chemometrics, ACS Symposium Series 292, (ed. J.J. 

A basic chemometric technique for - exploratory data analysis, modelling the p variables in the data matrix X(n x p), where n is the number of objects, as linear combinations of the common factors T (n x M), called principal components... 

The classical PCA is non-robust and sensitive to deviations of error distribution from the normal assumption, the PC directions being influenced by the presence of outlier(s). In PP PCA, the PC directions are determinated by the the inherent structure of the main body of the data. Using some robust projective index, the influence of the outliers is thus substantially reduced. The distorted appearance or misrepresentation of the projected data structure in the PC subspace caused by the presence of outlier(s) could be eliminated in PP PCA. This characteristic feature of PP PCA is essential for obtaining reliable results for exploratory data analysis, calibration and resolution in analytical chemometrics where PCA is used for dimension reduction. 

Bro R, Exploratory study of sugar production using fluorescence spectroscopy and multi-way analysis, Chemometrics and Intelligent Laboratory Systems, 1999, 46, 133-147. 

Exploratory Data Analysis (EDA) has been employed for decades in many research fields, including social sciences, psychology, education, medicine, chemometrics and related fields... 

EDA based on projection models has been successfully applied in the area of chemometrics and industrial process analysis. In this chapter, several standard tools for EDA with projection models, namely score plots, loading plots and biplots, are revised and their limitations are elucidated. Two recently proposed tools are introduced to overcome these limitations. The first of them, named Missing-data methods for Exploratory Data Analysis or MEDA for short... 

In this chapter, new tools for exploratory data analysis are presented and combined with already well known techniques in the chemometrics field, such as projection models, score and loading plots. The shortcomings and potential pitfalls in the application of common tools are elucidated and illustrated with examples. Then, the new techniques are introduced to overcome these problems. 

Camacho J.. Missing-data theory in the context of exploratory data analysis Chemometrics and Intelligent Laboratory Systems. 2010 103 8-18. 

Camacho J. Observation-based missing data methods for exploratory data analysis to unveil the connection between observations and variables in latent subspace models Journal of Chemometrics 25 (2011) 592 - 600. 

A FTIR spectrum is complex, containing many variables per sample and making visual analysis very difficult. Hence, to extract extra useful information, i.e., latent variables, from the whole spectra chemometric analysis was performed considering the whole FTIR data set using principal components analysis (PCA) for an exploratory overview of data. This method could reveal similarity/dissimilarity patterns among propolis samples, simplifying... 

After applying the appropriate pre-processing, different chemometric techniques can be applied according to the aim of the study. Pattern recognition is one of the chemometric methods most used in analytical chemistry and this is true for separations data. Pattern recognition can be generally divided into two classes exploratory data analysis and unsupervised and supervised pattern recognition (Otto, 2007 Brereton, 2007). 

Exploratory data analysis aims to extract important information, detect outliers and identify relationships between samples and its use is recommended prior to the application of other chemometric techniques. Examples of the use of exploratory data analysis tools applied to separations data include principal component analyisis (PCA) (de la Mata-Espinosa et al., 2011a Ruiz-Samblas et al., 2011) and factor analysis (Stanimirova et al., 2011). 

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