**7. References**


44 Principal Component Analysis

relevance of PCA to elucidate specific information from a data collection in several fields. Among these issues, the following aspects must be underlined: a) the influence of the data pre-treatment on the scores and loadings values; b) the *a-priori* knowledge of the data source to select the appropriate data pre-processing; c) the strategies and criteria used for the scores and loadings plots interpretation and, d) criteria used for outliers detection, and their

The amalgamation of the different sections included in this chapter can be used as a starting point for those researchers who are not specialists in the field, but that are interested in

This work was supported by CONACyT, Mexico [Projects No. 119491 (2009) and No. 153066 (2010)] and PROMEP, Mexico (Project UAZ-PTC-092), Agencia Nacional de Promoción Científica y Tecnológica, Argentina (Projects PICT/2008/145 and PICT/2010/2145), CYTED Program (Ciencia y Tecnología para el Desarrollo) Network P108RT0362 and CONACyT-CONICET (México, Argentina) (bilateral project res. Nº 962/07-05-2009), PIFI, México (project P/PIFI 2010-32MSU0017H-06). AGZ, PM and EET are members of the research career CONICET (National Research Council, Argentina). EG is doctoral fellow from

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**3** 

**Principal Component Analysis: A Powerful** 

> Maria Monfreda *Italian Customs Agency*

> > *Italy*

**Interpretative Tool at the** 

**Service of Analytical Methodology** 

*Central Directorate for Chemical Analysis and Development of Laboratories, Rome,* 

PCA is one of the most widely employed and useful tools in the field of exploratory analysis. It offers a general overview of the subject in question, showing the relationship that

An important application of PCA consists of the characterization and subsequent differentiation of products in relation to their origin (known as traceability). PCA is often applied in order to characterize some products obtained via a manufacturing process and the transformation of some raw materials. In this case, there are two kinds of elements linkable to the differentiation of products in relation to their origin: the variability associated to the raw material and the differences in various production techniques used around the world. In this study, two examples of PCA application to some products obtained via a manufacturing process are presented. These products, belonging to completely different fields (foodstuffs and petroleum based fuel) show one element in common: their traceability

The strength of PCA is that it provides the opportunity to visualize data in reference to objects described by more than 3 variables. Indeed, PCA allows us to study and understand such systems, helping the human eye to see in two or three dimension systems that otherwise would necessarily have to be seen in more than three dimensions in order to be studied. PCA allows data to maintain their original structure, making only an orthogonal rotation of variables, which helps to simplify the visualization of all the information already contained in the data. Consequently, PCA can be considered the best technique to begin to approach any qualitative multivariate problem, be it unsupervised or supervised. Needless to say, supervised problems - following a primary study by PCA - require the application of either a classification or a class modeling method. In this study, three cases regarding supervised problems which involved the preliminary application of PCA are put forward. Results from PCA have been compared to those obtained from classification or class

exists among objects as well as between objects and variables.

is correlated to the raw material and the production process.

**1. Introduction** 

modeling tools.

molecules as an important factor in effective cyropreservation. *Plant Science* Vol.160, pp.489-497.

