2014
DOI: 10.1007/s12011-014-0013-9
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Chemometric Methods for Studying the Relationships Between Trace Elements in Laryngeal Cancer and Healthy Tissues

Abstract: A quick and reliable method for the evaluation and classification of two types of tissues is presented. Several chemometric methods were applied to evaluate multivariate data of the tissue samples with respect to the content of trace elements. The content of Pb, Al, Zn, Cd, Cu, Ni and Co was determined in samples of healthy and cancerous tissue obtained from 26 patients. Determination was done at milligram/kilogram level with inductively coupled plasma optical emission spectrometry (ICP-OES) and atomic absorpt… Show more

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Cited by 13 publications
(8 citation statements)
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“…As can be seen from the existing literature, multivariate methods, also known as chemometrics, are frequently used in the analysis of elemental data especially in the field of life sciences [24,[33][34][35][36][37][38][39][40][41]. For example, hierarchical cluster analysis was successfully applied for the prediction of cancerrelated Cu-binding proteins, which can serve as a source for mechanistic-molecular studies of Cu-dependent processes in cancer [42].…”
Section: Discussionmentioning
confidence: 99%
“…As can be seen from the existing literature, multivariate methods, also known as chemometrics, are frequently used in the analysis of elemental data especially in the field of life sciences [24,[33][34][35][36][37][38][39][40][41]. For example, hierarchical cluster analysis was successfully applied for the prediction of cancerrelated Cu-binding proteins, which can serve as a source for mechanistic-molecular studies of Cu-dependent processes in cancer [42].…”
Section: Discussionmentioning
confidence: 99%
“…Chemometrics describes the extraction of information from large datasets, using particular data‐driven methods . These methods are well suited for large and complex datasets and can include machine learning . In machine learning, computer algorithms are used to automatically improve their performance through experience .…”
Section: Methodsmentioning
confidence: 99%
“…LDA is a multivariate and supervised statistical tool that separates groups/classes by finding linear discriminants that maximize the ratio of between-class variance and minimizes the ratio of variance within class (Dobrowolski et al 2014). Unlike PCA, LDA data is pre-categorized into classes were it is trained to separate species according to class (Dobrowolski et al 2014). Figure 2.13 represents LDA generated group clusters.…”
Section: Linear Discriminant Analysis (Lda)mentioning
confidence: 99%
“…Figure2.12: Example of an LDA plot which shows distinguishable group clusters of H (blue) and S (red)(Dobrowolski et al 2014) …”
mentioning
confidence: 99%