2010
DOI: 10.1016/j.aca.2010.06.017
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Multivariate analysis of nutritional information of foodstuff of plant origin for the selection of representative matrices for the analysis of pesticide residues

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Cited by 12 publications
(4 citation statements)
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References 13 publications
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“…The impact of these chemicals on the nutritional composition of cereal grains is of great concern. Studies have shown the prevalence of these chemical residues in stored products, affecting the health and nutritional value derived from their consumption [ [23] , [24] , [25] ]. The quality of these chemically stored grains is mainly influenced by the duration and method of storage [ 26 ] .…”
Section: Effect Of Storage Chemicals On the Qualities Of Agro-producementioning
confidence: 99%
“…The impact of these chemicals on the nutritional composition of cereal grains is of great concern. Studies have shown the prevalence of these chemical residues in stored products, affecting the health and nutritional value derived from their consumption [ [23] , [24] , [25] ]. The quality of these chemically stored grains is mainly influenced by the duration and method of storage [ 26 ] .…”
Section: Effect Of Storage Chemicals On the Qualities Of Agro-producementioning
confidence: 99%
“…Principal Component Analysis (PCA) is a multivariate tool used to interpret and study the pattern of multivariate characteristics (original variables) of several objects using the graphical representation of the projection of this information in two or three axes (latent variable axes) selected to contain maximum information given by the original variables (Da Silva & Camões, 2010). PCA has been widely used to reduce a set of original variables and to extract a small number of latent factors (principal components, PC) for analyzing relationships among the observed variables (Martin et al, 2013).…”
Section: Statisticsmentioning
confidence: 99%
“…Important multivariate statistic methods in food quality and safety analysis include principal component analysis (PCA) and hierarchical cluster analysis (CA). The latter method uses a dendrogram to present distances between samples [ 33 , 34 ].…”
Section: Introductionmentioning
confidence: 99%