Use of multivariate analysis techniques for the characterization of analytical results for the determination of the mineral composition of kale

Fadigas, Joelma C.; Santos, André Luíz dos; Jesus, Raildo Mota de; Lima, Daniel C.; Fragoso, Wallace D.; David, Jorge; Ferreira, Sérgio Luís Costa

doi:10.1016/j.microc.2010.06.006

Cited by 32 publications

(28 citation statements)

References 23 publications

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“…Basic statistical parameters, such as minimum, maximum, mean, median, standard error (SE), and skewness, were computed along with correlation study. Multivariate techniques have been used for evaluation and characterization of analytical data (Fadigas et al 2010). Principal component analysis (PCA) and cluster analysis (CA) are among the most popular methods.…”

Section: Discussionmentioning

confidence: 99%

Occurrence, risk assessment, and source apportionment of heavy metals in surface sediments from Khanpur Lake, Pakistan

Iqbal

Shah

2014

J Anal Sci Technol

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Background: The present study was carried out to assess the seasonal variations, source apportionment, and risk assessment of heavy metals (Cd, Cr, Cu, Fe, Mn, Pb, and Zn) in the surface sediments from the Khanpur Lake, Pakistan. Methods: Composite samples are collected and processed to measure the concentrations of heavy metals in Ca(NO 3 ) 2 extract and acid extract of the sediments using flame atomic absorption spectrophotometry.

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Section: Discussionmentioning

confidence: 99%

Occurrence, risk assessment, and source apportionment of heavy metals in surface sediments from Khanpur Lake, Pakistan

Iqbal

Shah

2014

J Anal Sci Technol

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“…STATISTICA software was used for statistical analyses of the metal data (StatSoft, 1999). Basic statistical parameters such as minimum, maximum, mean, median, standard deviations (SD) and skewness (skew) were computed along with multivariate analysis (da Silva et al, 2008;Fadigas et al, 2010). Principal component analysis (PCA) and cluster analysis (CA) are among the most popular multivariate techniques (Cecere et al, 2008;Williams et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

Distribution, source identification and risk assessment of selected metals in sediments from freshwater lake

Iqbal

Shah

Shaheen

2015

International Journal of Sediment Research

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“…Samples collected in the winter have a higher concentration of micronutrients (iron, zinc, and manganese) and macronutrients (calcium and magnesium). The average contents (wet weight and mg per 100 g) for the winter and summer were 551 and 535 for calcium; 117 and 106 for magnesium; 2.13 and 1.48 for iron; 2.63 and 1.95 for zinc, and 2.05 and 1.34 for manganese, respectively (Fadigas et al 2010). Gromboni et al compared the sample preparation procedures: photo-Fenton reaction, microwave irradiation and direct determination for the quantification of barium, calcium, potassium, magnesium, manganese, sodium, and strontium in several fruit juices (passion fruit, pineapple, orange, and grape).…”

Section: Introductionmentioning

confidence: 96%

Use of Multivariate Analysis Techniques for Evaluation of Analytical Data—Determination of the Mineral Composition of Cabbage (Brassica oleracea)

et al. 2010

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Cabbage (Brassica oleracea) is a vegetable food that is found in red and white varieties, and it has been consumed worldwide as raw or cooked. In this paper, the mineral composition of cabbage collected in 24 Brazilian cities was determined and the results were evaluated using multivariate analysis. The samples were digested using nitric acid and hydrogen peroxide and were analyzed using inductively coupled plasma optical emission spectrometry. The accuracy of the method was confirmed by analysis of a certified reference material of spinach leaves, furnished by National Institute of Standard and Technology. The study involved 55 samples, being 31 of the white specie and 24 of the red specie. The results expressed as milligrams of element per kilogram of sample demonstrated that the concentration ranges varied from 1,602.9 to 4,068.3 for potassium, 217.5 to 766.2 for phosphorous, 221.9 to 744.7 for calcium, 67.2 to 286.0 for magnesium, 0.81 to 4.40 for manganese, 1.91 to 8.60 for iron, 0.01 to 0.24 for molybdenum, 1.17 to 5.10 for zinc, 27.2 to 591.0 for sodium, and 0.35 to 5.79 for strontium. The principal component analysis and hierarchical cluster analysis evidenced that the mineral composition of the red specie is not different of the white specie.

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Use of multivariate analysis techniques for the characterization of analytical results for the determination of the mineral composition of kale

Cited by 32 publications

References 23 publications

Occurrence, risk assessment, and source apportionment of heavy metals in surface sediments from Khanpur Lake, Pakistan

Occurrence, risk assessment, and source apportionment of heavy metals in surface sediments from Khanpur Lake, Pakistan

Distribution, source identification and risk assessment of selected metals in sediments from freshwater lake

Use of Multivariate Analysis Techniques for Evaluation of Analytical Data—Determination of the Mineral Composition of Cabbage (Brassica oleracea)

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