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Routine Sample Preparation and HPLC Analysis for Ascorbic Acid (Vitamin C) Determination in Wheat Plants and Arabidopsis Leaf Tissues

Overview of attention for article published in Biologia Futura, December 2014
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Title
Routine Sample Preparation and HPLC Analysis for Ascorbic Acid (Vitamin C) Determination in Wheat Plants and Arabidopsis Leaf Tissues
Published in
Biologia Futura, December 2014
DOI 10.1556/abiol.65.2014.2.8
Pubmed ID
Authors

Gabriella Szalai, T. Janda, Magda Pál

Abstract

Plants have developed various mechanisms to protect themselves against oxidative stress. One of the most important non-enzymatic antioxidants is ascorbic acid. There is thus a need for a rapid, sensitive method for the analysis of the reduced and oxidised forms of ascorbic acid in crop plants. In this paper a simple, economic, selective, precise and stable HPLC method is presented for the detection of ascorbate in plant tissue. The sensitivity, the short retention time and the simple isocratic elution mean that the method is suitable for the routine quantification of ascorbate in a high daily sample number. The method has been found to be better than previously reported methods, because of the use of an economical, readily available mobile phase, UV detection and the lack of complicated extraction procedures. The method has been tested on Arabidopsis plants with different ascorbate levels and on wheat plants during Cd stress.

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The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Algeria 1 2%
United States 1 2%
Unknown 44 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 22%
Researcher 7 15%
Student > Bachelor 5 11%
Student > Doctoral Student 5 11%
Student > Master 4 9%
Other 8 17%
Unknown 7 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 43%
Chemistry 6 13%
Biochemistry, Genetics and Molecular Biology 3 7%
Nursing and Health Professions 2 4%
Engineering 2 4%
Other 2 4%
Unknown 11 24%