Dana Alina Magdas

Dr. Dana Alina Magdas

National Institute for Research & Development of Isotopic & Molecular Technologies, Strada Donath 67-103, Cluj-Napoca 400293, Romania
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Research Focus & Metrics

Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 October 2020 in Talanta
Verified
Journal Article
Closed access
Camelia GrosanCamelia Grosan
Camelia Grosan
Dana Alina MagdasDana Alina Magdas
Dana Alina Magdas

1 January 2021 in Food Chemistry
Verified
Journal Article
Closed access
Dana Alina MagdasDana Alina Magdas
Dana Alina Magdas
FGFrancois Guyon
Francois Guyon
RPRomulus Puscas
Romulus Puscas
AVAudrey Vigouroux
Audrey Vigouroux
LGLaetitia Gaillard
Laetitia Gaillard
Gabriela CristeaGabriela Cristea
Gabriela Cristea

1 January 2013 in The Scientific World Journal
Verified
Journal Article
Open access
PDF file
ADAdriana Dehelean
Adriana Dehelean
Dana Alina MagdasDana Alina Magdas
Dana Alina Magdas
The presence of potentially toxic elements and compounds in foodstuffs is of intense public interest and thus requires rapid and accurate methods to determine the levels of these contaminants. Inductively coupled plasma mass spectrometry is a powerful tool for the determination of metals and nonmetals in fruit juices. In this study, 21 commercial fruit juices (apple, peach, apricot, orange, kiwi, pear, pineapple, and multifruit) present on Romanian market were investigated from the heavy metals and mineral content point of view by ICP‐MS. Our obtained results were compared with those reported in literature and also with the maximum admissible limit in drinking water by USEPA and WHO. For Mn the obtained values exceeded the limits imposed by these international organizations. Co, Cu, Zn, As, and Cd concentrations were below the acceptable limit for drinking water for all samples while the concentrations of Ni and Pb exceeded the limits imposed by USEPA and WHO for some fruit juices. The results obtained in this study are comparable to those found in the literature.

1 March 2018 in Food Control
Verified
Journal Article
Closed access
Dana Alina MagdasDana Alina Magdas
Dana Alina Magdas
FGFrançois Guyon
François Guyon
IFIoana Feher
Ioana Feher
Simona Cinta PinzaruSimona Cinta Pinzaru
Simona Cinta Pinzaru

3 December 2020 in Scientific Reports
Verified
Journal Article
Open access
PDF file
CBCamelia Berghian-Grosan
Camelia Berghian-Grosan
Dana Alina MagdasDana Alina Magdas
Dana Alina Magdas
Through this pilot study, the association between Raman spectroscopy and Machine Learning algorithms were used for the first time with the purpose of distillates differentiation with respect to trademark, geographical and botanical origin. Two spectral Raman ranges (region I—200–600 cm−1 and region II—1200–1400 cm−1) appeared to have the higher discrimination potential for the investigated distillates. The proposed approach proved to be a very effective one for trademark fingerprint differentiation, a model accuracy of 95.5% being obtained (only one sample was misclassified). A comparable model accuracy (90.9%) was achieved for the geographical discrimination of the fruit spirits which can be considered as a very good one taking into account that this classification was made inside Transylvania region, among neighbouring areas. Because the trademark fingerprint is the prevailing one, the successfully distillate type differentiation, with respect to the fruit variety, was possible to be made only inside of each producing entity.