2015
DOI: 10.1080/03067319.2015.1036861
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Determination of trace elements in Italian wines by means of total reflection X-ray fluorescence spectroscopy

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Cited by 19 publications
(13 citation statements)
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“…Aluminum (Al), nickel (Ni), and manganese (Mn) demonstrated to play a role in the taste of wine [152], whereas copper (Cu), Fe, and Zn, which are essential metals, have been reported to enrich the nutrition quality of wine. In this sense, moderate wine consumption contributes to the intake of these and other essential metals (e.g., Ca, K, Mg) [152,153]. On the other hand, heavy metals (e.g., Cd, arsenic (As)), which can also be part of wines' composition, may affect human health owing to toxicity reasons [154,155].…”
Section: Metalsmentioning
confidence: 99%
“…Aluminum (Al), nickel (Ni), and manganese (Mn) demonstrated to play a role in the taste of wine [152], whereas copper (Cu), Fe, and Zn, which are essential metals, have been reported to enrich the nutrition quality of wine. In this sense, moderate wine consumption contributes to the intake of these and other essential metals (e.g., Ca, K, Mg) [152,153]. On the other hand, heavy metals (e.g., Cd, arsenic (As)), which can also be part of wines' composition, may affect human health owing to toxicity reasons [154,155].…”
Section: Metalsmentioning
confidence: 99%
“…In the last decades, among the various methods, total reflection X-ray analysis fluorescence spectrometry (TXRF) has also been considered a very suitable multielemental tool for use in food analysis ( Borgese et al, 2015 , Klockenkämper and von Bohlen, 2015 ). Sample preparation methods are usually a necessary and very important step in chemical analysis, and an advantage of TXRF is the ability to analyze complex liquid samples with simpler sample treatments (i.e., direct analysis or dilution samples) that are more in line with Green Analytical Chemistry principles ( Dalipi et al, 2015 , Jablan et al, 2021 , Pessanha et al, 2010 ). Moreover, in most cases, quantification by TXRF can be performed directly by internal standardization and thus the use of external calibration using matrix-matched standards is not required as in the case of ICP-MS analysis ( Obhod̵aš et al, 2021 , Selih et al, 2014 ).…”
Section: Introductionmentioning
confidence: 99%
“…Уже имеется достаточное количество публикаций по анализу пищевых объектов с различным содержанием сахара методом TXRF. Объекты анализа можно классифицировать следующим образом: вина (с содержанием сахара, 0.5 -3 %) [6][7][8][9][10][11][12][13], газированные напитки (около 10 %) [14,15], алкогольные напитки «Кашаса» (до 0.6 %) [16,17], конфеты (не менее 25 %) [18], мед (не менее 80 %) [19][20][21][22][23]. В большинстве работ отмечена высокая эффективность метода TXRF благодаря быстрому анализу проб напрямую без специальной подготовки либо после простого разбавления водой.…”
Section: Introductionunclassified