硝酸
原子吸收光谱法
化学
盐酸
镉
消化(炼金术)
微波消解
样品制备
锌
元素分析
分析物
原子光谱法
色谱法
检出限
无机化学
光谱学
有机化学
物理
量子力学
作者
Abm Helal Uddin,Reem S. Khalid,Mohamed Alaama,Abdualrahman Mohammed Abdualkader,Abdulrazak Kasmuri,Syed Atif Abbas
标识
DOI:10.1186/s40543-016-0085-6
摘要
Traditional medicine mainly of herbal origin is widely used all around the world. Heavy metal contamination in such products is frequently reported. Accumulation of heavy metals in the human body leads to various health hazards. Thus, precise determination for such contaminants is required for safety assurance. Sample preparation is a significant step in spectroscopic analysis to achieve reliable and accurate results. Wet digestion methods are basically used for the dissolution of herbal product samples prior to elemental analysis. This study has been designed to evaluate the efficiency of three acid digestion methods using different solvents. Five samples were digested with three different acid digestion methods namely method A (a combination of nitric-perchloric acids HNO3–HClO4 in a ratio 2:1), method B (only nitric acid HNO3), and method C (a mixture of nitric-hydrochloric acids HNO3–HCl in a ratio 1:3), to recommend the most efficient digestion method that gains the highest analyte recovery. The analysis of arsenic (As), cadmium (Cd), lead (Pb), nickel (Ni), zinc (Zn), and iron (Fe) was conducted using various techniques of atomic absorption spectrometry (AAS). The statistical analysis revealed that method C which represented the combination of nitric-hydrochloric acids HNO3–HCl in a ratio 1:3 was the most efficient digestion method for herbal product samples as it had given a significant high recovery (p < 0.05) for all metals compared to method A and method B. Accuracy of the proposed method was evaluated by the analysis of standard reference material (SRM) 1515 Apple Leaves from the National Institute of Standards and Technology (NIST) which presented good recoveries for all metals ranging from 94.5 to 108 %. Method C provides highest recovery for all the analytes under investigation using AAS in herbal medicine samples.
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