| 光谱学与光谱分析 |
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| Determination of Nine Mineral Elements in Hulless Barley by Ultraviolet Spectrophotometry and Flame Atomic Absorption Spectrometry |
| LIU Jin1, 2，ZHANG Huai-gang1* |
1. Northwest Institute of Plateau Biology，Chinese Academy of Sciences，Xining 810001，China
2. Graduate University of Chinese Academy of Sciences，Beijing 100049，China |
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Abstract The contents of nine mineral elements, including sulphur, zinc, calcium, magnesium, potassium, sodium, iron, copper and manganese in five hulless barley (Hordeum vulgare L. var. nudum Hook. f.) lines were determined by ultraviolet spectrophotometry and flames atomic absorption spectrometry (FAAS). For the determination of sulphur, the samples were dissolved by magnesia and anhydrous sodium carbonate at 250 ℃ for 0.5 h and at 550 ℃ for 3 h in the muffle furnace, and then a certain amount of barium chloride was put into the sample solution for colorimetry of the UV-Vs spectrophotometer. For the determination of other eight mineral elements, all of the samples were dissolved by a kind of incinerating method: first, the sample was put into the muffle furnace at 250 ℃ for 0.5 h and at 550 ℃ for 2.5 h, then two droplets of 50%HNO3 were distributed into each sample, and the last step was putting the sample into the muffle furnace at 550 ℃ for 0.5 h. And then all of the ash was dissolved by 50%HNO3 to 50 milliliter and determined by flames atomic absorption spectrometry. The precision, accuracy, repeatability and stability of the method were discussed too. The results showed that the relative standard deviations (RSD) were between 1.2% and 3.7%; The average recoveries were 97.44%-101.52% and the relative standard deviations (RSD) of sample determination were 1.3%-3.8%. The repeatability experiment showed that the relative standard deviations (RSD) were 2.6%-6.1%. And the content of each mineral element was the same after 24 hours; All these showed that the method has a good precision, accuracy, repeatability and stability. In all the hulless barley samples, the average contents were in the order of K＞S＞Mg＞Ca＞Fe＞Na＞Zn＞Mn＞Cu，and the contents of zinc, iron and manganese closely related to people’s health were relatively higher than other crops. The data of the experiment could provide an accurate and credible evidence for the deeper exploitation of the hulless barley.
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Received: 2009-05-06
Accepted: 2009-08-09
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Corresponding Authors:
ZHANG Huai-gang
E-mail: hgzhang@nwipb.ac.cn
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[1] XIE Zong-wan(谢宗万). Colorplate of Compendium of Materia Medica(本草纲目药物彩色图鉴). Beijing：People’s Medical Publishing House(北京：人民卫生出版社)，2001. 221.
[2] XIAO Gu-qing(肖谷清). Chinese Journal of Spectroscopy Laboratory(光谱实验室)，2006，23(3)：493.
[3] ZHANG Yuan，ZHANG Lu-da，BAI Qi-lin，et al(张 愿，张录达，白琪林, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2009，29(3)：686.
[4] WANG De-kai，PEI Ke-mei，LIU He-qin(王得凯，裴克梅，刘合芹). Chinese Journal of Spectroscopy Laboratory(光谱实验室)，2008，25(1)：1.
[5] LIN Jian-yuan，HUANG Wei(林建原，黄 炜). Guangdong Trace Elements Science(广东微量元素科学)，2008，15(4)：52.
[6] Nardi E P，Evangelista F S，Tormen L，et al. Food Chemistry，2009，112(3)：727.
[7] Ferreiraa H S，Santosa A C N，Portugal L A，et al. Talanta，2008，77 (1)：73.
[8] Araujo R G O，Dias F S，Macedo S M，et al. Food Chemistry，2007，101(1)：397.
[9] Biljana S，Antonije O. Food Control，2007，18(4)：338.
[10] LI Zeng-ning，MA Li，HE Yan，et al(李增宁，马 莉，何 燕，等). Chinese Journal of Health Laboratory Technology(中国卫生检验杂志)，2007，17(4)：660.
[11] Cubadda F, Raggi A, Marconi E，et al. Microchemical Journal，2005，79(1-2)：97.
[12] Frost H L，Ketchum Jr L H. Advances in Environmental Research，2000，4(4)：347.
[13] Saracoglu S，Saygi K O，Uluozlu O D，et al. Food Chemistry，2007，105(1)：280.
[14] Coudray C，Levrat-Verny M A，Tressol J C，et al. Journal of Trace Elements in Medicine and Biology，2001，15(2-3)：131.
[15] Tuna A L，Kaya C，Higgs D，et al. Environmental and Experimental Botany，2008，62(1)：10.
[16] ZHANG Ying-hua，ZHOU Shun-li，ZHANG Kai，et al(张英华，周顺利，张 凯，等). Acta Agronomica Sinica(作物学报)，2008，34(9)：1629.
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