SUN Wanqing, LI Xupeng, CEN Jianwei, CHEN Shengjun, DENG Jianchao, PAN Chuang, LI Chunsheng. Comparison of three methods of detecting aluminum content in jellyfish products[J]. South China Fisheries Science, 2021, 17(6): 101-106. DOI: 10.12131/20210112
Citation: SUN Wanqing, LI Xupeng, CEN Jianwei, CHEN Shengjun, DENG Jianchao, PAN Chuang, LI Chunsheng. Comparison of three methods of detecting aluminum content in jellyfish products[J]. South China Fisheries Science, 2021, 17(6): 101-106. DOI: 10.12131/20210112

Comparison of three methods of detecting aluminum content in jellyfish products

More Information
  • Received Date: April 08, 2021
  • Revised Date: May 07, 2021
  • Accepted Date: June 07, 2021
  • Available Online: June 20, 2021
  • In this paper, the content of aluminum in jellyfish (Rhopilema esculentum) was determined by inductively coupled plasma mass spectrometry (ICP-MS), spectrophotometry and EDTA titration. The differences of detection results, precision and accuracy were compared, and the advantages and disadvantages of three methods in industrial application were analyzed. The aluminum residue risk of ready-to-eat jellyfish in ten coastal cities of China was investigated by ICP-MS. The results show that there was no significant difference between ICP-MS and spectrophotometry. EDTA titration was different from the other two methods, but the difference was not significant. The relative standard deviation (RSD) of the three methods for salted jellyfish and ready-to-eat jellyfish ranged from 1.79% to 4.34%. The RSD of salted jellyfish and ready-to-eat jellyfish by ICP-MS were 92%−104% and 97%−100%, respectively. The RSD of spectrophotometry were 97%−102% and 98%−101%, respectively. The recovery of EDTA titration was 94%−99%, which were relatively low. The three methods all meet the testing requirements, and the appropriate testing method can be selected according to the actual needs. The results also indicate that the Al residue in ready-to-eat jellyfish in some areas is close to the national limit.
  • [1]
    单继航. 影响即食海蜇品质因素的研究[D]. 无锡: 江南大学, 2010: 1-7.
    [2]
    杨永芳, 黄芳芳, 丁国芳. 水母的化学组成及生物活性的研究进展[J]. 浙江海洋学院学报(自然科学版), 2009, 28(1): 86-90.
    [3]
    HSIEH Y H P, LEONG F M, RUDLOE J. Jellyfish as food[J]. Hydrobiologia, 2001, 451: 11-17. doi: 10.1023/A:1011875720415
    [4]
    DONG Z J, LIU D Y, KEESING J K. Jellyfish blooms in China: dominant species, causes and consequences[J]. Mar Pollut, 2010, 60(7): 954-963. doi: 10.1016/j.marpolbul.2010.04.022
    [5]
    农业农村部渔业渔政管理局. 2020中国渔业统计年鉴[M]. 北京: 中国农业出版社, 2020: 172.
    [6]
    刘希光. 海蜇的化学组成及其生物活性研究[D]. 青岛: 中国科学院研究生院(海洋研究所), 2004: 1-14.
    [7]
    陈伯虎, 李八方, 许志恒, 等. 海蜇养殖与加工技术的研究进展[J]. 安徽农业科学, 2010, 38(17): 9045-9047. doi: 10.3969/j.issn.0517-6611.2010.17.084
    [8]
    吕子娟. 即食海蜇标准研究及货架期预测[D]. 大连: 大连工业大学, 2016: 1-13.
    [9]
    刘洋, 赵玲, 刘淇, 等. 盐渍海蜇加工过程中铝的变化规律研究[J]. 食品安全质量检测学报, 2016, 7(5): 2042-2045.
    [10]
    李彩云. 铝的危害与防治方法研究[J]. 广东化工, 2017, 44(6): 90-91. doi: 10.3969/j.issn.1007-1865.2017.06.039
    [11]
    European Food Safety Authority. Safety of aluminium from dietary intake: scientific opinion of the panel on food additives, flavourings, processing aids and food contact materials (AFC)[J]. EFSA J, 2008, 754: 1-34.
    [12]
    NIU Q. Overview of the relationship between aluminum exposure and health of human being[J]. Adv Exp Med Biol, 2018, 1091: 1-31.
    [13]
    COLOMINA M T, PERIS-SAMPEDRO F. Aluminum and alzheimer's disease[J]. Neurotox Met, 2017: 183-197.
    [14]
    杜鹏. 铝的过量摄入对人体影响分析研究[J]. 中国卫生产业, 2018, 15(13): 150-151.
    [15]
    岑剑伟, 孙万青, 陈胜军, 等. 即食海蜇中铝检测、脱除与杀菌新技术研究进展[J]. 食品与发酵工业, 2021, 47(8): 268-275.
    [16]
    SHI Y F, ZHAN Q Y, TIAN L L, et al. Risk analysis and determination of aluminum concentration in jellyfish (Rhopilema esculentum)[C]//Proceedings of the 2016 International Conference on Biological Sciences and Technology, 2016: 343-347.
    [17]
    谭秀慧, 朱晓华, 李萍, 等. 即食海蜇中金属元素含量分析及食用安全性评价[J]. 食品工业, 2019, 40(3): 155-158.
    [18]
    王晔, 郭和光, 余娟. 水浴浸提-电感耦合等离子体质谱法测定粉丝铝含量[J]. 预防医学, 2020, 32(2): 208-210.
    [19]
    李文静, 袁春满, 高婷, 等. 血糖升高在职业铝暴露致工人认知障碍中的中介作用[J]. 环境与职业医学, 2021, 38(3): 217-222.
    [20]
    李建荣, 舒士倡, 张学玲, 等. 微波消解-电感耦合等离子体质谱法测定枸杞中22种痕量元素[J]. 理化检验(化学分册), 2021, 57(3): 278-282.
    [21]
    宋政, 马丽霞, 彭青枝, 等. 海苔和茶叶中铝的亚细胞分布及风险评估[J]. 食品与机械, 2021, 37(3): 46-50, 63.
    [22]
    张美琴, 罗玲, 陈海仟, 等. 石墨炉原子吸收光谱法测定水产品中的铝[J]. 食品科学, 2011, 32(10): 156-159.
    [23]
    周易枚, 陈彬, 蒋林惠, 等. 微波消解-石墨炉原子吸收光谱法检测食品中的铝[J]. 食品安全质量检测学报, 2017, 8(12): 4736-4740. doi: 10.3969/j.issn.2095-0381.2017.12.039
    [24]
    WONG W W K, CHUNG S W C, KWONG K P, et al. Dietary exposure to aluminium of the Hong Kong population[J]. Food Addit Contam, 2010, 27(4): 457-463. doi: 10.1080/19440040903490112
    [25]
    YANG X Q, WU Z Y, CEN J W, et al. Research on detecting aluminum in jellyfish by flame atomic absorption method[J]. Adv Mat Res, 2014, 3514(2067): 732-737.
    [26]
    吴则业, 杨贤庆, 岑剑伟, 等. 海蜇产品中铝含量3种测定方法的比较[J]. 食品与发酵工业, 2015, 41(3): 204-208.
    [27]
    余娟, 王晔, 邵国健. 不同方法测定海蜇中铝含量比较[J]. 预防医学, 2018, 30(9): 970-972.
    [28]
    岑剑伟, 李来好, 杨贤庆, 等. 酸煮滴定法测定海蜇产品中的明矾[J]. 食品科学, 2008, 29(9): 481-484. doi: 10.3321/j.issn:1002-6630.2008.09.112
    [29]
    杨贤庆, 吴则业, 岑剑伟, 等. 微波消解-EDTA滴定法测定海蜇产品中铝的含量[J]. 中国渔业质量与标准, 2014, 4(3): 18-22.
    [30]
    白艳艳, 谷伟丽, 马元庆, 等. 海蜇中铝测定前处理方法及检测方法比较[J]. 食品安全质量检测学报, 2014, 5(4): 1197-1203.
  • Related Articles

    [1]ZHAO Lianling, LIU Huaxue, RAO Yiyong, LIAO Xiuli, DAI Ming, HUANG Honghui. Seawater quality assessment method based on improved grey relational degree clustering: a case study of Daya Bay[J]. South China Fisheries Science, 2024, 20(1): 141-150. DOI: 10.12131/20230031
    [2]ZHOU Xihan, WU Qia'er, ZHOU Yanbo, XIE Enge, MA Shengwei. Prediction of abundance of Sthenoteuthis oualaniensis in South China Sea based on optimized grey system model[J]. South China Fisheries Science, 2021, 17(3): 1-7. DOI: 10.12131/20200218
    [3]LI Ting, ZHU Changbo, LI Junwei, CHEN Suwen, XIE Xiaoyong, LIU Yong. Water quality assessment for Hailing Bay estuary, China[J]. South China Fisheries Science, 2018, 14(3): 49-57. DOI: 10.3969/j.issn.2095-0780.2018.03.006
    [4]MO Baolin, QIN Chuanxin, CHEN Pimao, DIAO Yingjiao, YUAN Huarong, LI Xiaoguo, TONG Fei, FENG Xue. Preliminary analysis of structure and function of Daya Bay ecosystem based on Ecopath model[J]. South China Fisheries Science, 2017, 13(3): 9-19. DOI: 10.3969/j.issn.2095-0780.2017.03.002
    [5]SU Li, HUANG Zirong, CHEN Zuozhi. Characteristics of phytoplankton community in Shuidong Bay in spring and autumn[J]. South China Fisheries Science, 2015, 11(4): 27-33. DOI: 10.3969/j.issn.2095-0780.2015.04.004
    [6]YU Ying, JIANG Linlin, ZHONG Shuoliang. Residual characteristics of PCBs in ecological environment of Dongshan Bay in Fujian Province[J]. South China Fisheries Science, 2014, 10(1): 64-70. DOI: 10.3969/j.issn.2095-0780.2014.01.010
    [7]LIU Qun, XU Binduo, REN Yiping. Prediction of freshwater aquaculture production of Qingdao city by using a grey prediction model[J]. South China Fisheries Science, 2009, 5(5): 38-43. DOI: 10.3969/j.issn.1673-2227.2009.05.007
    [8]LI Xu-jie, REN Yi-ping, XU Bin-duo, MA Guang-wen. The growth characteristics of Penaeus japonicus in the Guzhenkou Bay of Qingdao[J]. South China Fisheries Science, 2008, 4(4): 26-29.
    [9]LIN Lin, LI Chunhou, DU Feiyan, DAI Ming, HUANG Honghui. GIS-based comprehensive assessment of marine ecological environment quality in Daya Bay[J]. South China Fisheries Science, 2007, 3(5): 19-25.
    [10]LU Zhenbin, CAI Qinghai, ZHANG Xuemin. Estimation of the aquaculture pollution to water body in Tongan Bay[J]. South China Fisheries Science, 2007, 3(1): 54-61.
  • Cited by

    Periodical cited type(4)

    1. 江满菊,郭禹,秦传新,辛益,赵心冉,于刚,马振华,杨育凯. 红鳍笛鲷幼鱼对不同开孔形状和尺寸人工鱼礁模型的行为偏好探究. 南方水产科学. 2024(05): 42-52 . 本站查看
    2. 曾钰,肖曲,陶佳,高雯媛,张翔宇. 基于人工神经网络构建湖南省企业重金属废水预测模型研究. 科技资讯. 2024(24): 202-204 .
    3. 马晓霞. 地下水水环境中的非突发性水质风险预测模型研究. 环境科学与管理. 2022(08): 181-185 .
    4. 徐文哲,朱丽梅,范海莉,王增远,周翠兰. 基于神经网络算法的造纸生产线废水处理水质预测. 造纸科学与技术. 2022(02): 39-43 .

    Other cited types(9)

Catalog

    Article views (642) PDF downloads (29) Cited by(13)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return