Application of Pearson and partial correlation coefficient model in the research of heavy metal pollution in rice
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(1.School of Public Health, Peking Union Medical College, Beijing 100730, China;2.China National Center for Food Safety Risk Assessment, Beijing 100022, China;3.Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China)

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    Abstract:

    Objective To construct and compare the methods for correlation analysis of different elements in rice,including barium, vanadium, cadmium, lithium, aluminum, manganese, lead,thallium,antimony,copper, selenium, ehromium, mercury and arsenic. Methods Analyze the correlation among the fourteen elements in rice by two methods:Pearson correlation coefficient and partial correlation coefficient, and compare the two methods. Results Both of the methods can find the correlations among various pollutants from the data and have their own characteristics on computational complexity, information abundancy and other aspects:Pearson correlation coefficient method has less computation, but also provides less information; Partial correlation coefficient provides more information but needs more samples and computing resources. The Pearson correlation coefficient method showed the positive correlation elements including barium-vanadium, barium-lead, vanadium-lithium, aluminum-antimony and copper-thallium. There was no significant correlation between the remaining elements. The partial correlation coefficient method showed strong positive correlation including vanadium-barium, lead-barium,total mercury-barium and antimony-aluminum. There was no significant correlation between the remaining elements. Conclusion Under the current data and software, hardware conditions, the correlation analysis of the partial correlation coefficient is recommended.

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WANG Shan, SU Liang, LIU Yuanli, WANG Xiaowan. Application of Pearson and partial correlation coefficient model in the research of heavy metal pollution in rice[J].中国食品卫生杂志,2020,32(6):631-635.

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  • Received:September 28,2020
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  • Online: February 18,2021
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