Assessing Health Risks Associated with Heavy Metals in Food: A Bibliometric Analysis

Abstract

Bibliometric analysis is an effective method used to identify research trends based on historical publications that involves combining different frameworks, tools and methods, leading to the creation of different metrics. This study employed bibliometric analysis to investigate the global health risk assessment of heavy metals in food from 2000 to 2022 using Web of Science and VOSviewer. We explore publication trends, affiliations, countries, journals, citations, keywords and author collaborations. Of the 573 publications on this topic, there has been a notable increase in recent years. The Ministry of Agriculture and Rural Affairs (China) and Shahid Beheshti University of Medical Sciences (Iran) are the most prolific affiliations. Environmental Science and Pollution Research is the top journal. Notably, "heavy metals", "risk assessment", "cadmium", "lead", and "trace elements" are frequently used keywords. A study by Miraglia et al. in 2009 received the most citations. Amin Mousavi Khaneghah (Poland) is the most prolific author, with 24 papers. Articles mainly focus on contamination levels in fish, seafood, cereals, dairy, meat, and fruit/vegetables. Some studies highlight potential risks, necessitating stricter food product controls for consumer safety.

Keywords: VOSviewer; bibliometrics; food products; health risk assessment; heavy metals.

Figure 1

Dynamics of publications on health risk assessment of heavy metals from food products between 2000 and 2022.

Figure 2

Analysis of affiliations. (a) The collaborations between organizations. (b) Organizations’ citation.

Figure 3

Country analysis. (a) International collaborations of countries. (b) Country citations.

Figure 4

Analysis of journals—network visualization of the most cited journals.

Figure 5

Top 20 cited publications in the field of health risk assessment of heavy metals from food products. 1—Miraglia et al., 2009 [70], 2—Hussain et al., 2017 [71], 3—Lu et al., 2017 [72], 4—Olmedo et al., 2013 [73], 5—Kumar et al., 2020 [74], 6—Yu et al., 2012 [75], 7—Rajeshkumar and Li, 2018 [76], 8—Zwolak et al., 2019 [2], 9—Awasthi et al., 2016 [77], 10—Rose et al., 2010 [78], 11—Mao et al., 2019 [79], 12—Thompson and Darwish, 2019 [80], 13—Antoniadis et al., 2019 [81], 14—Fathabad et al., 2018 [82], 15—Zhang et al., 2018 [83], 16—Tsatsakis et al., 2017 [84], 17—Wiesinger et al., 2021 [85], 18—Olmedo et al., 2013 [86], 19—Grant and Sheppard, 2008 [87], 20—Langie et al., 2015 [88].

Figure 6

Network visualization of documents citation. (Cluster 1: Li (2015) [89]; Zhao (2021) [43], Cluster 2: Ni (2021) [90]; Cluster 3: Roba (2016) [91], Cluster 4: Atambayeva (2022) [92], Cluster 5: Jalali (2021) [93], Cluster 6: Kowalska (2020) [94], Cluster 7: Roleda (2019) [95]; Han (2022) [96]; Wu (2016) [97]; Cluster 8: Gan (2018) [98], Wang (2022) [99], Cluster 9: Usydus (2009) [100], Munoz (2015) [22], Cluster 11: Singh (2021) [101], Giri (2011) [102], Cluster 12: Yousefi (2018) [103], Wang (2020) [104], Cluster 13: Olmedo (2013) [73], Cluster 14: Wei (2020) [105], Cluster 15: Cocarta (2016) [106], Cluster 16: Yu Tao (2012) [75], Cluster 17: Meli (2015) [107]; Cluster 18: Kilic (2021) [108], Cluster 19: Shahbazi (2016) [109], Cluster 20: Lei (2015) [110], Lucic (2022) [31], Cluster 21: Jarzynska (2011) [111], Cluster 22: Zwolak (2019) [2], Cluster 23: Zakaria (2021) [112], Cluster 24: Kong (2021) [113], Cluster 25: Tang (2014) [114], Cluster 26: Thompson (2019) [80], Cluster 27: Olmedo (2013) [86].

Figure 7

Authors analysis. (a) Co-authorship network visualization between the most connected authors. (b) Author citations.

Figure 8

Keyword analysis. (a) Co-occurrence network of all keywords. (b) Co-occurrence of Author Keywords. (c) Co-occurrence of Keywords Plus.

Figure 8

Keyword analysis. (a) Co-occurrence network of all keywords. (b) Co-occurrence of Author Keywords. (c) Co-occurrence of Keywords Plus.