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. 2022 Sep 23:9:990628.
doi: 10.3389/fnut.2022.990628. eCollection 2022.

Prediction models for monitoring selenium and its associated heavy-metal accumulation in four kinds of agro-foods in seleniferous area

Linshu Jiao  1 , Liuquan Zhang  1   2 , Yongzhu Zhang  1 , Ran Wang  1 , Xianjin Liu  1 , Baiyi Lu  1   2
Affiliations

Prediction models for monitoring selenium and its associated heavy-metal accumulation in four kinds of agro-foods in seleniferous area

Linshu Jiao et al. Front Nutr. .

Abstract

Se-rich agro-foods are effective Se supplements for Se-deficient people, but the associated metals have potential risks to human health. Factors affecting the accumulation of Se and its associated metals in Se-rich agro-foods were obscure, and the prediction models for the accumulation of Se and its associated metals have not been established. In this study, 661 samples of Se-rich rice, garlic, black fungus, and eggs, four typical Se-rich agro-foods in China, and soil, matrix, feed, irrigation, and feeding water were collected and analyzed. The major associated metal for Se-rich rice and garlic was Cd, and that for Se-rich black fungus and egg was Cr. Se and its associated metal contents in Se-rich agro-foods were positively correlated with Se and metal contents in soil, matrix, feed, and matrix organic contents. The Se and Cd contents in Se-rich rice grain and garlic were positively and negatively correlated with soil pH, respectively. Eight models for predicting the content of Se and its main associated metals in Se-rich rice, garlic, black fungus, and eggs were established by multiple linear regression. The accuracy of the constructed models was further validated with blind samples. In summary, this study revealed the main associated metals, factors, and prediction models for Se and metal accumulation in four kinds of Se-rich agro-foods, thus helpful in producing high-quality and healthy Se-rich.

Keywords: Se-rich agro-food; metals; metals accumulation; prediction model; selenium.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Distribution of sampling sites.
FIGURE 2
FIGURE 2
Pearson correlations analysis of Se and potential coexisting elements in Se-rich rice (A), garlic (B), black fungus (C), and egg (D). **Represents for highly significant correlation, p < 0.01, *represents for significant correlation, p < 0.05. The connections between the nodes represent for the significance of the correlation between different elements, solid red line represents for highly significant correlation, p < 0.01, solid blue line represents for significant correlation, p < 0.05.
FIGURE 3
FIGURE 3
The influence of factors on Se and Cd contents in rice grain. (A–D) The influence of Se content in soil, soil pH, SOM, and Se content in water on Se content in rice grain, respectively. (E–H) The influence of Cd content in soil, soil pH, SOM, and Cd content in water on Cd content in rice grain, respectively.
FIGURE 4
FIGURE 4
The influence of factors on Se and Cd contents in garlic. (A–D) The influence of Se content in soil, soil pH, SOM, and Se content in water on Se content in garlic, respectively. (E–H) The influence of Cd content in soil, soil pH, SOM, and Cd content in water on Cd content in garlic, respectively.
FIGURE 5
FIGURE 5
The influence of factors on Se and Cr contents in black fungus. (A–D) The influence of Se content in matrix, MOM, matrix pH, and Se content in water on Se content in black fungus, respectively. (E–H), The influence of Cr content in matrix, MOM, matrix pH, and Cr content in water on Cr content in black fungus, respectively.
FIGURE 6
FIGURE 6
The influence of factors on Se and Cr contents in egg. (A,B) The influence of Se content in feed and Se content in water on Se content in egg, respectively. (C,D) The influence of Cr content in feed and Cr content in water on Cr content in egg, respectively.
FIGURE 7
FIGURE 7
Validation of prediction models in Se-rich rice grain. (A,B) Correlation between the fitted and measured Se and Cd contents, respectively. (C,D) Residual analysis of the fitted and measured Se and Cd contents, respectively.
FIGURE 8
FIGURE 8
Validation of prediction models in Se-rich garlic. (A,B) Correlation between the predicted and measured Se and Cd contents, respectively. (C,D) Residual analysis of the fitted and measured Se and Cd contents, respectively.
FIGURE 9
FIGURE 9
Validation of prediction models in Se-rich black fungus. (A,B) Correlation between the predicted and measured Se and Cr contents, respectively. (C,D) Residual analysis of the fitted and measured Se and Cr contents, respectively.
FIGURE 10
FIGURE 10
Validation of prediction models in Se-rich egg. (A,B) Correlation between the predicted and measured Se and Cr contents, respectively. (C,D) Residual analysis of the fitted and measured Se and Cr contents, respectively.
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