Safety risk assessment and early warning of chemical contamination in vegetable oil

Niu, Bing; Zhang, Hui; Zhou, Guangya; Zhang, Shuwen; Yang, Yibing; Deng, Xiaojun; Qin, Chen

doi:10.1016/j.foodcont.2021.107970

Cited by 32 publications

(27 citation statements)

References 48 publications

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“…Many different types of commercial vegetable oils are currently available, which range from traditional and generally inexpensive canola oil, peanut oil, soy oil, and sunflower oil to more exotic and expensive flaxseed oil and olive oil. Although most commercially available edible vegetable oils are produced by responsible vendors, economic motivations have led to the unreported adulteration of vegetable oils with less expensive vegetable oil substitutes by unscrupulous vendors [ 1 , 2 ]. This is a serious issue not only because it represents consumer deception, but also because it can lead to health and safety issues [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints

Huang

Xin

Sun

et al. 2021

Foods

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Most current approaches applied for the essential identification of adulteration in edible vegetable oils are of limited practical benefit because they require long analysis times, professional training, and costly instrumentation. The present work addresses this issue by developing a novel simple, accurate, and rapid identification approach based on the magnetic resonance relaxation fingerprints obtained from low-field nuclear magnetic resonance spectroscopy measurements of edible vegetable oils. The relaxation fingerprints obtained for six types of edible vegetable oil, including flaxseed oil, olive oil, soybean oil, corn oil, peanut oil, and sunflower oil, are demonstrated to have sufficiently unique characteristics to enable the identification of the individual types of oil in a sample. By using principal component analysis, three characteristic regions in the fingerprints were screened out to create a novel three-dimensional characteristic coordination system for oil discrimination and adulteration identification. Univariate analysis and partial least squares regression were used to successfully quantify the oil adulteration in adulterated binary oil samples, indicating the great potential of the present approach on both identification and quantification of edible oil adulteration.

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Section: Introductionmentioning

confidence: 99%

Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints

Huang

Xin

Sun

et al. 2021

Foods

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show abstract

“…A food safety assessment has to consider the effect of food pollutant exposure on human beings. Accordingly, B. Niu et al [14] established dietary exposure models, which are typically used to assess the carcinogenic and non-carcinogenic risks of children and adults after metal exposure [15], allowing for a comprehensive assessment of the health risks in vegetables and providing scientific and comprehensive support for risk assessments.…”

Section: Background Studies 21 Food Safety Risk Assessment and Classi...mentioning

confidence: 99%

“…In this study, three heavy metals that cause heavy metal pollution in grain processing products are selected as the research objects: chromium [35], cadmium [14], and arsenic [36]. A total of 65,302 samples from the 2020 National Food Safety Sampling Inspection Information System are included: chromium (12,501), cadmium (29,456), and arsenic (23,795).…”

Section: Data Sourcesmentioning

confidence: 99%

A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

Wang

Zou

et al. 2022

Foods

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Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.

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“…The Fuzzy-AHP still has strong subjective assumption although it has optimized the comparison matrix by fuzzy numbers (Khamhong et al , 2019). Here, an improved data-driven AHP (Niu et al , 2021; Geng et al , 2021) based on the sum product method (AHP-SP) is proposed to calculate the index weight based on the inherent law of the data. Therefore, risk evaluation results obtained through the AHP-SP method are objective.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk early warning of food safety using novel long short-term memory neural network integrating sum product based analytic hierarchy process

Geng

Lingling

Han

et al. 2021

BFJ

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PurposeFood safety risk brought by environmental pollution seriously threatens human health and affects national economic and social development. In particular, heavy metal pollution and nutrient deficiency have caused regional diseases. Thus, the purpose of this paper is to present a risk early warning method of food safety considering environmental and nutritional factors.Design/methodology/approachA novel risk early warning modelling method based on the long short-term memory (LSTM) neural network integrating sum product based analytic hierarchy process (AHP-SP) is proposed. The data fuzzification method is adopted to overcome the uncertainty of food safety detection data and the processed data are viewed as the input of the LSTM. The AHP-SP method is used to fuse the risk of detection data and the obtained risk values are viewed as the expected output of the LSTM. Finally, the proposed method is applied on one group of sterilized milk data from a food detection agency in China.FindingsThe experimental results show that compared with the back propagation and the radial basis function neural networks, the proposed method has higher accuracy in predicting the development trend of food safety risk. Moreover, the causal factors of the risk can be figured out through the predicted results.Originality/valueThe proposed modelling method can achieve accurate prediction and early warning of food safety risk, and provide decision-making basis for the relevant departments to formulate targeted risk prevention and control measures, thereby avoiding food safety incidents caused by environmental pollution or nutritional deficiency.

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Safety risk assessment and early warning of chemical contamination in vegetable oil

Cited by 32 publications

References 48 publications

Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints

Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints

A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

Risk early warning of food safety using novel long short-term memory neural network integrating sum product based analytic hierarchy process

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