Detection Strategies of Zearalenone for Food Safety: A Review

Çağlayan, Mustafa Oğuzhan; Şahin, Samet; Üstündağ, Zafer

doi:10.1080/10408347.2020.1797468

Cited by 37 publications

(15 citation statements)

References 183 publications

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“…Over the past ten years, the ZEN-binding aptamers with nanomolar affinity have been isolated by several research groups ( Supporting Information (SI) Table S1 ). Based on these aptamers, different types of ZEN-aptasensors including colorimetric, fluorescence, and electrochemical sensing platforms have been reported ( SI Table S2 ) [ 22 , 23 ]. By using various types of nanomaterials, the sensitivities of the aptasensors have been improved orders of magnitude, especially for the electrochemical aptasensors, which are inherent much more sensitive than optical sensors.…”

Section: Introductionmentioning

confidence: 99%

Evanescent Wave Optical-Fiber Aptasensor for Rapid Detection of Zearalenone in Corn with Unprecedented Sensitivity

et al. 2022

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Zearalenone (ZEN) is a common mycotoxin pollutant found in agricultural products. Aptamers are attractive recognition biomolecules for the development of mycotoxin biosensors. Even though numerous aptasensors have been reported for the detection of ZEN in recent years, many of them suffer from problems including low sensitivity, low specificity, tedious experimental steps, high-cost, and difficulty of automation. We report here the first evanescent wave optical-fiber aptasensor for the detection of ZEN with unprecedented sensitivity, high specificity, low cost, and easy of automation. In our aptasensor, a 40-nt ZEN-specific aptamer (8Z31) is covalently immobilized on the fiber. The 17-nt fluorophore Cy5.5-labeled complementary DNA strand and ZEN competitively bind with the aptamer immobilized on the fiber, enabling the signal-off fluorescent detection of ZEN. The coating of Tween 80 enhanced both the sensitivity and the reproducibility of the aptasensor. The sensor was able to detect ZEN spiked-in the corn flour extract with a semilog linear detection range of 10 pM-10 nM and a limit of detection (LOD, S/N = 3) of 18.4 ± 4.0 pM (equivalent to 29.3 ± 6.4 ng/kg). The LOD is more than 1000-fold lower than the maximum ZEN residue limits set by China (60 μg/kg) and EU (20 μg/kg). The sensor also has extremely high specificity and showed negligible cross-reactivity to other common mycotoxins. In addition, the sensor was able to be regenerated for 28 times, further decreasing its cost. Our sensor holds great potential for practical applications according to its multiple compelling features.

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Section: Introductionmentioning

confidence: 99%

Evanescent Wave Optical-Fiber Aptasensor for Rapid Detection of Zearalenone in Corn with Unprecedented Sensitivity

et al. 2022

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“…Zearalenone (ZEN) is primarily produced by Fusarium graminearum and Fusarium culmorum and predominantly occurs in maize and other grain crops [7,8]. These species of Fusarium are generally found on plants primarily grown in temperate regions and contaminate foods of both plant and animal origin [1].…”

Section: Introductionmentioning

confidence: 99%

Occurrence, Impact on Agriculture, Human Health, and Management Strategies of Zearalenone in Food and Feed: A Review

Mahato

Devi

Pandhi

et al. 2021

Toxins

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Mycotoxins represent an assorted range of secondary fungal metabolites that extensively occur in numerous food and feed ingredients at any stage during pre- and post-harvest conditions. Zearalenone (ZEN), a mycotoxin categorized as a xenoestrogen poses structural similarity with natural estrogens that enables its binding to the estrogen receptors leading to hormonal misbalance and numerous reproductive diseases. ZEN is mainly found in crops belonging to temperate regions, primarily in maize and other cereal crops that form an important part of various food and feed. Because of the significant adverse effects of ZEN on both human and animal, there is an alarming need for effective detection, mitigation, and management strategies to assure food and feed safety and security. The present review tends to provide an updated overview of the different sources, occurrence and biosynthetic mechanisms of ZEN in various food and feed. It also provides insight to its harmful effects on human health and agriculture along with its effective detection, management, and control strategies.

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“…Food safety usually means that the items people consume are nontoxic; that is, they do not cause any harm to the body after consumption, and they provide the body with the nutrients it needs [ 1 , 2 ]. Food safety is not just something that the producer needs to be aware of since there are multiple components involved in the process of food from production to consumption.…”

Section: Introductionmentioning

confidence: 99%

Application of Intelligent Taste Analysis Based on Random Forest Algorithm in Food Quality Inspection

Zhang

Sun

2022

Computational Intelligence and Neuroscience

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Food safety is a major concern that has an impact on the national economy and people’s lives. The food industry has grown in quality and innovation in tandem with the rapid development of the economy and society. The emergence of new food technologies, as well as changes in dietary habits, has increased public concern about food safety. With the emergence of various counterfeit and substandard products, food quality and safety testing have become even more important. Traditional testing methods rely on sensory analysis and physical and chemical analysis. This approach is subjective and poorly adapted to the general public. It requires a high level of technical operation and is difficult to carry out on a large scale. To address this situation, this paper proposes an intelligent approach to food safety quality testing. The core idea is, first, to use sensors to collect data on the various components of the sample to be tested. Second, the random forest (RF) model used in this paper is trained. Third, the trained model is used to classify and identify the test samples. Based on the classification results, a conclusion is drawn as to whether the food product is a variant or a counterfeit. The advantage of this study is that the training model used is a weighted RF algorithm based on mutual information. The correlation between any two decision trees is calculated using mutual information, and for the more correlated decision trees, only the one with the highest evaluation accuracy is retained to form a new RF, and the evaluation accuracy is converted into voting weights, resulting in an RF model with less redundancy and higher evaluation accuracy. The experimental results show that the method used in this paper can successfully identify spoiled or counterfeit products and has good practicality.

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Detection Strategies of Zearalenone for Food Safety: A Review

Cited by 37 publications

References 183 publications

Evanescent Wave Optical-Fiber Aptasensor for Rapid Detection of Zearalenone in Corn with Unprecedented Sensitivity

Evanescent Wave Optical-Fiber Aptasensor for Rapid Detection of Zearalenone in Corn with Unprecedented Sensitivity

Occurrence, Impact on Agriculture, Human Health, and Management Strategies of Zearalenone in Food and Feed: A Review

Application of Intelligent Taste Analysis Based on Random Forest Algorithm in Food Quality Inspection

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