Environmental chemical contaminants in food seriously impact human health and food safety. Successful detection methods can effectively monitor the potential risk of emerging chemical contaminants. Among them, molecularly imprinted polymers (MIPs) based on electrochemical biomimetic sensors overcome many drawbacks of conventional detection methods and offer opportunities to detect contaminants with simple equipment in an efficient, sensitive, and low-cost manner. We searched eligible papers through the Web of Science (2000–2022) and PubMed databases. Then, we introduced the sensing mechanism of MIPs, outlined the sample preparation methods, and summarized the MIP characterization and performance. The classification of electrochemistry, as well as its advantages and disadvantages, are also discussed. Furthermore, the representative application of MIP-based electrochemical biomimetic sensors for detecting small molecular chemical contaminants, such as antibiotics, pesticides, toxins, food additives, illegal additions, organic pollutants, and heavy metal ions in food, is demonstrated. Finally, the conclusions and future perspectives are summarized and discussed.

Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside‐the‐box, predictions not found in other state‐of‐the‐art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.

Deoxynivalenol (DON), produced by Fusarium species, is one of the most common trichothecenes detected in cereals pre- and post-harvest, which poses a great threat to the health of livestock and human beings due to its strong toxicity. In this study, we isolated and characterized two DON-degrading bacterial strains, Bacillus sp. HN117 and Bacillus sp. N22. Both strains could degrade DON efficiently in a wide range of temperatures (from 25 °C to 42 °C) and concentrations (from 10 mg/L to 500 mg/L). After optimization of the degradation conditions, 29.0% DON was eliminated by HN117 in 72 h when it was incubated with 1000 mg/L DON; meanwhile, the DON degradation rate of N22 was boosted notably from 7.41% to 21.21% within 120 h at 500 mg/L DON. Degradation products analysis indicated HN117 was able to transform DON into a new isomer M-DOM, the possible structure of which was deduced based on LC-MS and NMR analysis, and N22 could convert DON into potential low-toxic derivatives norDON E and 9-hydroxymethyl DON lactone. These two strains have the potential to be developed as new biodegrading agents to control DON contamination in food and feed industries.

Excessive use of pesticides can cause contamination of the environment and agricultural products that are directly threatening human life and health. Therefore, in the process of food safety supervision, it is crucial to conduct sensitive and rapid detection of pesticide residues. The recognition element is the vital component of sensors and methods for fast testing pesticide residues in food. Improper recognition elements may lead to defects of testing methods, such as poor stability, low sensitivity, high economic costs, and waste of time. We can use the molecular biological technique to address these challenges as a good strategy for recognition element production and modification. Herein, we review the molecular biological methods of five specific recognition elements, including aptamers, genetic engineering antibodies, DNAzymes, genetically engineered enzymes, and whole-cell-based biosensors. In addition, the application of these identification elements combined with biosensor and immunoassay methods in actual detection was also discussed. The purpose of this review was to provide a valuable reference for further development of rapid detection methods for pesticide residues. Graphical Abstract Recognition elements based on the molecular biological technique and their application in pesticide detection.