Optimizing the degradation of aflatoxin B1 in corn by Trametes versicolor and improving the nutritional composition of corn.

BACKGROUND: Corn, being an important grain, is prone to contamination by aflatoxin B1 (AFB1 ), and AFB1 -contaminated corn severely endangers the health of humans and livestock. Trametes versicolor, a fungus that can grow in corn, possesses the ability to directly degrade AFB1 through its laccase. This study aimed to optimize the fermentation conditions for T. versicolor to degrade AFB1 in corn and investigate the effect of T. versicolor fermentation on the nutritional composition of corn. AFB1 -contaminated corn was used as the culture substrate for T. versicolor. A combination of single-factor experiments and response surface methodology was employed to identify the optimal conditions of AFB1 degradation. RESULTS: The optimal conditions of AFB1 degradation were as follows: 9 days of fermentation, a fermentation temperature of 26.7 °C, a moisture content of 70.5% and an inoculation amount of 4.9 mL (containing 51.99 mg of T. versicolor mycelia). With the optimal conditions, the degradation rate of AFB1 in corn could reach 93.01%, and the dry basis content of protein and dietary fiber in the fermented corn was significantly increased. More importantly, the lysine content in the fermented corn was also significantly increased. CONCLUSION: This is the first report that direct fermentation of AFB1 -contaminated corn by T. versicolor not only efficiently degrades AFB1 but also improves the nutritional composition of corn. These findings suggest that the fermentation of corn by T. versicolor is a promising, environmentally friendly and efficient approach to degrade AFB1 and improve the nutritional value of corn. © 2023 Society of Chemical Industry.

A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets.

In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity.

Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking.

The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gaussian mixture implementations. The results show that the proposed approach can improve the accuracy of target state estimation, as well as the robustness.