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In recent years, the antibacterial activity of plant essential oils has been extensively researched and applied in the food, drug, and cosmetics industries. Extensive research has discovered that plant essential oils can inhibit microbial growth in various ways such as disrupting the bacterial cell membrane, interfering with cellular metabolic activities, and influencing the synthesis of bacterial nucleic acids and proteins. Furthermore, plant essential oils exhibit a complex composition, with multiple target sites and broad-spectrum antibacterial properties. With the continuous advancement of research, omics technologies have made it possible to dissect the antibacterial mechanisms of plant essential oils. This article starts with an introduction to the classification and antibacterial activities of plant essential oils, and then summarizes recent progress in research on the antibacterial mechanisms of plant essential oils from the aspects of transcriptomics, proteomics, metabolomics, and multi-omics. Finally, this review provides an outlook on the future of this field, aiming to provide a theoretical foundation for understanding the multi-target antibacterial mechanisms of plant essential oils and for their application in the food industry.

The contamination status of cold resistant Morganella psychrotolerans in commercially available aquatic products was investigated, and the generation of histamine by its isolates was explored. A total of 100 samples of commercially available fish from Guangdong province were surveyed. The results revealed a widespread distribution of M. psychrotolerans with a contamination rate of 34%. The contamination rates for red-fleshed and white-fleshed fish were 35.9% and 27.3%, respectively. Specifically, the mackerel samples had a contamination rate of 38.24%, and the tuna samples had a contamination rate of 29.41% among all positive samples. Sixteen strains of M. psychrotolerans were isolated from the positive samples, and all isolates produced histamine levels exceeding 1 000 mg/L after 48 h incubation at 20 ℃. Notably, isolate 1 showed higher histamine-producing capacity than the type strain. Although the two strains exhibited no significant differences in growth curves at varying temperatures (4 and 20 ℃), their histamine-producing capacities were different. After 10 days of culture at 4 ℃, histamine production was higher by isolate 1 than the type strain, while the opposite result was observed after 60 h of culture at 20 ℃. These findings offer crucial theoretical support for addressing the problem of histamine accumulation in cold chain aquatic products.

In this study, the effect of fermentation with Bacillus subtilis B-2, a salt-tolerant strain capable of producing high yield of protease, on the microbial community and the contents of biogenic amines and amino acid nitrogen (AAN) during the fermentation of low-salt fish sauce. The results of high-throughput 16S rRNA gene sequencing showed that fermentation with B-2 reduced the richness and evenness of the microbial community, Bacillus was dominant throughout the fermentation process and the abundance of spoilage microorganisms significantly decreased. B-2 significantly inhibited the formation of histamine, putrescine, cadaverine, and tyramine, and their contents at the end of fermentation decreased by 25.9%, 35.6%, 23.6% and 9.3%, respectively, compared with those in naturally fermented fish sauce. The content of AAN in low-salt fish sauce was significantly improved by inoculated fermentation, reaching 1.23 g/100 mL after 15 days of fermentation, which was significantly higher than that of naturally fermented fish sauce (0.79 g/100 mL). The correlation network map showed that the decrease in the abundance of Brevibacterium, Dietzia, Paracoccus, Aequorivita, and Brachybacterium was the major reason for the decrease in microbial diversity at the late stage of fermentation of naturally fermented fish sauce. The abundance of Stenotrophomonas showed a significantly positive correlation with the contents of many biogenic amines in naturally fermented and B-2 fermented fish sauce, suggesting its important role in the formation of biogenic amines in low-salt fish sauce. Comparative analysis of the microbial community and quality attributes at the end of fermentation showed that the metabolism of B-2 was the main reason for the decrease in the species and abundance of spoilage microorganisms, the increase in AAN content, and the decrease in the contents of key biogenic amines. B. subtilis B-2 is expected to be developed as a special fermentation starter for fish sauce to improve the quality and safety of rapidly fermented low-salt fish sauce.

AbstractCellulose nanofibers (CNFs) are the newly introduced plant-based materials in the construction industry to ensure sustainable development. The use of artificial intelligence (AI) techniques especially machine learning (ML) models has assisted to economized civil engineering. This research aims to determine the compressive strength of cellulose nanofibers reinforced concrete by using supervised regression machine learning techniques for analysis before adopting to utilize. To achieve this task, the machine learning models: Random Forest (RF), Linear Regression (LR), Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), Ada Boosting Regressor (ABR), K-Neighbor Regressor (KNN), Bagging Regressor (BR), XG Boost Regressor (XGBR), Decision Tree (DT), and Pruned Decision Tree (PDT) were implemented. An experimental-based dataset containing 695 data points was prepared and split into two categories (Training dataset = 70%, Testing dataset = 30%) for the evolution of ML models. There were seven independent variables: cement (kg/m3), water (kg/m3), CNFs (kg/m3), superplasticizer (kg/m3), fine aggregate (kg/m3), coarse aggregate (kg/m3), and age (Day) variables as an input and one dependent variable: compressive strength fc of CNFs reinforced concrete (MPa) as an output. The following metrics were employed to gauge the ability of the model: R2, MAPE, MAE, MSE, and RMSE. The findings specified that seven out of ten models (RF, BR, XGBR, DT, GBR, ABR, and KNN) to predict the compressive strength of CNFs concrete had a firm capability (R2 >0.72, MAPE ≤ 0.1, and MAE ≤ 5) confirming to the standard of R2 value greater than 0.60 and metrics values very less, close to one. According to the sensitivity analysis of Random Forest model, water and cement were the factors with the biggest effects on the prediction of CNFs reinforced concrete, while the smallest effecting variable was coarse aggregate. It was concluded that the RF, BR, and DT were the premier predicting models.