Comparative Analysis of Texture Characteristics, Sensory Properties, and Volatile Components in Four Types of Marinated Tofu.

In this study, three different brands of commercially available marinated tofu were analyzed and compared with homemade products to explore the effect of key flavor substances on their sensory quality, sensory properties, texture characteristics, and volatile components. The texture characteristics and flavor substances of the three brands of commercially available marinated tofu were significantly different from those of homemade products. A total of 64 volatile components were identified by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), mainly including 11 hydrocarbons, 11 alcohols, 10 ketones, 15 aldehydes, 4 esters, 1 acid, and 12 other volatile substances. Among these, nine key flavor compounds (ROAV > 1, VIP > 1) were identified using the relative odor activity value (ROAV) combined with a partial least squares discriminant analysis (PLS-DA) and variable importance in projection, including α-Pinene, ß-Myrcene, α-Phellandrene, 1-Penten-3-one, Butanal, 3-Methyl butanal, acetic acid ethyl ester, 1,8-Cineol, and 2-Pentyl furan. The correlation heatmap showed that sensory evaluation was positively correlated with hardness, gumminess, chewiness, and springiness while negatively correlated with 2-Pentyl furan, α-Pinene, resilience, α-Phellandrene, 1-Penten-3-one, acetic acid ethyl ester, and 1,8-Cineol. Overall, this study provides a theoretical reference for developing new instant marinated tofu snacks.

Transcriptome analysis reveals the molecular mechanism of cinnamaldehyde against Bacillus cereus spores in ready-to-eat beef.

The purpose of this study was to investigate the antibacterial effect and mechanism of cinnamaldehyde on Bacillus cereus spores in ready-to-eat beef. The colour difference and texture of the ready-to-eat beef supplemented with cinnamaldehyde did not differ greatly from the colour and texture of the blank beef. However, cinnamaldehyde has an effective antibacterial effect on the total number of bacterial colonies and B. cereus spores in ready-to-eat beef. Transmission electron microscopy (TEM) analysis revealed that the cell membrane of B. cereus was disrupted by cinnamaldehyde, leading to leakage of intracellular components. Transcriptome sequencing (RNA-seq) indicated that the B. cereus spore resistance regulation system (sigB, sigW, rsbW, rsbV, yfkM and yflT) and phosphoenolpyruvate phosphotransferase system (PTS) (ptsH, ptsI and ptsG) respond positively to cinnamaldehyde in an adverse environment. Intracellular disorders due to damage to the cell membrane involve some transporters (copA, opuBA and opuD) and some oxidative stress systems (ywrO, scdA and katE) in the regulation of the body. However, downregulation of K+ transport channels (kdpD and kdpB), osmotic pressure regulation (opuE) and some oxidative stress (norR and srrA)-related genes may accelerate spore apoptosis. In addition, cinnamaldehyde also effectively inhibits the spore germination-related genes (smc, mreB and gerE). This study provides new insights into the molecular mechanism of the antibacterial effect of cinnamaldehyde on B. cereus spores in ready-to-eat beef.

Comparative Transcriptomic Analysis of Staphylococcus aureus Reveals the Genes Involved in Survival at Low Temperature.

In food processing, the temperature is usually reduced to limit bacterial reproduction and maintain food safety. However, Staphylococcus aureus can adapt to low temperatures by controlling gene expression and protein activity, although its survival strategies normally vary between different strains. The present study investigated the molecular mechanisms of S. aureus with different survival strategies in response to low temperatures (4 °C). The survival curve showed that strain BA-26 was inactivated by 6.0 logCFU/mL after 4 weeks of low-temperature treatment, while strain BB-11 only decreased by 1.8 logCFU/mL. Intracellular nucleic acid leakage, transmission electron microscopy, and confocal laser scanning microscopy analyses revealed better cell membrane integrity of strain BB-11 than that of strain BA-26 after low-temperature treatment. Regarding oxidative stress, the superoxide dismutase activity and the reduced glutathione content in BB-11 were higher than those in BA-26; thus, BB-11 contained less malondialdehyde than BA-26. RNA-seq showed a significantly upregulated expression of the fatty acid biosynthesis in membrane gene (fabG) in BB-11 compared with BA-26 because of the damaged cell membrane. Then, catalase (katA), reduced glutathione (grxC), and peroxidase (ahpC) were found to be significantly upregulated in BB-11, leading to an increase in the oxidative stress response, but BA-26-related genes were downregulated. NADH dehydrogenase (nadE) and α-glucosidase (malA) were upregulated in the cold-tolerant strain BB-11 but were downregulated in the cold-sensitive strain BA-26, suggesting that energy metabolism might play a role in S. aureus under low-temperature stress. Furthermore, defense mechanisms, such as those involving asp23, greA, and yafY, played a pivotal role in the response of BB-11 to stress. The study provided a new perspective for understanding the survival mechanism of S. aureus at low temperatures.

Forecasting fuel combustion-related CO2 emissions by a novel continuous fractional nonlinear grey Bernoulli model with grey wolf optimizer.

Foresight of CO2 emissions from fuel combustion is essential for policy-makers to identify ready targets for effective reduction plans and to further improve energy policies and plans. A new method for forecasting the future development of China's CO2 emissions from fuel combustion is proposed in this paper by using grey forecasting theory. Although the existing fractional nonlinear grey Bernoulli model (denoted as FNGBM(1,1)) has been theoretically proven to enhance the adaptability to diverse sequences, its fixed integer-order differential derivative still impairs the performance to some extent. To this end, a varying-order differential derivative is introduced into the existing differential equation to enable a more flexible structure, thus improving the prediction ability of FNGBM(1,1). Specifically, because of the advantages of conformable fractional accumulation, the traditional differential derivative is first replaced by the conformable fractional differential derivative. As a consequence, the continuous conformable fractional nonlinear grey Bernoulli model (hereinafter referred to as CCFNGBM(1,1)) is proposed. To further increase the validity of the model, a metaheuristic algorithm, namely Grey Wolf Optimizer (GWO), is then applied to search for the optimal emerging coefficients for the proposed model. Two real examples and China's CO2 emissions from fuel combustion are considered to verify the effectiveness of the newly proposed model, the experimental results show that the newly proposed model outperforms other benchmark models in terms of forecasting accuracy. The proposed model is finally employed to forecast the future China's CO2 emissions from fuel combustion by 2023, accounting for 10,039.80 million tons. Based on the forecasts, several policy suggestions are provided to curb CO2 emissions.