不同挤压参数对大豆粕蛋白质NSI的影响

不同挤压条件会对粕蛋白质溶解性有很大影响,本文系统研究了一种新型预处理工艺中5种挤压参数δ段长度、模孔长度、挤压温度、螺杆转速、物料含水量变化对粕蛋白质NSI的影响规律,并从中优化出最佳挤压参数。     

环介导等温核酸扩增技术快速检测产毒亚历山大藻

DNA环介导等温扩增(LAMP)方法是一种新型的核酸扩增技术,该方法是在等温的条件下进行的,具有反应时间短、灵敏度高、特异性强的特点.本文以产麻痹性贝毒(PSP)的亚历山大藻为研究对象,采用简易法提取DNA模板,设计特异性LAMP引物,利用LAMP技术进行产毒藻种的快速检测,同时,着重对LAMP技术与PCR技术在检测微小亚历山大藻细胞的灵敏度方面做了比较.向LAMP终产物中加入SYBR Green I 染料后可直接用肉眼观察结果而不需要通过凝胶电泳来观察.结果表明,LAMP技术在恒温65℃,1h内就可以检测到产毒藻种;LAMP技术检测微小亚历山大藻的最低检测限为200个/ml,而PCR技术的最低检测限为1000个/ml,LAMP扩增方法比PCR扩增方法的程序简单、反应时间短、灵敏度高;LAMP技术不需要精密的温度循环装置,有恒温加热设备就可以满足检测条件,可用于野外检测.     

Evaluation of hybrid pressure-driven and osmotically-driven membrane process for non-thermal production of apple juice concentrate

This study investigates the potential of hybrid membrane processes including microfiltration (MF), ultrafiltration (UF) and forward osmosis (FO) for non-thermal concentration of apple juice. The process performance and characteristics (physicochemical properties, nutritional and aroma components and microbiological quality) of apple juice were studied. The clarity of apple juice was significantly promoted as pore size of membrane reduced. MF and UF can also ensure microbiological safety in pre-treated apple juice. According to its efficiency of filtration as well as performance of simultaneous clarification and cold-sterilization, 0.22 μm MF membrane was identified as the optimal membrane for the pre-treatment. The pre-treated apple juice can be concentrated up to 65°Brix by subsequent single stage FO. FO retained nutritional and volatile compounds of apple juice while significant reductions were found in the juice concentrated by vacuum evaporation. Hybrid MF-FO can be a promising non-thermal technology to produce apple juice concentrate with high quality.     

板栗加工副产物抑菌作用的研究进展

板栗加工过程易产生诸如板栗壳、板栗花、板栗总苞、板栗 次果及次品等副产物,大量资源常被当作废弃物丢弃,未得到有效利用, 开发的产品较少,造成资源浪费。近年来,有关板栗加工副产物的研究得 到关注,板栗中存在的多种天然活性成分相继被报道,其中具有抑菌作用 的成分占比较高。因此,该研究综述了板栗加工副产物含有的抑菌成分 及其作用机理,以期为板栗加工副产物在抑菌材料方面的应用提供一定 的参考。     

Recent Advances in Methods and Techniques for Freshness Quality Determination and Evaluation of Fish and Fish Fillets: A Review

The freshness quality of fish plays an important role in human health and the acceptance of consumers as well as in international fishery trade. Recently, with food safety becoming a critical issue of great concern in the world, determination and evaluation of fish freshness is much more significant in research and development. This review renovates and concentrates recent advances of evaluating methods for fish freshness as affected by preharvest and postharvest factors and highlights the determination methods for fish freshness including sensory evaluation, microbial inspection, chemical measurements of moisture content, volatile compounds, protein changes, lipid oxidation, and adenosine triphosphate (ATP) decomposition (K value), physical measurements, and foreign material contamination detection. Moreover, the advantages and disadvantages of these methods and techniques are compared and discussed and some viewpoints about the current work and future trends are also presented.     

IDENTIFICATION OF PROCYANIDIN A2 AS POLYPHENOL OXIDASE SUBSTRATE IN PERICARP TISSUES OF LITCHI FRUIT

Postharvest browning of litchi fruit results in short shelf life and reduced commercial value. Experiments were conducted to separate, purify and identify polyphenol oxidase (PPO ) substrates that cause litchi fruit to brown. PPO and its substrate were extracted from the pericarp tissues of litchi fruit. The litchi PPO substrate was purified using polyamide column, silica gel column and Sephadex LH‐20 column chromatography. The browning substrate was selected by a 0.5% FeCl₃ solution and then identified using a partially purified litchi PPO. Analyses of ultraviolet spectrometry, nuclear magnetic resonance and electrospray ionization mass spectrometry indicated that the PPO substrate was procyanidin A2. The substrate can be oxidized to ο‐quinones by litchi PPO and then form brown‐colored by‐products, resulting in pericarp browning of harvested litchi fruit.     

芝麻素及其衍生物与DNA相互作用的研究

在pH=7.4的生理条件下,采用紫外吸收光谱法、圆二色(CD)光谱法、DNA热变性法及粘度法对芝麻提取物芝麻酚(SL)、芝麻素(SN)及细辛脂素(AN)与小牛胸腺DNA(ctDNA)的相互作用机理进行初步探讨。研究结果显示,ctDNA能使三种化合物在290 nm附近处的紫外吸收峰产生减色效应,且伴随轻微的红移现象,ctDNA浓度越大,减色效应越明显,SL、SN及AN与ctDNA的结合常数大小分别为2.65×10~4、4.91×10~4、7.33×10~4 L/mol;三种化合物均能使ctDNA的正CD带(276 nm)增加,负CD带(246 nm)发生轻微的减小,且浓度越大,谱带变化越明显;三种化合物的存在条件下,ctDNA的熔点分别增加了1.75、2.51和6.63℃,且ctDNA粘度均有所增加。综合光谱学和粘度实验的结果,推断三种化合物与DNA的作用方式均为经典的嵌插方式,且作用强度大小为:ANSNSL。     

Computational study of CO2 methanation over two-dimensional molybdenum carbide catalysts

Two-dimensional molybdenum carbide (2D-Mo₂C) is thought to be promising for catalytic hydrogenation of CO₂ to CH₄, but little is known about its catalytic reaction mechanism. In this work, we investigate the hydrogenation of CO₂ to CH₄ on 2D-Mo₂C using density functional theory. Our calculations show that Mo on the surface can efficiently decompose CO₂ to CO and O, and also H₂ to H. The hydrogenation of CO produces CHO that is readily deoxygenated to CH, and CH is selectively hydrogenated to produce CH₄. Interestingly, the embedded Ir₁ on 2D-Mo₂C can act as a single-atom promoter to improve the performance of CO₂ methanation, while on the other hand maintaining its high selectivity for CH₄. This work provides insight into the mechanism of 2D-Mo₂C-catalyzed CO₂ methanation reactions and suggests a strategy to improve the performance of such catalysts through single-atom promoters.     

Prediction model for methanation reaction conditions based on a state transition simulated annealing algorithm optimized extreme learning machine

Methanation is the core process of synthetic natural gas, the performance of the entire reaction system depends on precise values of the reaction condition parameters. Accurate predictions of the CO conversion rate of the methanation reaction can eliminate time-consuming and complex steps in experiments and speed up the discovery of the best reaction conditions. However, the methanation reaction is an uncertain, highly complex, and highly nonlinear process. Thus, this paper proposes a machine learning prediction model for the methanation reaction to facilitate the subsequent search for optimal reaction conditions. The reaction temperature, pressure, hydrogen–carbon ratio, water vapor content, CO₂ content, and space velocity were selected as the condition variables. The CO conversion rate was the optimization objective. An extreme learning machine (ELM) was selected as a prediction model. Because the input weights and bias matrices of the ELM are randomly generated, an ELM based on a state transition simulated annealing (STASA-ELM) algorithm is proposed. The STASA algorithm was used to optimize the ELM to improve the accuracy and stability of the model. Five additional sets of experimental data were designed for the experiment, and the error between the experimental and predicted values was small. Thus, the STASA-ELM algorithm can accurately predict the conversion of CO for different values of reaction conditions.     

Influence of natural fillers on the properties of starch-based biocomposite films

Starch microparticles (SMs) were prepared by delivering ethanol into starch solution. Chemically modified starch microparticles (CMSs) by reaction with malic acid were obtained and incorporated within glycerol plasticized-corn starch (GCS) matrix, composites being further prepared by the casting process. Three natural fillers (NFs) – keratin, lignin, and spruce cellulose – were incorporated within the CMS–GCS matrix. The surface properties and water sorption, as well as mechanical and thermal properties of chemically modified starch/plasticized starch/natural filler (CMS/S/NF) films were investigated. The water resistance and thermal stability were improved through addition of NF. Higher tensile strength and lower elongation capacity were also observed.