Effects of temperature, ammonium and glucose concentrations on yeast growth in a model wine system

In enology, alcoholic fermentation is a complex process involving several mechanisms. Slow and incomplete alcoholic fermentation is a chronic problem for the wine industry and factors leading to sluggish and stuck fermentations have been extensively studied and reviewed. The most studied cause of sluggish and stuck fermentation is the nitrogen content limitation. Nevertheless, other factors, such as temperature of fermentation and sugar concentration can affect the growth of yeasts. In this study we modelled the yeast growth‐cycle in wine model system as a function of temperature, sugar and ammonium concentrations; the individual effects and the interaction of these factors were analysed by means of a quadratic response surface methodology. Cell concentrations and weight loss were monitored in the whole wine fermentation process. The results of central composite design show that lower is the availability of nitrogen, higher is the cell growth rate; moreover, initial nitrogen concentration also influences survival time of Saccharomyces cerevisiae.     

Bioprocess applications of model-based estimation techniques

The last decade has seen the development of a number of approaches for estimating those variables which are difficult to measure on-line in industrial process situations. Whilst a range of techniques is available, a common element is the use of process knowledge in the form of a system model. In the case of bioprocess systems, although a large range of models has been presented in the literature, their use in estimation schemes on an industrial scale has been limited. A number of reasons can be identified for their low level of utilisation. Of particular significance is the uncertainty which exists in quantifying system performance and the process-model mismatch which inevitably results. The level of ‘pre-defined model’ uncertainty, together with the knowledge gained during the course of the fermentation, serves to dictate estimator structure. The paper considers a range of estimation strategies and contrasts, through industrial applications, their performance characteristics and utility.     

IMP2, a nuclear gene controlling the mitochondrial dependence of galactose, maltose and raffinose utilization in Saccharomyces cerevisiae.

The IMP2 gene of Saccharomyces cerevisiae is involved in the nucleo-mitochondrial control of maltose, galactose and raffinose utilization as shown by the inability of imp2 mutants to grow on these carbon sources in respiratory-deficient conditions or in the presence of ethidium bromide and erythromycin. The negative phenotype cannot be scored in the presence of inhibitors of respiration and oxidative phosphorylation, indicating that the role of the mitochondria in the utilization of the above-mentioned carbon sources in imp2 mutants is not at the energetical level. Mutations in the IMP2 gene also confer many phenotypic alterations in respiratory-sufficient conditions, e.g. leaky phenotype on oxidizable carbon sources, sensitivity to heat shock and sporulation deficiency. The IMP2 gene has been cloned, sequenced and disrupted. The phenotype of null imp2 mutants is indistinguishable from that of the originally isolated mutant.     

抗生素生产过程补料时机的Fuzzy识别与预报

本文就抗生素发酵生产过程,提出了一种补料时机的Fuzzy识别、预报的建模方法,并根据20m~3发酵罐工业数据的分析,选取了以总糖、总糖变化率、氨基氮和氨基氮变化率四个主要参数作为输入集因素,运用本文提出的方法,建立了一个实际的抗生素发酵生产过程补料时机的 Fuzzy 识别、预报模型。经现场调试效果良好,与富有经验的行家相比,准确率达90%以上。     

高功率固体激光器

本文详细讨论了设计高平均功率固体激光器的有关问题,包括激光材料性质、棒状和板条状几何结构,聚光器和光学谐振腔等。分析表明,板条状几何结构固体激光器提供了达到千瓦级功率水平同时又保持良好光束质量的可能性。     

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

A two‐dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5‐aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self‐organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM‐based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. Copyright © 2005 Society of Chemical Industry     

Lactic acid production from dining‐hall food waste by Lactobacillus plantarum using response surface methodology

BACKGROUND: Food waste generally has a high starch content and is rich in nutritional compounds, including lipids and proteins. It therefore represents a potential renewable resource. In this study, dining‐hall food waste was used as a substrate for lactic acid production, and response surface methodology was employed to optimise the fermentation conditions. RESULTS: Lactic acid biosynthesis was significantly affected by the interaction of protease and temperature. Protease, temperature and CaCO₃ had significant linear effects on lactic acid production, while α‐amylase and yeast extract had insignificant effects. The optimal conditions were found to be an α‐amylase activity of 13.86 U g^?1 dried food waste, a protease activity of 2.12 U g^?1 dried food waste, a temperature of 29.31 °C and a CaCO₃ concentration of 62.67 g L^?1, which resulted in a maximum lactic acid concentration of 98.51 g L^?1 (88.75% yield). An increase in inoculum size would be appropriate for accelerating the depletion of initial soluble carbohydrate to enhance the efficiency of α‐amylase in dining‐hall food waste fermentation. CONCLUSION: A suitable regression model for lactic acid production was developed based on the experimental results. Dining‐hall food waste was found to be a good substrate for lactic acid fermentation with high product yield and without nutrient supplementation. Copyright © 2008 Society of Chemical Industry     

Optimal biocompatible solvent design for a two-stage extractive fermentation process with cell recycling

In this study, crisp and fuzzy multiple-goal optimization approaches are respectively introduced to design an optimal biocompatible solvent to a two-stage extractive fermentation with cell recycling for ethanol production. When designing a biocompatible solvent for the extractive fermentation process, many issues, such as extractive efficiency, conversion, amount of solvent utilized and so on, have to be considered. An interactive multiple-goal design procedure is introduced to determine a trade-off result in order to satisfy such contradicted goals. Both approaches could be iterated to solve the interactive multiple-goal design problem in order to yield a trade-off result. However, the crisp optimization design is a tedious task that requires the designer to provide various pairs of the upper bounds for the design problem to obtain the corresponding solution. The fuzzy optimization approach is able to be trade-off several goals simultaneously and to yield the overall satisfactory grade for the product/process design problem.     

Effects of growth with ethanol on fermentation and membrane fluidity of Saccharomyces cerevisiae

Saccharomyces cerevisiae HSc was grown with ethanol at concentrations up to 10% (v/v). The immediate effects of additions of externally added ethanol on CO₂ production and O₂ consumption of washed organisms were studied by stopped-flow membrane inlet quadrupole mass spectrometry. Fermentative activities of organisms grown with ethanol (0–5% v/v) showed similar sensitivities to inhibition by ethanol, whereas those grown with 10% (v/v) ethanol had become protected and were markedly less sensitive. The fluidity of subcellular membrane fractions was measured by determination of the temperature dependence of the rotational order parameter of the spin label 5-doxyl stearic acid (free radical) by electron spin resonance. Mitochondria prepared from yeasts grown with 0, 7 and 9% (v/v) ethanol showed similar overall fluidity, although differences in temperature-dependent behaviour indicate altered lipid composition or lateral phase separations. On the other hand the microsomal fraction from organisms grown with 9% ethanol showed a remarkable increase in fluidity. These data suggest that the protective effects of growth with ethanol near the limit of tolerance on fermentative activities may arise from altered plasma membrane fluidity properties.     

Solid-state NMR as a probe of porous catalysts and catalytic processes

The power of solid-state NMR for the interrogation of porous catalytic materials is illustrated using three examples. First, for the investigation of catalytic processes occurring within the confines of a microporous catalyst NMR is shown to reveal both the details of shape-selectivity and the nature of internal surface species. Second, NMR is shown to be a powerful short-range tool to reveal precise structural information on highly disordered microporous titanosilicates. Despite long-range disorder the short-range order is maintained and can be easily studied. Finally, the same utility of probing short-range chemical phenomena is shown to be crucial for the investigation of novel-ordered-amorphous-mesoporous materials known generically as M41S. This class of material is currently one of the most important with potential catalytic application.