The most important non-starch polysaccharides in barley grain are b-glucan and arabinoxylans. Arabinoxylans are partially water-extractable, high-molecular-weight polymers that contribute to viscosity and membrane filterability. Changes to arabinoxylans content and endo-xylanase activity during germination and kilning were studied. This paper was aimed at etermining the composition of total arabinoxylans and water-extractable arabinoxylans in malt and observing their changes during brewing. The water-extractable and total arabinoxylans contents of six barley malts and wheat malt varied in the range of 0.42-0.98% and 3.84-6.90%, respectively. Their L-arabinose-to-D-xylose ratio ranged between 0.49-0.59 (total arabinoxylans) and 0.60-0.85 (water-extractable arabinoxylans). Contents of arabinoxylans in worts mainly originated from the soluble part of arabinoxylans in malt. The use of rice adjunct decreased arabinoxylans contents of wort and beer. The correlation between arabinoxylans and viscosity was investigated. Results showed that water-extractable arabinoxylans in wort have positive and higher correlation value with wort viscosity than those of b-glucan in wort, and both water-extractable arabinoxylans and b-glucan in beer have significant correlation (p<0.01) with beer viscosity.

A general mathematical model for performance evaluation of acetone-butanol continuous flash extractive fermentation system was formulated in the terms of productivity, energy requirement (energy utilization efficiency) and product purity. The simulation results based on experimental data showed that the most pronounced performance improvement could be achieved by using highly concentrated substrate as feed and the increase in solvent dilution rate could only improve the total productivity at the expense of energy utilization efficiency. A two-vessel partial flash system, with the first vessel of two to three plates and the second vessel as a complete flash vessel, is required to ensure high product purity. This method of mathematical formulation and analysis could be used and extended as a general tool to other bioprocesses equipped with multiple in siturecovery systems.

采用PCR从海栖热袍菌（Thermotoga maritima）克隆出编码极耐热稳定性阿拉伯糖苷酶基因，以pET-20b为表达质粒，与其C 末端6个组氨酸标签序列融合，在大肠杆菌中得到高效表达。基因表达产物通过热处理和亲和层析柱纯化后，酶纯度达电泳均一。纯化重组酶稳定性检测表明，阿拉伯糖苷酶活性最适作用温度和最适作用pH分别为90～95℃和pH5.0～5.5，在pH4.2～8.2之间酶活力稳定，95℃的半衰期为4h；SDS2PAGE测得酶的分子量为56.57kD，与理论推算值相吻合。在所测定的底物中，阿拉伯糖苷酶仅对对硝基苯-阿拉伯呋喃糖苷（pNPAF）有专一性水解作用，其动力学参数Km值为0.18mmolPL，Vmax为139μmolPmin•mg。

The experimental data showed that, in glutamate fermentation with strain Corynebacterium glutamicum S9114, glutamate produced in different ways with different patterns of OUR, CER and lactate accumulation at different DO levels. A metabolic network model combined with the linear programming optimization was thus developed to on-line predict glutamate production under different DO levels, with only OUR and CER be on-line measured. The results indicated the advantages of the metabolic network model over the traditional unstructured models, in terms of easy parameter identification, prediction accuracy, and the universal abilities. The proposed model potentially supplies an alternative way for on-line control and optimization of fermentation processes.

The rice heat blast process is a novel technique for gelatinizing raw starch. It uses heated air to replace steam for processing rice under high temperature fluidization in a short time period. This is a new technique for making rice wine with the characteristics of easy storage and zero water pollution. This study focused on three major performance indexes (starch gelatinization ratio, total fat content, and amino nitrogen content in the roasted rice), which largely affect the performance of the rice heat blast process and the rice wine quality. The relationship between these performance indexes and the corresponding operation variables were modeled by an artificial neural network (ANN) via learning sets of experimental data. Based on the ANN models obtained, genetic algorithms were used to optimize the operating conditions of the rice heat blast process. The results showed the power in determination of optimal operating conditions by the combinational utilization of artificial neural network and genetic algorithms.