水溶性大豆多糖对啤酒酵母悬浊液的絮凝作用

Flocculation Effect of Water Soluble Soybean Polysaccharides on Brewer Yeast Suspension

采用酒石酸、六偏磷酸钠和氢氧化钠浸提豆渣制得大豆多糖SSPS P1、SSPS P2和SSPS P3,分别考察金属阳离子(Al3+、Fe3+、Fe2+、Mg2+、Ca2+、K+、Na+)、多糖浓度、p H值以及温度对SSPS絮凝啤酒酵母悬浊液的影响,并进行响应面试验设计,优化SSPS P1絮凝酵母悬浊液的条件,得到相应的数学回归模型.单因素研究结果表明:3种水溶性大豆多糖中SSPS P1絮凝酵母悬浊液的效果最好;多数金属离子对于3种SSPS的絮凝性起促进作用,其中Fe3+协同絮凝效果显著,Fe3+浓度在0.025 mmol/L时效果最好;SSPS P1浓度和温度也会影响絮凝啤酒酵母悬浊液的效果,SSPS P1质量浓度和温度分别为10 mg/L和30℃时效果较好;强碱性环境有利于SSPS P1絮凝酵母悬浊液.分析数学回归模型得到的絮凝最佳条件为SSPS P1质量浓度11.56 mg/L、Fe3+浓度0.26 mmo L/L、p H值9.05、温度30.68℃,絮凝率的预测值与实验值非常接近,回归模型能够反映各因素对啤酒酵母悬浊液絮凝效果的影响.

Three kinds of water soluble soybean polysaccharides, namely, SSPS P1, SSPS P2 and SSPS P3, were extracted from soy bean dregs by using tartaric acid, sodium hexametaphosphate and sodium hydroxide. Then, the flocculation effects of the SSPS on the brewer yeast suspension with different metal cations （ A13 ＋ , Fe3 ＋ , Fe2 ＋ , Mg2＋, Ca2＋, K＋ and Na＋）, polysaccharide concentrations, pH values and temperatures were respectively in- vestigated. Moreover, the response surface experiment design was performed to optimize the flocculation condi- tions, and a corresponding mathematical regression model was constructed. Single factor research results show that （ 1 ） SSPS P1 has the best effect in flocculating the brewer yeast suspension ; （2） most of the metal cations increase the flocculation effects, among which Fe3 ＋ shows the best performance; （3） when the Fe3 ＋ concentration reaches 0. 025 mmol/L, the best flocculation effect can be achieved; （4） the flocculation effects can also be influenced by the SSPS P1 concentration and the temperature, and the effects are better at 10mg/L and 30℃ ; and （5） a strong alkaline environment is helpful in adopting SSPS PI to flocculate the brewer yeast suspension. The analytical results based on the constructed model indicate that the optimal flocculating condition is a SSPS PI mass concentration of 11.56 mg/L, a Fe3 ＋ concentration of 0.26 mmol/L, a pH value of 9.05 and a temperature of 30. 68℃, and the experimental value of the flocculating rate under the optimal condition is very close to the predicted one, which means that the constructed model can reflect the influence of various factors on the flocculation effect.