酶膜生物反应器制备抗凝血酶蛋清水解物的研究

Use of Artificial Neural Networks for the Optimization of Preparation of Egg-white Hydrolysate with Antithrombin Activity in an Enzymatic Membrane Bioreactor

采用酶膜生物反应器(EMBR)制备抗凝血酶蛋清水解物,并用人工神经网络方法优化工艺。在0.13～0.15MPa条件下,采用L18(37×21)混合水平正交试验考察底物质量分数、酶质量分数、pH值、温度、滤过液流速和水解时间对蛋清水解物的抗凝血酶生物活性的影响,并用多层前馈网络(BP神经网络)对EMBR酶解的过程进行模拟和预测,获得抗凝血酶水解产物的最优制备工艺条件。结果表明,在底物质量分数1%、酶质量分数1%、pH8.0、温度55℃、滤过液流速10mL/min、水解时间4h时,产物的最低抗凝血酶IC50预测值为10.43mg/mL,与实测值仅相差4.03%,说明采用EMBR制备蛋清酶解物的方法可行,并经人工神经网络方法优化得到了抗凝血酶蛋清水解物的最优制备条件。

An enzymatic membrane reactor designed based on the combination of membrane filtration and Protease N Amanocatalyzed hydrolysis was used to prepare egg-white hydrolysate （EWH） with antithrombin activity. A L18（37×21） mixed-level orthogonal array design was used to investigate the effects of substrate concentration, enzyme dosage, pH, temperature, filtration flow rate and length of hydrolysis on the antithrombin activity of EWH. This was followed by the stimulation and predication of EW hydrolysis using back propagation （BP） neural networks to obtain the optimal values of the above parameters. The results showed that the predicted value of IC50 of EWH obtained under the following optimized conditions： substrate concentration 1%, enzyme dosage 1%, filtration flow rate 10 mL/min, and length of hydrolysis 4 h was 10.43 mg/mL, 4.03% lower then the actual value. This demonstrates good reliability of BP neural networks in optimizing egg white protein hydrolysis.