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人工神经网络和响应面法在优化 Actinomadura keratinilytica 菌株 Cpt29 生产环保且耐洗涤剂脂肪酶中的应用

Artificial Neural Networks and Response Surface Methodology Approach for Optimization of an Eco-Friendly and Detergent-Stable Lipase Production from Actinomadura Keratinilytica Strain Cpt29.

出版信息

Acta Chim Slov. 2021 Sep;68(3):575-586.

PMID:34897543
Abstract

This work mainly focused on the production of an efficient, economical, and eco-friendly lipase (AKL29) from Actinomadura keratinilytica strain Cpt29 isolated from poultry compost in north east of Algeria, for use in detergent industries. AKL29 shows a significant lipase activity (45 U/mL) towards hydrolyzed triacylglycerols, indicating that it is a true lipase. For maximum lipase production the modeling and optimization of potential culture parameters such as incubation temperature, cultivation time, and Tween 80 (v/v) were built using RSM and ANN approaches. The results show that both the two models provided good quality predictions, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities. A 4.1-fold increase in lipase production was recorded under the following optimal condition: incubation temperature (37.9 °C), cultivation time (111 h), and Tween 80 (3.27%, v/v). Furthermore, the partially purified lipase showed good stability, high compatibility, and significant wash performance with various commercial laundry detergents, making this novel lipase a promising potential candidate for detergent industries.

摘要

这项工作主要集中在生产一种高效、经济、环保的脂肪酶(AKL29),该酶来自于从阿尔及利亚东北部家禽堆肥中分离的 Actinomadura keratinilytica 菌株 Cpt29,用于洗涤剂工业。AKL29 对水解三酰基甘油表现出显著的脂肪酶活性(45 U/mL),表明它是一种真正的脂肪酶。为了获得最大的脂肪酶产量,使用 RSM 和 ANN 方法对潜在的培养参数(如培养温度、培养时间和吐温 80(v/v))进行建模和优化。结果表明,两种模型都提供了良好的预测质量,但 ANN 在数据拟合和估计能力方面明显优于 RSM。在以下最佳条件下,脂肪酶产量提高了 4.1 倍:培养温度(37.9°C)、培养时间(111 小时)和吐温 80(3.27%,v/v)。此外,部分纯化的脂肪酶具有良好的稳定性、高兼容性和对各种商业洗衣洗涤剂的显著洗涤性能,使这种新型脂肪酶成为洗涤剂工业有前途的潜在候选者。

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