Downstream Processing Laboratory, Department of Biotechnology, Kumaraguru College of Technology, Coimbatore, Tamilnadu, India.
Fermentation Bioengineering Laboratory, Department of Biotechnology, School of Bioengineering, SRM University, Kattankulathur, Chennai, Tamilnadu, India.
Int J Biol Macromol. 2019 Mar 1;124:750-758. doi: 10.1016/j.ijbiomac.2018.11.036. Epub 2018 Nov 8.
In this work, Response Surface Methodology (RSM) and Artificial Neural Network coupled with genetic algorithm (ANN-GA) have been used to develop a model and optimise the conditions for the extraction of pectin from sunflower heads. Input parameters were extraction time (10-20 min), temperature (40-60 °C), frequency (30-60 Hz), solid/liquid ratio (S/L) (1:20-1:40 g/mL) while pectin yield (PY%) was the output. Results showed that ANN-GA had a higher prediction efficiency than RSM. Using ANN as the fitness function, a maximum pectin yield of 29.1 ± 0.07% was searched by genetic algorithm at the time of 10 min, temperature of 59.9 °C, frequency of 30 Hz, and solid liquid ratio of 1:29.9 g/mL while the experimental value was found to be 29.5 ± 0.7%. Extracted pectin was characterised by FTIR and C NMR. Thus, ANN coupled GA has proved to be the effective method for the optimization of process parameters for pectin extraction from sunflower heads.
在这项工作中，响应面法（RSM）和人工神经网络与遗传算法（ANN-GA）相结合，用于开发从葵花头中提取果胶的模型并优化提取条件。输入参数为提取时间（10-20 分钟）、温度（40-60°C）、频率（30-60Hz）、固液比（S/L）（1:20-1:40g/mL），而果胶得率（PY%）为输出。结果表明，ANN-GA 的预测效率高于 RSM。使用 ANN 作为适应度函数，遗传算法在 10 分钟、59.9°C 的温度、30Hz 的频率和固液比为 1:29.9g/mL 的条件下搜索到最大果胶得率为 29.1±0.07%，而实验值为 29.5±0.7%。提取的果胶通过傅里叶变换红外光谱（FTIR）和碳核磁共振（C NMR）进行了表征。因此，ANN 与 GA 的结合被证明是优化从葵花头中提取果胶的工艺参数的有效方法。
