利用响应面法（RSM）和遗传算法对向日葵头中果胶的提取及其人工神经网络（ANN）模型进行研究。
Pectin extraction from Helianthus annuus (sunflower) heads using RSM and ANN modelling by a genetic algorithm approach.
机构信息
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.
出版信息
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。
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