PDF(Pubmed)
Abstract：
The laboratory-scale (in-vitro) microbial fermentation based on screening of process parameters (factors) and statistical validation of parameters (responses) using regression analysis. The recent trends have shifted from full factorial design towards more complex response surface methodology designs such as Box-Behnken design, Central Composite design. Apart from the optimisation methodologies, the listed designs are not flexible enough in deducing properties of parameters in terms of class variables. Machine learning algorithms have unique visualisations for the dataset presented with appropriate learning algorithms. The classification algorithms cannot be applied on all datasets and selection of classifier is essential in this regard. To resolve this issue, factor-response relationship needs to be evaluated as dataset and subsequent preprocessing could lead to appropriate results. The aim of the current study was to investigate the data-mining accuracy on the dataset developed using in-vitro pyruvate production using organic sources for the first time. The attributes were subjected to comparative classification on various classifiers and based on accuracy, multilayer perceptron (neural network algorithm) was selected as classifier. As per the results, the model showed significant results for prediction of classes and a good fit. The learning curve developed also showed the datasets converging and were linearly separable.
摘要：
基于工艺参数(因素)的筛选和使用回归分析的参数(响应)的统计验证的实验室规模(体外)微生物发酵。最近的趋势已经从全因子设计转向更复杂的响应面方法设计，如Box-Behnken设计，中央复合材料设计。除了优化方法之外，列出的设计在根据类变量推导参数属性方面不够灵活。机器学习算法对于通过适当的学习算法呈现的数据集具有独特的可视化。分类算法不能应用于所有数据集，在这方面，分类器的选择至关重要。要解决此问题，因子-反应关系需要作为数据集进行评估，随后的预处理可能会导致适当的结果。当前研究的目的是首次研究使用有机来源的体外丙酮酸生产开发的数据集的数据挖掘准确性。属性在各种分类器上进行比较分类，并基于准确性，选择多层感知器(神经网络算法)作为分类器。根据结果，该模型对类别的预测结果显著，拟合良好。所开发的学习曲线还显示数据集收敛并且是线性可分离的。
