IMS Engineering College, Ghaziabad, Uttar Pradesh, India.
Kumaun University, Nainital, Uttarakhand, India.
PLoS One. 2024 Jul 11;19(7):e0306987. doi: 10.1371/journal.pone.0306987. eCollection 2024.
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.
基于筛选工艺参数（因子）和使用回归分析对参数（响应）进行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、中心复合设计。除了优化方法外，所列设计在根据类别变量推断参数性质方面不够灵活。机器学习算法具有针对呈现的数据集的独特可视化效果，并使用适当的学习算法。分类算法不能应用于所有数据集，因此在这方面选择分类器至关重要。为了解决这个问题，需要评估数据集的因子-响应关系，随后进行适当的预处理可以得出合适的结果。本研究的目的是首次调查使用有机来源在体外生产丙酮酸的数据集上的数据挖掘准确性。对各种分类器进行了属性的比较分类，并根据准确性选择了多层感知器（神经网络算法）作为分类器。根据结果，该模型显示出对分类预测的显著结果和良好的拟合度。开发的学习曲线也显示了数据集的收敛和线性可分性。
