# Evaluating classification tools for the prediction of in-vitro microbial pyruvate yield from organic carbon sources

## TL;DR

This study evaluates machine learning tools to predict microbial pyruvate yield from organic carbon sources in laboratory settings.

## Contribution

The novelty lies in applying data mining and neural networks to classify in-vitro pyruvate production from organic sources for the first time.

## Key findings

- Multilayer perceptron (neural network) achieved significant accuracy in predicting pyruvate yield classes.
- The dataset was found to be linearly separable, with learning curves showing convergence.
- Comparative analysis showed the selected classifier provided a good fit for the data.

## 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.