Application of multifractal wavelet analysis to spontaneous fermentation processes

TL;DR

This paper introduces a novel method combining neural networks and multifractal wavelet analysis to monitor spontaneous fermentation processes using only redox potential data, providing detailed insights into microbial activity.

Contribution

It presents a new two-step approach that estimates biomass concentrations from redox potential and applies multifractal wavelet analysis for process monitoring.

Findings

Redox potential effectively indicates microbial activity.

The method accurately estimates biomass concentrations.

Multifractal analysis reveals detailed fermentation dynamics.

Abstract

An algorithm is presented here to get more detailed information, of mixed culture type, based exclusively on the biomass concentration data for fermentation processes. The analysis is performed with only the on-line measurements of the redox potential being available. It is a two-step procedure which includes an Artificial Neural Network (ANN) that relates the redox potential to the biomass concentrations in the first step. Next, a multifractal wavelet analysis is performed using the biomass estimates of the process. In this context, our results show that the redox potential is a valuable indicator of microorganism metabolic activity during the spontaneous fermentation. In this paper, the detailed design of the multifractal wavelet analysis is presented, as well as its direct experimental application at the laboratory level