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- Title
Application of a generative adversarial network for multi-featured fermentation data synthesis and artificial neural network (ANN) modeling of bitter gourd–grape beverage production.
- Authors
Gbashi, Sefater; Maselesele, Tintswalo Lindi; Njobeh, Patrick Berka; Molelekoa, Tumisi Beiri Jeremiah; Oyeyinka, Samson Adeoye; Makhuvele, Rhulani; Adebo, Oluwafemi Ayodeji
- Abstract
Artificial neural networks (ANNs) have in recent times found increasing application in predictive modelling of various food processing operations including fermentation, as they have the ability to learn nonlinear complex relationships in high dimensional datasets, which might otherwise be outside the scope of conventional regression models. Nonetheless, a major limiting factor of ANNs is that they require quite a large amount of training data for better performance. Obtaining such an amount of data from biological processes is usually difficult for many reasons. To resolve this problem, methods are proposed to inflate existing data by artificially synthesizing additional valid data samples. In this paper, we present a generative adversarial network (GAN) able to synthesize an infinite amount of realistic multi-dimensional regression data from limited experimental data (n = 20). Rigorous testing showed that the synthesized data (n = 200) significantly conserved the variances and distribution patterns of the real data. Further, the synthetic data was used to generalize a deep neural network. The model trained on the artificial data showed a lower loss (2.029 ± 0.124) and converged to a solution faster than its counterpart trained on real data (2.1614 ± 0.117).
- Subjects
GENERATIVE adversarial networks; ARTIFICIAL neural networks; MOMORDICA charantia; FERMENTATION; FERMENTED beverages
- Publication
Scientific Reports, 2023, Vol 13, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-023-38322-3