ArticleScientific reports2023
Application of a generative adversarial network for multi-featured fermentation data synthesis and artificial neural network (ANN) modeling of bitter gourd-grape beverage production.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Toward engineered microbial chassis for growth-production balancing.Synthetic and systems biotechnology · 2027Review
- Microbial cell factories for gamma-linolenic acid biosynthesis: metabolic engineering, downstream processing, and future perspectives.Bioresources and bioprocessing · 2026Review
- Analysis of lipid-protein interactive oxidation and underlying pathways in aquatic products via multi-omics and machine learning: a case study of air-dried hairtail.Food chemistry: X · 2026Article
- Machine Learning-Enabled Rapid Assessment of Plant-Based Protein Digestibility Through Physicochemical Profiles.Foods (Basel, Switzerland) · 2025Article
- Precision to plate: AI-driven innovations in fermentation and hyper-personalized diets.Frontiers in nutrition · 2025Review
- Article
- Utilizing Artificial Neural Networks for Establishing Hearing-Loss Predicting Models Based on a Longitudinal Dataset and Their Implications for Managing the Hearing Conservation Program.Safety and health at work · 2024Article
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7 authors.
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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).
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