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Gbashi S, Maselesele TL, Njobeh PB, Molelekoa TBJ, Oyeyinka SA, Makhuvele R, Adebo OA. Application of a generative adversarial network for multi-featured fermentation data synthesis and artificial neural network (ANN) modeling of bitter gourd-grape beverage production. Sci Rep 2023; 13:11755. [PMID: 37474706 PMCID: PMC10359352 DOI: 10.1038/s41598-023-38322-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 07/06/2023] [Indexed: 07/22/2023] Open
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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Affiliation(s)
- Sefater Gbashi
- Department of Biotechnology and Food Technology, Faculty of Science, Doornfontein Campus, University of Johannesburg, P.O Box 17011, Johannesburg, 2028, Gauteng, South Africa.
| | - Tintswalo Lindi Maselesele
- Food Innovation Research Group, Department of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, P.O Box 17011, Johannesburg, 2028, Gauteng, South Africa
| | - Patrick Berka Njobeh
- Department of Biotechnology and Food Technology, Faculty of Science, Doornfontein Campus, University of Johannesburg, P.O Box 17011, Johannesburg, 2028, Gauteng, South Africa
| | - Tumisi Beiri Jeremiah Molelekoa
- Department of Biotechnology and Food Technology, Faculty of Science, Doornfontein Campus, University of Johannesburg, P.O Box 17011, Johannesburg, 2028, Gauteng, South Africa
| | - Samson Adeoye Oyeyinka
- National Centre for Food Manufacturing, Centre of Excellence in Agri-Food Technologies Building, South Lincolnshire Food Enterprise Zone Campus, University of Lincoln, 2 Peppermint Way, Holbeach, Spalding, PE12 7FJ, Lincolnshire, UK
| | - Rhulani Makhuvele
- Toxicology and Ethnoveterinary Medicine, Agricultural Research Council-Onderstepoort Veterinary Research (ARC-OVR), Private Bag X05, Onderstepoort, Pretoria, 0110, Gauteng, South Africa
| | - Oluwafemi Ayodeji Adebo
- Food Innovation Research Group, Department of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, P.O Box 17011, Johannesburg, 2028, Gauteng, South Africa.
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Dai L, Song Z, Niu C, Liu Y, Zhang H. Composition optimization and safety assessment of lactic-acid-bacteria-loaded composite film. CYTA - JOURNAL OF FOOD 2022. [DOI: 10.1080/19476337.2022.2085328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Lu Dai
- Yangling Vocational and Technical College, Shaanxi, China
| | - Zihan Song
- Institute of Vegetables and Flower, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chen Niu
- Northwest University, Xian Shaanxi, China
| | - Yingsha Liu
- Yangling Vocational and Technical College, Shaanxi, China
| | - Hongjuan Zhang
- Yangling Vocational and Technical College, Shaanxi, China
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