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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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Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis. Foods 2023; 12:foods12061242. [PMID: 36981168 PMCID: PMC10048131 DOI: 10.3390/foods12061242] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.
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Sarkar B, Sen S, Dutta S, Lahiri SK. Application of multi-gene genetic programming technique for modeling and optimization of phycoremediation of Cr(VI) from wastewater. BENI-SUEF UNIVERSITY JOURNAL OF BASIC AND APPLIED SCIENCES 2023. [DOI: 10.1186/s43088-023-00365-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Abstract
Abstract
Background
Removal of Cr(VI) from wastewater is essential as it is potentially toxic and carcinogenic in nature. Bioremediation of heavy metals using microalgae is a novel technique and has several advantages such as microalgae remove metals in an environmentally friendly and economic manner. The present study deals with modeling and optimization of the phycoremediation of Cr(VI) from synthetic wastewater. The initial concentration of Cr(VI), initial pH, and inoculum size were considered as input factors, and the percentage removal of Cr(VI) was chosen as a response.
Results
An accurate data-driven genetic programming model was developed with the experimental data of other scientists to find a relation between the percentage removal of Cr(VI) and all input parameters. To maximize the removal of Cr(VI), the grey wolf optimization technique was applied to determine the optimal values of input parameters.
Conclusion
These optimum input parameters are difficult to get through experimentation using the trial-and-error method. The established modelling and optimization technique is generic and can be applied to any other experimental study.
Graphical Abstract
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Sadhu T, Lahiri SK, Roy J, Bhattacharjee A, Chakrabarty J. Optimization of frying process for maintaining nutritional quality to satisfy consumers' sensory attributes: A novel application of multi‐criteria decision‐making approach. JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS 2022. [DOI: 10.1002/mcda.1799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Tithli Sadhu
- Department of Chemistry National Institute of Technology Durgapur Durgapur India
- Department of Biochemistry, School of Agriculture SR University Hanumakonda India
| | - Sandip Kumar Lahiri
- Department of Chemical Engineering National Institute of Technology Durgapur Durgapur West Bengal India
| | - Jagannath Roy
- Department of Mathematics National Institute of Technology Warangal Hanumakonda India
| | - Ashish Bhattacharjee
- Department of Biotechnology National Institute of Technology Durgapur Durgapur India
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Evaluation of the Thermophysical, Sensory, and Microstructural Properties of Colombian Coastal Carimañola Obtained by Atmospheric and Vacuum Frying. INTERNATIONAL JOURNAL OF FOOD SCIENCE 2022; 2022:7251584. [PMID: 35747781 PMCID: PMC9213204 DOI: 10.1155/2022/7251584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/16/2022] [Indexed: 11/17/2022]
Abstract
The quality of fried products affects consumer purchase decisions, and frying is an important stage in the production process. The objective of this research was to evaluate the thermophysical properties, the sensory quality, and microstructure of Colombian coastal Carimañola traditionally manufactured, in atmospheric frying and vacuum frying conditions. Lower moisture and fat content were reported in samples fried under vacuum compared to samples fried under atmospheric conditions, which is associated with the vacuum pressure during the process. Thermophysical properties, related to heat transfer in the samples, showed a correlation between thermal conductivity and moisture content. The micrographs visualized the changes in the porous structure of the coastal Carimañola. A greater effect was evidenced in the samples obtained by atmospheric frying because higher temperatures were used. The sensory evaluation reflected a preference for Carimañolas made with conventional frying. This research provides a basis for consumer purchases of traditionally fried products made with vacuum frying.
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Sadhu T, Banerjee I, Lahiri SK, Chakrabarty J. Enhancement of nutritional value of fried fish using an artificial intelligence approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:20048-20063. [PMID: 33761072 DOI: 10.1007/s11356-021-13548-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 03/16/2021] [Indexed: 06/12/2023]
Abstract
Frying affects the nutritional quality of fish detrimentally. In this study, using Catla catla and mustard oil, experiments were carried out in varying temperatures (140-240 °C), times (5-20 min), and oil amounts (25-100 ml/kg of fish) which established drastic reduction of 44.97% and 99.40% for polyunsaturated fatty acid (PUFA)/saturated fatty acids (SFA) and index of atherogenicity (IA) profile, respectively. Artificial neural network (ANN) was implemented successfully to provide an association between the independent inputs with dependent outputs (values of R2 were 0.99 and 0.98; RMSE were 0.038 and 0.046; and performance were 0.038 and 0.067 for PUFA/SFA and IA, respectively) by exhaustive search of various algorithms and activation functions available in literature. ANN model-based meta-heuristic, stochastic optimization formalisms, genetic algorithm (GA) and particle swarm optimization (PSO), were applied to optimize the combination of cooking parameters for improving the nutritional quality of food which improved the nutritional value by maximizing the PUFA/SFA profile up to 63.05% and minimizing the IA profile to 99.64%. Multi-objective genetic algorithm (MOGA) was also employed to tune the inputs by maintaining a balance between the contrasting outputs and enhance the overall food value simultaneously with multi-objective (beneficial for health, economic, and environment-friendly) proposal. MOGA was able to improve the PUFA/SFA profile up to 44.76% and reduce the IA profile to 92.94% concurrently with the reduction of wastage of culinary media and energy consumption, following the optimized cooking condition (118.92 °C, 6.06 min, 40 ml oil/kg of fish).
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Affiliation(s)
- Tithli Sadhu
- Department of Chemistry, National Institute of Technology Durgapur, Mahatma Gandhi Avenue, Durgapur, West Bengal, 713209, India
| | - Indrani Banerjee
- Department of Chemistry, National Institute of Technology Durgapur, Mahatma Gandhi Avenue, Durgapur, West Bengal, 713209, India
| | - Sandip Kumar Lahiri
- Department of Chemical Engineering, National Institute of Technology Durgapur, Durgapur, India
| | - Jitamanyu Chakrabarty
- Department of Chemistry, National Institute of Technology Durgapur, Mahatma Gandhi Avenue, Durgapur, West Bengal, 713209, India.
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