Application of bio-inspired optimization algorithms in food processing

Sarkar, Tanmay; Salauddin, Molla; Mukherjee, Alok; Shariati, Mohammad Ali; Rebezov, Мaksim; Tretyak, Lyudmila; Pateiro, Mirian; Lorenzo, José M.

doi:10.1016/j.crfs.2022.02.006

Cited by 26 publications

(28 citation statements)

References 126 publications

(106 reference statements)

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“…Thus, the ANN-TLBO model is implemented on the investigational data obtained from the executed BBD, the data set was split into three subsets (32:7:7) for the purpose of training, testing and validating the model. The mean square obtained from the analysis is considered as an indicator of performance, and R 2 (Correlation coefficient) is considered the precision index (Sarkar et al, 2020(Sarkar et al, , 2022. The regression plots of the neural networks help in the representation of the training set, testing set, and validation set.…”

Section: Ann-tlbo Predictionmentioning

confidence: 99%

Anti-biofilm efficacy of green-synthesized ZnO nanoparticles on oral biofilm: In vitro and in silico study

et al. 2022

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The development of biofilm on the biotic and abiotic surfaces is the greatest challenge for health care sectors. At present times, oral infection is a common concern among people with an unhealthy lifestyle and most of these biofilms-associated infections are resistant to antibiotics. This has increased a search for the development of alternate therapeutics for eradicating biofilm-associated infection. Nanobiotechnology being an effective way to combat such oral infections may encourage the use of herbal compounds, such as bio-reducing and capping agents. Green-synthesis of ZnO nanoparticles (ZnO NP) by the use of the floral extract of Clitoria ternatea, a traditionally used medicinal plant, showed stability for a longer period of time. The NPs as depicted by the TEM image with a size of 10 nm showed excitation spectra at 360 nm and were found to remain stable for a considerable period of time. It was observed that the NPs were effective in the eradication of the oral biofilm formed by the major tooth attacking bacterial strains namely Porphyromonsas gingivalis and Alcaligenes faecalis, by bringing a considerable reduction in the extracellular polymeric substances (EPS). It was observed that the viability of the Porphyromonsas gingivalis and Alcaligenes faecalis was reduced by NP treatment to 87.89 ± 0.25% in comparison to that of amoxicillin. The results went in agreement with the findings of modeling performed by the use of response surface methodology (RSM) and artificial neural network (ANN). The microscopic studies and FT-IR analysis revealed that there was a considerable reduction in the biofilm after NP treatment. The in silico studies further confirmed that the ZnO NPs showed considerable interactions with the biofilm-forming proteins. Hence, this study showed that ZnO NPs derived from Clitoria ternatea can be used as an effective alternative therapeutic for the treatment of biofilm associated oral infection.

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Section: Ann-tlbo Predictionmentioning

confidence: 99%

Anti-biofilm efficacy of green-synthesized ZnO nanoparticles on oral biofilm: In vitro and in silico study

et al. 2022

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“…The review focused on the role of algorithms in biomimetic innovations in agriculture. As of 2022, extensive research has been conducted on BIAs for agriculture and related applications [19]; this includes Flockstream-a bio-inspired model that enables flock to aggregate into a swarm [46]; bioinspired hybridization of artificial neural networks (ANN) [47], and computing optimization algorithms (GA/GS-SVM) for optimized processes such as earthworm, root tree, plant growth, and regeneration optimization [48]. BIAs are part of the IoT systems intelligence decision supporting system for agriculture [49], which encompass smart sensors, wireless communication systems [50,51], ground-based AGV (automated guided vehicles), and UAVs [52].…”

Section: Contextmentioning

confidence: 99%

“…At present, there has been limited adoption of BIAs in agriculture beyond the prediction of rice growth rates [6], the direction of artificial bees [19], the scheduling of agricultural production [18], crop yield, and assessment of fruit quality [55][56][57]. Despite the immense potential of BIAs [18,[46][47][48][49], there are practical constraints to widespread use in agriculture. For example, Sarkar et al [48] noted it was challenging to establish an appropriate fitness function for better performance.…”

Section: Contextmentioning

confidence: 99%

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Maraveas

Arvanitis

Bartzanas

et al. 2022

Arch Computat Methods Eng

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The article reviewed the four major Bioinspired intelligent algorithms for agricultural applications, namely ecological, swarm-intelligence-based, ecology-based, and multi-objective algorithms. The key emphasis was placed on the variants of the swarm intelligence algorithms, namely the artificial bee colony (ABC), genetic algorithm, flower pollination algorithm (FPA), particle swarm, the ant colony, firefly algorithm, artificial fish swarm, and Krill herd algorithm because they had been widely employed in the agricultural sector. There was a broad consensus among scholars that certain BIAs' variants were more effective than others. For example, the Ant Colony Optimization Algorithm and genetic algorithm were best suited for farm machinery path optimization and pest detection, among other applications. On the contrary, the particle swarm algorithm was useful in determining the plant evapotranspiration rates, which predicted the water requirements and optimization of the irrigation process. Despite the promising applications, the adoption of hyper-heuristic algorithms in agriculture remained low. No universal algorithm could perform multiple functions in farms; different algorithms were designed to perform specific functions. Secondary concerns relate to data integrity and cyber security, considering the history of cyber-attacks on smart farms. Despite the concerns, the benefits associated with the BIAs outweighed the risks. On average, farmers can save 647–1866 L on fuel which is equivalent to US$734-851, with the use of GPS-guided systems. The accuracy of the BIAs mitigated the risk of errors in applying pesticides, fertilizers, irrigation, and crop monitoring for better yields.

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“…The application of ANNs and GAs to predict and optimize greenhouse banana fruit yield through nitrogen, potassium, and magnesium was performed by Ramezanpour et al [ 36 ]. Also, artificial neural networks and genetic algorithm used for optimization in food processing [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimizing in vitro percentage and speed callus induction of carrot via Multilayer Perceptron-Single point discrete GA and radial basis function

2022

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Background Callus induction is the first step in optimizing plant regeneration. Fit embryogenesis and shooting rely on callus induction. In addition, using artificial intelligence models in combination with an algorithm can be helpful in the optimization of in vitro culture. The present study aimed to evaluate the percentage and speed of callus induction optimization in carrot with a Multilayer Perceptron-Single point discrete genetic algorithm (GA). Materials and methods In this study, the outputs included callus induction percentage and speed, while inputs were different types and concentrations of plant growth regulator (0. 5, 0.2 mg/l 2,4-D, 0.3, 0.2, 0.5 mg/l BAP, 1, 0.2 mg/l Kin, and 2 mg/l NAA), different explants (shoot, root, leaf, and nodal), a different concentration compound of MS medium (1 × MS, 4× MS, and 8× MS) and time of sampling. The data were obtained in the laboratory, and multilayer perceptron (MLP) and radial basis function (RBF), two well-known ANNs, were employed to model. Then, GA was used for optimization, and sensitivity analysis was performed to indicate the inputs’ importance. Results The results showed that MLP had better prediction efficiency than RBF. Based on the results, R2 in training and testing data was 95 and 95% for the percentage of callus induction, while it was 94 and 95% for the speed of callus induction, respectively. In addition, a concentration compound of MS had high sensitivity, while times of sampling had low sensitivity. Based on the MLP-Single point discrete GA, the best results were obtained for shoot explants, 1× MS media, and 0.5 mg/l 2, 4-D + 0.5 mg/l BAP. Further, a non-significant difference was observed between the test result and predicted MLP. Conclusions Generally, MLP-Single point discrete GA is considered a potent tool for predicting treatment and fit model results used in plant tissue culture and selecting the best medium for callus induction.

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Application of bio-inspired optimization algorithms in food processing

Cited by 26 publications

References 126 publications

Anti-biofilm efficacy of green-synthesized ZnO nanoparticles on oral biofilm: In vitro and in silico study

Anti-biofilm efficacy of green-synthesized ZnO nanoparticles on oral biofilm: In vitro and in silico study

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Modeling and optimizing in vitro percentage and speed callus induction of carrot via Multilayer Perceptron-Single point discrete GA and radial basis function

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