Artificial intelligence-based approaches for modeling the effects of spirulina growth mediums on total phenolic compounds

Metekia, Wubshet Asnake; Usman, A. G.; Ulusoy, Beyza; Abba, S.I.; Bali, Kefyalew Chirkena

doi:10.1016/j.sjbs.2021.09.055

Cited by 23 publications

(14 citation statements)

References 39 publications

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“…Still researchers are searching and focusing on different natural extracts from different sources including spirulina algae. Together with this many recent studies showed that Spirulina algae extracted compounds displayed good results in controlling food pathogenic microorganisms and serve as a preservatory and an antimicrobial activator in fish and fish products 4 , 15 – 17 .…”

Section: Introductionmentioning

confidence: 67%

“…On the other hand, Artificial Intelligence (AI) based models are currently being utilized in many production systems to evaluate, simulate, and forecast the process and interaction of numerous input and output factors. Metekia et al 17 studied the effect of spirulina growth mediums on phenolic compounds using the Artificial Intelligence based models; ANFIS and ANN together with SWLR. And the researchers find out, total phenolic compounds had high positive correlation with growth mediums and the ANFIS and SWLR gives excellent prediction than the ANN model.…”

Section: Introductionmentioning

confidence: 99%

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Antimicrobial activity of Spirulina platensis extract on total mesophilic and psychrophilic bacteria of fresh tilapia fillet

Metekia

Ulusoy

2023

Sci Rep

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Spirulina platensis has a wide range of activities, notably antibacterial property against food pathogens. This study investigates the antibacterial activity of S. platensis extract on Total Mesophilic and Psychrophilic Aerobic Bacteria. The results were compared using statistical analysis and the predicted model values using artificial intelligence-based models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) Models. The extraction of spirulina was done by using the freeze–thaw method with a concentration of 0.5, 1 and 5% w/v. Before the application of the extract, initial microbial load of fillets was analyzed the and the results were used as control. After application analysis was performed at 1, 24 and 48 h of storage at 4 °C. Based on the statistical analysis result the S. platensis extracts’ antimicrobial activity over TMAB of fresh tilapia fish fillets at 1, 24 and 48 h was using EA from 2.5 log10 CFU/g during the control stage to 1.8, 1.1 and 0.7 log10 CFU/g respectively whereas EB and EC was from 2.1 and 2.2 log10 CFU/g at control to 1.5, 0.8, 0.5 log10 CFU/g and 1.23, 0.6 and 0.32 log10 CFU/g respectively at the specified hour interval. Similarly, the three extracts over TPAB were from 2.8 log10 CFU/g at control time to 2.1, 1.5 and 0.9 in EA, while using EB reduces from 2.8 log10 CFU/g to 1.9, 1.3 and 0.8 log10 CFU/g at 1, 24 and 48 h respectively. Although EC presented the reduction from 1.9 log10 CFU/g to 1.4, 1 and 0.5 log10 CFU/g. This was supported by ANN and ANFIS models prediction.

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Section: Introductionmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Antimicrobial activity of Spirulina platensis extract on total mesophilic and psychrophilic bacteria of fresh tilapia fillet

Metekia

Ulusoy

2023

Sci Rep

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“…The primary focus of data-driven models is to obtain reliable forecasts for undiscovered datasets by fitting the model to the available data in accordance with the indicators being used [ 25 ]. In most cases, this is achieved by adjusting the model to better suit the data.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Ozsahin

Onakpojeruo²,

Duwa³

et al. 2023

Diagnostics

Self Cite

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The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models—specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)—were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases.

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“…Among 12 different models, ANN presented the best predictive ability showing R 2 of 0.94 as compared to other models with R 2 ranging from 0.81–0.94, demonstrating economical, rapid, and effective option for the development of TCC tools. An AI-based approach was conducted by Asnake Metekia et al [ 70 ], to study the effects of Spirulina platensis growth mediums on total phenolic compounds by comparing ANFIS, MLP, and SWLR algorithms. These algorithms were trained on several input variables such as algae productivity (P), extraction yield (EY), total flavonoids (TF), percentage flavonoid (%F), and percentage phenols (%P) to predict the concentration of total phenolic compounds.…”

Section: Digitalised Perspectives On the Quantification Of Organic Pi...mentioning

confidence: 99%

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Chong

Tang

Leong³

et al. 2023

Bioengineered

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Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R 2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R 2 accuracy ranging from 66.0% − 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

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Artificial intelligence-based approaches for modeling the effects of spirulina growth mediums on total phenolic compounds

Cited by 23 publications

References 39 publications

Antimicrobial activity of Spirulina platensis extract on total mesophilic and psychrophilic bacteria of fresh tilapia fillet

Antimicrobial activity of Spirulina platensis extract on total mesophilic and psychrophilic bacteria of fresh tilapia fillet

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

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