ArticleSaudi journal of biological sciences2022
Artificial intelligence-based approaches for modeling the effects of spirulina growth mediums on total phenolic compounds.
Article in Saudi journal of biological sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 40 citations in OpenAlex.
- AI verification for spirulina's antimicrobial power in total coliform and Staphylococcus aureus isolated from tilapia fillet.Scientific reports · 2025Article
- Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples.Scientific reports · 2025Article
- Suspended sediment load prediction using sparrow search algorithm-based support vector machine model.Scientific reports · 2024Article
- Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae.Bioengineered · 2023Review
- Antimicrobial activity of Spirulina platensis extract on total mesophilic and psychrophilic bacteria of fresh tilapia fillet.Scientific reports · 2023Article
- COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Novel Hybridized Computational Paradigms Integrated with Five Stand-Alone Algorithms for Clinical Prediction of HCV Status among Patients: A Data-Driven Technique.Life (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spirulina is a microalga and its phenolic compound is affected by growth mediums. In this study, Artificial intelligence (AI) based models, namely the Adaptive-Neuro Fuzzy Inference System (ANFIS) and Multilayer perceptron (MLP) models, and Step-Wise-Linear Regression (SWLR) were used to predict total phenolic compounds (TPC) of the spirulina algae. Spirulina productivity (P), extraction yield (EY), total flavonoids (TF), percent of flavonoid (%F) and percent of phenols (%P) are considered as input variables with the corresponding TPC as an output variable. From the result, TPC has a high positive correlation with the input variables with R = 0.99999. Also, the models showed that the ANFIS and SWLR gives superior result in the testing phase and increased its accuracy by 2% compared to MLP model in the prediction of TPC.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.