ReviewAnalytical sciences : the international journal of the Japan Society for Analytical Chemistry2025
Artificial neural network in optimization of bioactive compound extraction: recent trends and performance comparison with response surface methodology.
Review in Analytical sciences : the international journal of the Japan Society for Analytical Chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Bioactive Compound Recovery from Apple Pomace by Aqueous Ultrasound-Assisted Extraction: Machine Learning Modelling and Multi-Objective Optimization.Antioxidants (Basel, Switzerland) · 2026Article
- Optimization of flavonoids extraction and elucidation of antioxidant mechanisms in Dendrobium flexicaule using metabolomics and machine learning.Ultrasonics sonochemistry · 2026Article
- Green Acerola (Malpighia emarginata) Extraction Optimization, Cellular Antioxidant Activity, and Spray Drying: Toward a Stable Vitamin C-Rich Powder.Plant foods for human nutrition (Dordrecht, Netherlands) · 2026Article
- Article
- Optimization of Bioactive Compound Extraction fromAntioxidants (Basel, Switzerland) · 2026Article
- Ultrasound-Assisted Extraction of Bioactive Compounds from Strawberry Pomace: Optimization and Bioactivity Assessment.Antioxidants (Basel, Switzerland) · 2025Article
- Optimisation of Phenolic Compound Extraction fromAntioxidants (Basel, Switzerland) · 2025Article
- Ultrasound-assisted extraction, enrichment, and hypolipidemic potential of triterpenes from Grifola frondosa mycelia.BMC chemistry · 2025Article
- Ecofriendly Extraction of Polyphenols fromMolecules (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Plant products and its by-products are rich source of bioactive compounds like antioxidants, flavonoids, phenolics, pigments and phytochemicals. Bioactive compound's health-promoting properties are well studied. However, optimal extraction of bioactive compounds is a complex, labour- and time-intensive process. It is also highly sensitive to experimental variables. Predicting output variables can reduce the experimental work and has positive environmental impact. Various tools such as Response Surface Methodology (RSM), Mathematical modelling have been commonly used for optimization and predictive modelling of the extraction process. Although mathematical modelling and RSM are efficient, recent studies have used Artificial Neural Network (ANN) which is more efficient and accurate and can perform extensive predictions with high accuracy. The manuscript focuses on current trends of ANN application in optimizing the extraction of bioactive compounds. In this study, ANN and RSM have been compared in terms of their performances in optimizing and modelling the extraction of bioactive compounds from herbs, medicinal plants, fruit, vegetables, and their by-products. The findings from the literature indicate that efficiency of ANN was superior to RSM. Future researches can focus on use of ANN in industrial optimization experiments.
Indexed as
Identifiers
39503809What Socratic holds
Registered trials
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