ArticleUltrasonics sonochemistry2024
Ultrasound assisted phytochemical extraction of red cabbage by using deep eutectic solvent: Modelling using ANFIS and optimization by genetic algorithms.
Article in Ultrasonics sonochemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- COSMO-RS guided screening of deep eutectic solvent extraction for essential oil fromRSC advances · 2026Article
- Deep Learning Enabled Optimization and Mass Transfer Mechanism in Ultrasound-Assisted Enzymatic Extraction of Polyphenols from Tartary Buckwheat Hulls.Foods (Basel, Switzerland) · 2025Article
- Influence of natural deep eutectic solvent pretreatment on the yield and composition of essential oil derived from Cyperi Rhizoma.BMC chemistry · 2025Article
- Optimization of Ultrasound-Assisted Extraction of Glucosinolates from Upcycled Cauliflower Using Response Surface Methodology.Molecules (Basel, Switzerland) · 2025Article
- Study on the Ultrasonic-Assisted Extraction Process of Anthocyanin from Purple Cabbage with Deep Eutectic Solvent.Molecules (Basel, Switzerland) · 2025Article
- The effects of experimental conditions on extraction of polyphenols from African Nutmeg peels using NADESs-UAE: a multifactorial modelling technique.Scientific reports · 2025Article
- Artificial neural network in optimization of bioactive compound extraction: recent trends and performance comparison with response surface methodology.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2025Review
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Authors and funding
7 authors.
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Abstract
The present investigation studied the effect of process parameters on the extraction of phytochemicals from red cabbage by the application of ultrasonication and temperature. The solvent selected for the study was deep eutectic solvent (DES) prepared by choline chloride and citric acid. The ultrasound assisted extraction process was modeled using adaptive neuro-fuzzy inference system (ANFIS) algorithm and integrated with the genetic algorithm for optimization purposes. The independent variables that influenced the responses (total phenolic content, antioxidant activity, total anthocyanin activity, and total flavonoid content) were ultrasonication power, temperature, molar ratio of DES, and water content of DES. Each ANFIS model was formed by the training of three Gaussian-type membership functions (MF) for each input, trained by a hybrid algorithm with 500 epochs and linear type MF for output MF. The ANFIS model predicted each response close to the experimental data which is evident by the statistical parameters (R
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