TY - JOUR
T1 - Green extraction of bioactive compounds from orange peel waste using NADES and microwave-assisted technique
T2 - A CatBoost-GMDH ensemble optimized by mantis search algorithm
AU - Khajeh, Mostafa
AU - Ghaffari-Moghaddam, Mansour
AU - Piri, Jamshid
AU - Barkhordar, Afsaneh
AU - Saloglu, Didem
N1 - Publisher Copyright:
© 2025 Elsevier GmbH
PY - 2025/11
Y1 - 2025/11
N2 - The increasing need for sustainable and efficient extraction methods has led to interest in green technologies for the extraction of bioactive compounds from agricultural waste. Orange peel, a rich of polyphenols and flavonoids, offers significant potential for sustainable application. This research formulated a novel multi-task optimization approach combining the Mantis Search Algorithm (MSA) with hybrid machine learning models to extract bioactive compounds from orange peel waste using natural deep eutectic solvents (NADES) and microwave-assisted extraction (MAE). The holistic strategy employed a multi-task optimization approach that concurrently optimized four key components: feature selection, model hyperparameters, ensemble weights, and process parameters. The process parameters investigated included microwave power (302–495 W), extraction temperature (31–59 °C), extraction time (5.2–30 min), and mass-to-solvent ratio (41–80 mg/mL). Three machine learning models were developed and systematically compared: CatBoost, Group Method of Data Handling (GMDH), and their weighted ensemble fusion. The ensemble MSA-hybrid model exhibited the best predictive performance with R² of 0.656, 0.981, and 0.990 for total phenolic content, total flavonoid content, and DPPH radical scavenging activity, respectively. Temperature was found to be the most significant process parameter for all response variables, followed by extraction time and mass-to-solvent ratio. The multi-task optimization approach successfully developed robust predictive models capable of guiding extraction parameter selection for improved bioactive compound yields. Extensive validation using thorough residual analysis, stability testing, and confidence interval analysis reaffirmed model reliability and generalizability. This novel study was successful in offering valuable industry-ready solutions for sustainable bioactive compound extraction while supporting agricultural waste valorization and circular economy concepts.
AB - The increasing need for sustainable and efficient extraction methods has led to interest in green technologies for the extraction of bioactive compounds from agricultural waste. Orange peel, a rich of polyphenols and flavonoids, offers significant potential for sustainable application. This research formulated a novel multi-task optimization approach combining the Mantis Search Algorithm (MSA) with hybrid machine learning models to extract bioactive compounds from orange peel waste using natural deep eutectic solvents (NADES) and microwave-assisted extraction (MAE). The holistic strategy employed a multi-task optimization approach that concurrently optimized four key components: feature selection, model hyperparameters, ensemble weights, and process parameters. The process parameters investigated included microwave power (302–495 W), extraction temperature (31–59 °C), extraction time (5.2–30 min), and mass-to-solvent ratio (41–80 mg/mL). Three machine learning models were developed and systematically compared: CatBoost, Group Method of Data Handling (GMDH), and their weighted ensemble fusion. The ensemble MSA-hybrid model exhibited the best predictive performance with R² of 0.656, 0.981, and 0.990 for total phenolic content, total flavonoid content, and DPPH radical scavenging activity, respectively. Temperature was found to be the most significant process parameter for all response variables, followed by extraction time and mass-to-solvent ratio. The multi-task optimization approach successfully developed robust predictive models capable of guiding extraction parameter selection for improved bioactive compound yields. Extensive validation using thorough residual analysis, stability testing, and confidence interval analysis reaffirmed model reliability and generalizability. This novel study was successful in offering valuable industry-ready solutions for sustainable bioactive compound extraction while supporting agricultural waste valorization and circular economy concepts.
KW - Machine learning optimization
KW - Mantis Search Algorithm
KW - Microwave-assisted extraction
KW - Natural deep eutectic
KW - Orange peel
UR - https://www.scopus.com/pages/publications/105018021307
U2 - 10.1016/j.jarmap.2025.100668
DO - 10.1016/j.jarmap.2025.100668
M3 - Article
AN - SCOPUS:105018021307
SN - 2214-7861
VL - 49
JO - Journal of Applied Research on Medicinal and Aromatic Plants
JF - Journal of Applied Research on Medicinal and Aromatic Plants
M1 - 100668
ER -