Response surface optimization and support vector regression modeling of microwave-assisted essential oil extraction from cumin seeds

[1]  S. Rezania,et al.  Kinetics and thermodynamic analysis of palm oil decanter cake and alum sludge combustion for bioenergy production , 2023, Sustainable Chemistry and Pharmacy.

[2]  Zhigang Li,et al.  Freshness prediction of modified atmosphere packaging lamb meat based on digital images from mobile portable devices , 2023, Journal of Food Process Engineering.

[3]  V. Dragović-Uzelac,et al.  Hydrodistillation and Steam Distillation of Fennel Seeds Essential Oil: Parameter Optimization and Application of Cryomilling Pretreatment , 2023, Processes.

[4]  Rui Zhang,et al.  Triboelectric-electromagnetic hybrid generator based self-powered flexible wireless sensing for food monitoring , 2023, Chemical Engineering Journal.

[5]  Xiaoshuan Zhang,et al.  Non-Destructive Detection of the Freshness of Air-Modified Mutton Based on Near-Infrared Spectroscopy , 2023, Foods.

[6]  Abdullah M. Asiri,et al.  Removal of congo red from water by adsorption onto activated carbon derived from waste black cardamom peels and machine learning modeling , 2023, Alexandria Engineering Journal.

[7]  F. Kusumo,et al.  Optimization of ultrasound-assisted oil extraction from Carica candamarcensis; A potential Oleaginous tropical seed oil for biodiesel production , 2023, Renewable Energy.

[8]  Arunandan Kumar,et al.  Review on essential oil extraction from aromatic and medicinal plants: Techniques, performance and economic analysis , 2022, Sustainable Chemistry and Pharmacy.

[9]  Yi-ming Ha,et al.  New research development on trans fatty acids in food: Biological effects, analytical methods, formation mechanism, and mitigating measures. , 2022, Progress in lipid research.

[10]  Lini Peng,et al.  Extraction, Structure and Immunoregulatory Activity of Low Molecular Weight Polysaccharide from Dendrobium officinale , 2022, Polymers.

[11]  Khursheed B. Ansari,et al.  Novel Machine Learning (ML) Models for Predicting the Performance of Multi-Metal Binding Green Adsorbent for the Removal of Cd (II), Cu (II), Pb (II) and Zn (II) ions , 2022, Environmental Advances.

[12]  Naila Iram,et al.  Chemical Composition, Essential Oil Characterization and Antibacterial activity of Cumin (Cuminum Cyminum) , 2022, Pakistan BioMedical Journal.

[13]  K. Nishinari,et al.  The pH-responsive phase separation of type-A gelatin and dextran characterized with static multiple light scattering (S-MLS) , 2022, Food Hydrocolloids.

[14]  W. Zhu,et al.  Chemical compositions and bioactivities of essential oil from perilla leaf (Perillae Folium) obtained by ultrasonic-assisted hydro-distillation with natural deep eutectic solvents. , 2021, Food chemistry.

[15]  P. Mishra,et al.  Herbs and Spices—New Processing Technologies. Syzygium aromaticum: Medicinal Properties and Phytochemical Screening , 2021, Herbs and Spices - New Processing Technologies [Working Title].

[16]  W. Guo,et al.  Low residual stress C/C composite-titanium alloy joints brazed by foam interlayer , 2021, Ceramics International.

[17]  M. Assari,et al.  Sequential ultrasound-microwave technique as an efficient method for extraction of essential oil from Lavandula coronopifolia Poir , 2021, Journal of Food Measurement and Characterization.

[18]  A. Sabarudin,et al.  An experimental design approach for the optimization of scopoletin extraction from Morinda citrifolia L. using accelerated solvent extraction. , 2021, Talanta.

[19]  S. Hazrati,et al.  The effect of ultrasonic pre-treatment on the yield, chemical composition and biological activity of essential oil in Oliveria decumbens flowers , 2021 .

[20]  L. K. Keong,et al.  Syngas production from greenhouse gases using Ni–W bimetallic catalyst via dry methane reforming: Effect of W addition , 2021, International Journal of Hydrogen Energy.

[21]  Mohammad Azad Alam,et al.  Modeling, Optimization and Performance Evaluation of TiC/Graphite Reinforced Al 7075 Hybrid Composites Using Response Surface Methodology , 2021, Materials.

[22]  Khursheed B. Ansari,et al.  Support vector regression-based model for phenol adsorption in rotating packed bed adsorber , 2021, Environmental Science and Pollution Research.

[23]  Zhang Xiaoshuan,et al.  Optimization and validation of the knowledge-based traceability system for quality control in fish waterless live transportation , 2021 .

[24]  Qiaozhi Zhang,et al.  Dietary protein-phenolic interactions: characterization, biochemical-physiological consequences, and potential food applications , 2020, Critical reviews in food science and nutrition.

[25]  L. Abdullah,et al.  Ultrasonic-Assisted Extraction (UAE) Process on Thymol Concentration from Plectranthus Amboinicus Leaves: Kinetic Modeling and Optimization , 2020, Processes.

[26]  Huu Nghi Do,et al.  Optimization of Microwave-Assisted Extraction Process of Callicarpa candicans (Burm. f.) Hochr Essential Oil and Its Inhibitory Properties against Some Bacteria and Cancer Cell Lines , 2020, Processes.

[27]  S. Hazrati,et al.  Extraction of essential oils of Ferulago angulata with microwave-assisted hydrodistillation , 2019, Industrial Crops and Products.

[28]  S. Zaidi,et al.  Support vector regression (SVR)-based adsorption model for Ni (II) ions removal , 2019, Groundwater for Sustainable Development.

[29]  Guangjing Chen,et al.  Comparison of different extraction methods for polysaccharides from bamboo shoots (Chimonobambusa quadrangularis) processing by-products. , 2019, International journal of biological macromolecules.

[30]  S. Zaidi,et al.  Development of SVR-based model and comparative analysis with MLR and ANN models for predicting the sorption capacity of Cr(VI) , 2017 .

[31]  S. Zaidi,et al.  Support Vector Regression Prediction and Analysis of the Copper (II) Biosorption Efficiency , 2017 .

[32]  S. Zaidi,et al.  Support vector regression model for predicting the sorption capacity of lead (II) , 2016 .

[33]  S. Zaidi.  Development of support vector regression (SVR)-based model for prediction of circulation rate in a vertical tube thermosiphon reboiler , 2012 .

[34]  Mansour Ghaffari Moghaddam,et al.  Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles , 2011 .

[35]  J. Bélanger,et al.  Microwave‐Assisted Extraction , 2011 .

[36]  Béatrice Kaufmann,et al.  Recent extraction techniques for natural products: microwave-assisted extraction and pressurised solvent extraction. , 2002, Phytochemical analysis : PCA.

[37]  M. D. Luque de Castro,et al.  Soxhlet extraction of solid materials: an outdated technique with a promising innovative future , 1998 .

[38]  M Vinatoru,et al.  The use of ultrasound for the extraction of bioactive principles from plant materials. , 1997, Ultrasonics sonochemistry.

[39]  S. Chelliapan,et al.  Biodiesel production by single-step acid-catalysed transesterification of Jatropha oil under microwave heating with modelling and optimisation using response surface methodology , 2022, Fuel.

[40]  Support vector regression: A novel soft computing technique for predicting the removal of cadmium from wastewater , 2020, Indian Journal of Chemical Technology.

[41]  Khushwinder Kaur.  Functional nutraceuticals: past, present, and future , 2016 .

[42]  Siddhartha Datta,et al.  Modeling of microwave-assisted extraction of natural dye from seeds of Bixa orellana (Annatto) using response surface methodology (RSM) and artificial neural network (ANN) , 2013 .

[43]  V. Vapnik.  The Support Vector Method of Function Estimation , 1998 .