Optimization of Lycopene Extraction Process Using Response Surface Methodology and Artificial Neural Network Model
Lycopene is a natural carotenoid with antioxidant,anti-cancer,lipid-lowering,and immune-boosting properties,widely used in the development of health products.To enhance the comprehensive utilization and extraction techniques for lycopene,a study was conducted to investigate the effects of various extraction conditions on lycopene yield from tomatoes.Using response surface methodology(RSM)and an artificial neural network model(ANN),the ultrasonic-assisted extraction process for lycopene was systematically modeled and optimized.The study found that the optimal extraction conditions for lycopene were an extraction temperature of 42.6℃,extraction time of 56.7 minutes,ultrasonic power of 350W,and a liquid-to-material ratio of 9mL/g,with the highest lycopene yield being 67.73µg/g.Compared to RSM,the ANN model demonstrated better fitting capability and optimization effects in the extraction of lycopene.The results of this study provide a basis for the extraction process and influencing factors of lycopene using ultrasound-assisted extraction.
lycopeneextraction processresponse surface methodologyartificial neural network model
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