# Modern Techniques for Flavonoid Extraction--To Optimize or Not to Optimize?
###### Abstract  
Flavonoids, specialized metabolites found in plants, have a number of beneficial properties and are important for maintaining good health. Efficient extraction methods are required to extract the most bioactive compounds from plant material. Modern techniques are replacing conventional methods of flavonoids extraction in order to reduce energy and solvent consumption, increase extraction efficiency, and satisfy growing market demand as well as environmental legislation. The extraction of bioactive molecules compounds is affected by a number of variables. To determine the conditions that ensure the highest extraction yield, it is advisable to analyze the interactions between the above in parallel. In this work, an overview of the advantages and performance of modern methods (microwave-assisted extraction, ultrasound-assisted extraction, pressurized liquids-assisted extraction, and supercritical fluids extraction) for the extraction of flavonoids is presented. This work also presents the application of extraction process optimization and extraction kinetics for flavonoid extraction, using different types of experimental designs for different flavonoid sources and different extraction methods. The general conclusion of all the studies listed is that an experimental design combined with RSM modeling reduces the number of experiments that should be performed to achieve maximum extraction yield. Keywords:flavonoids; extraction; extraction parameters optimization; modern extraction techniques +
Footnote †: journal: Medical  
Due to their important role in plants and their numerous health benefits, much attention has been paid for years to the development of the most effective methods for their extraction and characterization [8]. The most commonly used methods for characterization and quantification of known flavonoids are high performance liquid chromatography (HPLC) coupled with DAD/PDA detectors or mass spectrometry (MS). For the elucidation of unknown structures, multistep MS or NMR can be used. Gas chromatography (GC) is rarely used because it requires derivatization before analysis and derivatization is a time-consuming process involving hazardous reagents/solvents, sometimes under extreme temperature and pressure conditions [9]. Recently, the MALDI imaging method has also enabled the identification of flavonoids in situ in the tissue under study without extraction [10], but this method is not widely used due to its high cost. For the correct identification and further use of flavonoids, the crucial step is the extraction of flavonoids. Effective extraction techniques are necessary to extract the largest amount of bioactive chemicals from plant material [11; 12]. To minimize energy and solvent consumption, increase extraction efficiency, and meet increasing market demand and environmental regulations, modern techniques (such as microwaves, ultrasound, pressurized fluids, and supercritical fluids) are replacing traditional flavonoid extraction methods [13; 14]. Numerous factors such as solvent selection, temperature, contact time, liquid-to-solid ratio, particle size, and pH affect the extraction of bioactive molecular compounds [15]. In order to define the process conditions that ensure maximum extraction yield, it is recommended to simultaneously analyze the interaction between the listed factors. Therefore, mathematical and statistical tools such as response surface methodology (RSM) and artificial neural networks (ANN) are often used to study the effects of different factors and their interactions on extraction efficiency [16; 17; 18]. Although reaction surface models are commonly used to explain how process variables affect extraction efficiency, these models do not always provide data that support mass transfer and the dynamics of the extraction process, so the use of kinetic models is required to understand the extraction process [19; 20; 21]. Mathematical modeling is required to evaluate extraction kinetics and ensure the maximum product reproducibility [11; 15].