Valorization of lignocellulosic biomass and food residues to obtain valuable chemicals is essential to the establishment of a sustainable and biobased economy in the modern world. The latest and greenest generation of ionic liquids (ILs) are deep eutectic solvents (DESs) and natural deep eutectic solvents (NADESs); these have shown great promise for various applications and have attracted considerable attention from researchers who seek versatile solvents with pretreatment, extraction, and catalysis capabilities in biomass- and biowaste-to-bioenergy conversion processes. The present work aimed to review the use of DESs and NADESs in the valorization of biomass and biowaste as pretreatment or extraction solvents or catalysis agents.

Deep eutectic solvents (DESs) have emerged as promising green solvents, due to their versatility and properties such as high biodegradability, inexpensiveness, ease of preparation and negligible vapor pressure. Thus, DESs have been used as sustainable media and green catalysts in many chemical processes. On the other hand, lignocellulosic biomass as an abundant source of renewable carbon has received ample interest for the production of biobased chemicals. In this review, the state of the art of the catalytic use of DESs in upgrading the biomass-related substances towards biofuels and value-added chemicals is presented, and the gap in the knowledge is indicated to direct the future research.

In this work, quantitative structure-property relationship (QSPR) models for prediction of retention time (RT) of 47 volatile compounds in rosé wines on the basis of their molecular structures were developed by applying multiple linear regression (MLR) and artificial neural network (ANN). A Levenberg- Marquardt algorithm trained feed-forward back-propagation artificial neural network (ANN) was employed. The data were randomly divided into 29 training and 9 validation sets. For comparison purpose, multiple linear regression (MLR) model of the same data was developed. Cross-validation was used to validate the QSPR models. Also, the application of the models for prediction of external set’s retention times of compounds without any contribution to model development steps was another validation process. The MLR model yielded marginally acceptable statistics with test correlation R² = 0.993 and mean squared error (MSE) = 0.0075. Not surprisingly, the ANN model was significantly more accurate with test correlation R² = 0.995 and mean squared error (MSE) = 0.00013.