Prediction of partition coefficients of alkaloids in ionic liquids based aqueous biphasic systems using hybrid group method of data handling (GMDH) neural network

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

The complex modeling accuracy of gas hydrate models has been recently improved owing to the existence of data for machine learning tools. In this review, we discuss most of the machine learning tools used in various hydrate-related areas such as phase behavior predictions, hydrate kinetics, CO2 capture, and gas hydrate natural distribution and saturation. The performance comparison between machine learning and conventional gas hydrate models is also discussed in detail. This review shows that machine learning methods have improved hydrate phase property predictions and could be adopted in current and new gas hydrate simulation software for better and more accurate results.

Aqueous Two-Phase Systems Based on Ionic Liquids and Deep Eutectic Solvents as a Tool for the Recovery of Non-Protein Bioactive Compounds—A Review

Aqueous two-phase systems (ATPS) based on ionic liquids (IL) and deep eutectic solvents (DES) are ecofriendly choices and can be used to selectively separate compounds of interest, such as bioactive compounds. Bioactive compounds are nutrients and nonnutrients of animal, plant, and microbial origin that benefit the human body in addition to their classic nutritional properties. They can also be used for technical purposes in food and as active components in the chemical and pharmaceutical industries. Because they are usually present in complex matrices and low concentrations, it is necessary to separate them in order to increase their availability and stability, and ATPS is a highlighted technique for this purpose. This review demonstrates the application of ATPS based on IL and DES as a tool for recovering nonprotein bioactive compounds, considering critical factors, results and the most recent advances in this field. In addition, the review emphasizes the perspectives for expanding the use of nonconventional ATPS in purification systems, which consider the use of molecular modelling to predict experimental conditions, the investigation of diverse compounds in phase-forming systems, the establishment of optimal operational parameters, and the verification of bioactivities after the purification process.

Bioconjugation of enzyme with silica microparticles: a promising platform for α-amylase partitioning

Here, we report the implementation of α-amylase conjugated silica microparticles for improvement of α-amylase partitioning in a PEG-organic salt-based aqueous two phase system. A direct reduction method was employed for the synthesis of silica microparticles with simultaneous introduction of α-amylase. In this context, we synthesized three different silica α-amylase conjugated microparticles with variation of tetraethyl orthosilicate concentration, and thus the effect of final particle size and enzyme loading on partitioning was also studied. The partition coefficient ratio of α-amylase to Si:α-amylase of 2.186 : 21.701 validated an almost tenfold increase in separation. The microscopic structure of the system was thoroughly investigated in order to understand the extraction mechanism and any possible denaturation. Improved partition coefficients can be interpreted by the formation of α-amylase-silica-PEG carriers. Furthermore, circular dichroism (CD) spectra validated partial unfolding of the enzyme.

Modeling of glucose release from native and modified wheat starch gels during in vitro gastrointestinal digestion using artificial intelligence methods

Estimation of the amounts of glucose release (AGR) during gastrointestinal digestion can be useful to identify food of potential use in the diet of individuals with diabetes. In this work, adaptive neuro-fuzzy inference system (ANFIS), genetic algorithm-artificial neural network (GA-ANN) and group method of data handling (GMDH) models were applied to estimate the AGR from native (NWS), cross-linked (CLWS) and hydroxypropylated wheat starch (HPWS) gels during digestion under simulated gastrointestinal conditions. The GA-ANN and ANFIS were fed with 3 inputs of digestion time (1-120min), gel volume (7.5 and 15ml) and concentration (8 and 12%, w/w) for prediction of the AGR. The developed ANFIS predictions were close to the experimental data (r=0.977-0.996 and RMSE=0.225-0.619). The optimized GA-ANN, which included 6-7 hidden neurons, predicted the AGR with a good precision (r=0.984-0.993 and RMSE=0.338-0.588). Also, a three layers GMDH model with 3 neurons accurately predicted the AGR (r=0.979-0.986 and RMSE=0.339-0.443). Sensitivity analysis data demonstrated that the gel concentration was the most sensitive factor for prediction of the AGR. The results dedicated that the AGR will be accurately predictable through such soft computing methods providing less computational cost and time.

Influence of process variables on extraction of Cefalexin in a novel biocompatible ionic liquid based-aqueous two phase system

Despite the fact that ionic liquid-based aqueous two phase systems (ATPSs) have been widely studied for extraction purposes, the adequacy of biodegradable organic salts as salting out agents has been left unexploited.