Optimization and modelling of enzymatic polymerization of ε-caprolactone to polycaprolactone using Candida Antartica Lipase B with response surface methodology and artificial neural network

A performance comparison study on PM2.5 prediction at industrial areas using different training algorithms of feedforward-backpropagation neural network (FBNN)

Presence of particulate matters with aerodynamic diameter of less than 2.5 μm (PM_2.5) in the atmosphere is fast increasing in Malaysia due to industrialization and urbanization. Prolonged exposure of PM_2.5 can cause serious health effects to human. This research is aimed to identify the most reliable model to predict the PM_2.5 pollution using multi-layered feedforward-backpropagation neural network (FBNN). Air quality and meteorological data were collected from Department of Environment (DOE) Malaysia. Six different training algorithms consisting of thirteen various training functions were trained and compared. FBNN model with the highest coefficient correlation (R²) and lowest root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were selected as the best performing model. Levenberg Marquardt (trainlm) is the best performing algorithms compared to other algorithms with R² value of 0.9834 and the lowest error values for RMSE (2.3981), MAE (1.7843) and MAPE (0.1063).

Modeling and Optimization of Process Parameters for Nutritional Enhancement in Enzymatic Milled Rice by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)

This study involves information about the concentrations of nutrients (proteins, phenolic compounds, free amino acids, minerals (Ca, P, and Iron), hardness) in milled rice processed with enzymes; xylanase and cellulase produced by Aspergillus awamori, MTCC 9166 and Trichoderma reese, MTCC164. Brown rice was processed with 60-100% enzyme (40 mL buffer -undiluted) for 30 to 150 min at 30 °C to 50 °C followed by polishing for 20-100 s at a safe moisture level. Multiple linear regression (MLR) and artificial neural network (ANN) models were used for process optimization of enzymes. The MLR correlation coefficient (R2) varied between 0.87-0.90, and the sum of square (SSE) was placed within 0.008-8.25. While the ANN R2 (correlation coefficient) varied between 0.97 and 0.9999(1), MSE changed from 0.005 to 6.13 representing that the ANN method has better execution across MLR. The optimized cellulase process parameters (87.2% concentration, 80.1 min process time, 33.95 °C temperature and 21.8 s milling time) and xylanase process parameters (85.7% enzyme crude, 77.1 min process time, 35 °C temperature and 20 s) facilitated the increase of Ca (70%), P (64%), Iron (17%), free amino acids (34%), phenolic compounds (78%) and protein (84%) and decreased hardness (20%) in milled rice. Scanning electron micrographs showed an increased rupture attributing to enzymes action on milled rice.

Lactobacillus amylovorus derived lipase-mediated silver derivatization over poly(ε-caprolactone) towards antimicrobial coatings

Owing to the probiotic origin, lipases-derived from the Lactobacilli sp. are considered to be promising biomaterials for in vivo applications. On a different note, poly(ε-caprolactone) (PCL)-an FDA-approved polymer for implantable applications-lacks inherent antimicrobial property, because of which suitable modifications are required to render it with bactericidal activity. Here, we employ Lactobacillus amylovorous derived lipase to surface derivatize the PCL films with silver that is a highly efficient inorganic broad-spectrum antimicrobial substance. Two different surface functionalization strategies have been employed over the alkaline hydrolyzed PCL films towards this purpose: In the first strategy, lipase-capped silver nanoparticles (Ag NPs) have been synthesized in a first step, which have been covalently immobilized over the activated carboxylic groups on the PCL film surface in a subsequent step. In the second strategy, the lipase was covalently immobilized over the activated carboxylic groups of the PCL film surface in the first step, over which silver was deposited in the second step using the dip-coating method. While the characterization study using X-ray photoelectron spectroscopy (XPS) has revealed the successful derivatization of silver over the PCL film, the surface characterization using field-emission scanning electron microscopy (FE-SEM) study has shown a distinct morphological change with higher silver loading in both strategies. The antimicrobial studies employing E. coli have revealed 100 % inhibition in the bacterial growth in 4-6 h with the Ag NPs-immobilized PCL films as opposed to >8 h with those prepared through the dip-coating method. Additionally, the cytotoxicity assay using mouse fibroblast cells has shown that the PCL films immobilized with lipase-capped Ag NPs exhibit high cell compatibility, similar to that of pristine PCL film, and thereby making it suitable for in vivo applications.

Recent advancements in enzyme-mediated crosslinkable hydrogels: In vivo-mimicking strategies

Enzymes play a central role in fundamental biological processes and have been traditionally used to trigger various processes. In recent years, enzymes have been used to tune biomaterial responses and modify the chemical structures at desired sites. These chemical modifications have allowed the fabrication of various hydrogels for tissue engineering and therapeutic applications. This review provides a comprehensive overview of recent advancements in the use of enzymes for hydrogel fabrication. Strategies to enhance the enzyme function and improve biocompatibility are described. In addition, we describe future opportunities and challenges for the production of enzyme-mediated crosslinkable hydrogels.

Emerging application of robust data envelopment analysis for optimization of graft copolymerization of poly(2-hydroxyethyl methacrylate) to Tamarindus indica seed polysaccharide

A newer application of data envelopment analysis (DEA) model with robust data envelopment analysis (RDEA) was presented for optimization of reaction variables of graft copolymerization of 2-hydroxyethyl methacrylate (HEMA) to Tamarindus indica seed polysaccharide (TSP). It helped to find out the most appropriate reaction conditions and variables (concentrations of HEMA and reaction initiator; temperature and time duration) for copolymerization. The data generated through the experimental work has been analyzed and indexed to predict the maximum %grafting. Sensitivity analysis was performed to check robustness of efficiency scores of CCR DEA efficient samples and a comparative analysis of the CCR DEA and RRDEA efficiency score has been done. The data obtained via real-time experiments and data predicted using computational modelling predictions were found to be in close vicinity.