Chironji (Buchanania lanzan) fruit juice extraction using cellulase enzyme: modelling and optimization of process by artificial neural network and response surface methodology.

Chironji fruit juice extraction using cellulase enzyme was performed at various concentrations of cellulase, incubation temperature, and time. Artificial neural network (ANN) conjugated with genetic algorithm (GA), and response surface methodology (RSM) were used for attaining the process conditions where the highest juice yield can be achieved. The recommended values of process parameters obtained using ANN-GA method were cellulase concentration of 0.093% (w/w), incubation temperature of 45.7 °C, and incubation time of 116 min. Using RSM method, the recommended values were cellulase concentration of 0.081% (w/w), incubation temperature of 39.6 °C, and incubation time of 99 min. However, the incubation time was found to be the most significant independent process parameter followed by cellulase concentration that affect the juice yield. The juice yield determined experimentally at RSM and ANN-GA recommended conditions was 69.77 ± 0.16% and 70.15 ± 0.12%, respectively. These values indicated that both RSM and ANN-GA methods have comparable accuracies. However, juice extracted using ANN-GA recommended conditions had better physicochemical properties than the juice extracted using RSM recommended conditions.

Nanocellulose separation from barley straw via ultrasound-assisted choline chloride - Formic acid deep eutectic solvent pretreatment and high-intensity ultrasonication.

The present study aims at investigating the application of ultrasound assisted choline chloride (ChCl) - formic acid (FA) deep eutectic solvent (DES) pretreatment of Barley straw. In addition, the efficiency of a wet grinding followed by high intensity ultrasound (HIUS) treatment for production of cellulose nanofibers (CNF) has been evaluated. The DES (using ChCl: FA at 1:9 M ratio) treatment at 45 kHz ultrasound frequency and 3 h of treatment duration resulted in 84.68 ± 1.02 % and 82.96 ± 0.79 % of lignin and hemicellulose solubilisation, respectively. The purification of DES treated solid residue resulted in cellulose with more than 90 % purity. Further, 10 min of wet grinding followed by 40 min of HIUS treatment resulted in more than 80 % nano-fibrillation efficiency. The produced CNF had diameters less than 100 nm in number size distribution and type I cellulose structure. This study confirmed that the developed process offers a sustainable method for producing nanocellulose from agricultural waste.

Emerging technologies for the production of nanocellulose from lignocellulosic biomass.

Nanocellulose is a unique and promising natural nanomaterial and has gained significant attention due to its applications in several important areas. Thus, researchers are continuously looking for the most efficient, sustainable, economically viable, and environmentally friendly production technologies to fulfil its growing demand. Conventional production technologies, which include various physical, chemical, and physicochemical methods, are currently inadequate for this purpose and have several limitations such as long processing time, high energy consumption, low recovery of nanocellulose, and many others. To overcome these shortcomings, scientists have investigated the prospect of utilizing emerging processing technologies such as microwave irradiation, deep eutectic solvent, enzymatic processing, cold plasma, electron beam irradiation, and pulsed electric field in nanocellulose preparation. In general, studies have shown that the application of emerging technologies enhances the extraction yield and properties of nanocellulose. This article presents a review of the most recent works reported on the application of emerging technologies in nanocellulose production.

Process optimization of cypermethrin biodegradation by regression analysis and parametric modeling along with biochemical degradation pathway.

Pyrethroid pesticides are of great environmental and health concern with regard to neurotoxicity and ubiquitous occurrence. Here, we reported a new bacterial strain identified as Bacillus cereus AKAD 3-1 that degraded 88.1% of 50 mg/l of cypermethrin in an aqueous medium. The biodegradation of cypermethrin was optimized by CCD (central composite design) and validated by ANN-GA (artificial neural network-genetic algorithm). Both the approaches proved to possess good performance in modeling and optimizing the growth conditions. Results indicated that the process variables have a significant (< 0.0001) impact on cypermethrin biodegradation. Moreover, the predicted CCD model had a "lack of fit p-value" of "0.9975." The optimum CCD and ANN model had an R2 value of 0.9703 and 0.9907, indicating that the two models' experimental and predicted values are closely fitted. The isolate successfully converted cypermethrin to CO2 and phenol without producing any toxic metabolite. Finally, a degradation pathway was proposed with the intermediate compounds identified by GC-MS. The present study highlights an important potential application of strain AKAD 3-1 for the in situ bioremediation of cypermethrin-contaminated environments.

Modeling and optimization of pectinase-assisted low-temperature extraction of cashew apple juice using artificial neural network coupled with genetic algorithm.

In this study, pectinase-assisted extraction of cashew apple juice was modeled and optimized using a multi-layer artificial neural network (ANN) coupled with genetic algorithm (GA). The effect of incubation time, incubation temperature, and enzyme concentration on different responses such as yield, turbidity, ascorbic acid content, polyphenol content, total soluble solids, and pH was also determined. The developed ANN has minimum mean squared error values of 0.83, 40.92, 29.01, and 8.95 and maximum R values of 0.9999, 0.9972, 0.9995, and 0.9996 for training, testing, validation, and all data sets, respectively, which shows good agreement between the actual and predicted values. The optimum extraction parameters obtained using the developed ANN-GA were as follows: an incubation time of 64 min, incubation temperature of 32 °C, and enzyme concentration of 0.078%. The measured value of responses at the optimized process conditions were in accordance with the predicted values obtained using the developed ANN model.

Seaweed Polysaccharide in Food Contact Materials (Active Packaging, Intelligent Packaging, Edible Films, and Coatings).

Food contact materials (FCMs) are materials that come in contact with food products such as food packaging which play a significant role in the food quality and safety. Plastic, which is a major food packaging material, harms the eco-system, wildlife, and the environment. As a result, numerous researches have been in progress on alternative polymers, which has similar properties as plastic but is also environmentally friendly (biodegradable). In recent years, the utilization of seaweed polysaccharides has piqued interest due to its biodegradability, non-toxicity, antioxidant capabilities, and excellent film formation ability. However, it has a number of drawbacks such as low tensile strength, water solubility, and moderate antibacterial characteristics, among others. The addition of other biopolymers, nanoparticles, or natural active agents improves these features. In this review article, we have summarized the current state of seaweed polysaccharide research in active packaging, intelligent packaging, edible films, and coatings. It also highlights the physical, thermal, antioxidant, and other properties of these materials. Finally, the article discusses the relevant legislation as well as the field's future prospects. Research shows that seaweeds polysaccharide looks promising as a sustainable food contact material, but there is always a potential for development to make it market feasible.