Fruit peel waste: characterization and its potential uses

Globally, India is the leading producer of fruits. Fruits after consumption leave a peel which is a nuisance to the environment as a solid waste. In this article, commonly available large volume-fruit peels (FP) (viz. banana, orange, citrus, lemon and jackfruit) were investigated for surface, physical and chemical characteristic with a view to propose their valorization in detail. Each FP was characterized by proximate and ultimate analysis, porosity, particle density, bulk density, point of zero charge (pHpac), surface pH, surface charges, water absorption capacity, BET surface area, scanning electron microscopy, Fourier transform infrared spectroscopy and TGA/derivative of thermogravimetric. The BET surface area of FP is very less, between 0.60 and 1.2 m2/g. The pHpac and surface pH values of orange peel (OP), citrus peel (CP), lemon peel (LP) and jackfruit peels (JFP) are in the range of 3-4. The pHpac value and surface pH of banana peel (BP) is closer to 7. The order of surface acidity is OP > LP > CP > JFP > BP. From TG curves it is clear that FPs are stable below 150°C. The results will be useful for rational design, when FP is used as a substrate for bioactive compounds, phenolic antioxidants, organic acids, enzymes, biofertilizer, production of energy and as absorbents

A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli

Motivation: Inclusion body formation has been a major deterrent for overexpression studies since a large number of proteins form insoluble inclusion bodies when overexpressed in Escherichia coli. The formation of inclusion bodies is known to be an outcome of improper protein folding; thus the composition and arrangement of amino acids in the proteins would be a major influencing factor in deciding its aggregation propensity. There is a significant need for a prediction algorithm that would enable the rational identification of both mutants and also the ideal protein candidates for mutations that would confer higher solubility-on-overexpression instead of the presently used trial-and-error procedures. Results: Six physicochemical properties together with residue and dipeptide-compositions have been used to develop a support vector machine-based classifier to predict the overexpression status in E.coli. The prediction accuracy is ~72% suggesting that it performs reasonably well in predicting the propensity of a protein to be soluble or to form inclusion bodies. The algorithm could also correctly predict the change in solubility for most of the point mutations reported in literature. This algorithm can be a useful tool in screening protein libraries to identify soluble variants of proteins

Water-triggered frontal polymerization

A totally new mode of frontal polymerization (FP) of acrylamide is established which is triggered by the simple addition of a minute, specific volume of water. Experimental conditions under which this mode of polymerization yields linear and water-soluble polyacrylamide were carefully established, paving the way to synthesize commercially pertinent homo- and copolymers. A new redox couple was identified to circumvent the imidization and the ensuing gelation, hitherto associated with FP of acrylamide. Effects of reaction variables such as type and concentration of redox couple and volume of water on measurable parameters of FP such as front velocity, front temperature, shape of front and yield have been studied. Two types of redox couples are reported. Nonplanar frontal regime was observed in few redox couples. We could visually observe helical patterns with naked eyes, while layered patterns were observable under SEM. Additionally, micro-phase separation and heterogeneity in the polymer matrix was observed due to unreacted pockets of monomer which evolve via bulk mode. This nonlinear phenomenon is described

Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation

This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses

Enhanced production of amidase from Rhodococcus erythropolis MTCC 1526 by medium optimisation using a statistical experimental design

In the present work, statistical experimental methodology was used to enhance the production of amidase from Rhodococcus erythropolis MTCC 1526. R. erythropolis MTCC 1526 was selected through screening of seven strains of Rhodococcus species. The Placket-Burman screening experiments suggested that sorbitol as carbon source, yeast extract and meat peptone as nitrogen sources, and acetamide as amidase inducer are the most influential media components. The concentrations of these four media components were optimised using a face-centred design of response surface methodology (RSM). The optimum medium composition for amidase production was found to contain sorbitol (5 g/L), yeast extract (4 g/L), meat peptone (2.5 g/L), and acetamide (12.25 mM). Amidase activities before and after optimisation were 157.85 units/g dry cells and 1,086.57 units/g dry cells, respectively. Thus, use of RSM increased production of amidase from R. erythropolis MTCC 1526 by 6.88-fold

Physical and magnetic properties of barium calcium hexaferrite nano-particles synthesized by water-in-oil reverse micelle and co-precipitation techniques

BaCaB2BFeB16BOB27B hexaferrite particles were prepared using two different techniques namely (i) reverse micelle and (ii) co-precipitation with and without presence of surfactants (cationic, anionic and nonionic). The precipitate was calcinated at 950EsC for 4 hours and characterized by using various instrumental techniques. The structural studies of the samples were studied by using XRD and SEM. The field dependent magnetic properties of prepared Ba-Ca hexaferrite powder was investigated at room temperature by using vibrating sample magnetometer. It has been observed that the type of surfactant plays a crucial role in deciding the morphology of the particles. There is significant change in crystallite size of the resultant Ba-Ca hexaferrite prepared in presence of anionic surfactant sodium dodecyl sulfate (SDS) and reverse micelle route. The samples prepared in presence of cationic and non ionic surfactants show agglomerated large particles. Magnetic study reveals that the value of anisotropy constant (K) depends on the type of surfactant used. The sample prepared in presence of nonionic surfactant Polyethylene glycol sorbitan monooleate (Tween 80) shows low anisotropy constant (0.26×10P-3P HAP2P/kg) where as the sample prepared in presence of SDS surfactant exhibits high anisotropy constant (3.26×10P-3P HAP2P/kg) compared to normal sample (0.41 ×10P-3P HAP2P/kg)

Media optimization for biosurfactant production by Rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach

This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques-(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)-were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI24)] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were ~3 and ~6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were ~0.99 and ~3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield

A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli

ABSTRACT Motivation: Inclusion body formation has been a major deterrent for overexpression studies since a large number of proteins form insoluble inclusion bodies when overexpressed in Escherichia coli. The formation of inclusion bodies is known to be an outcome of improper protein folding; thus the composition and arrangement of amino acids in the proteins would be a major influencing factor in deciding its aggregation propensity. There is a significant need for a prediction algorithm that would enable the rational identification of both mutants and also the ideal protein candidates for mutations that would confer higher solubility-on-overexpression instead of the presently used trial-anderror procedures. Results: Six physicochemical properties together with residue and dipeptide-compositions have been used to develop a support vector machine-based classifier to predict the overexpression status in E.coli. The prediction accuracy is~72% suggesting that it performs reasonably well in predicting the propensity of a protein to be soluble or to form inclusion bodies. The algorithm could also correctly predict the change in solubility for most of the point mutations reported in literature. This algorithm can be a useful tool in screening protein libraries to identify soluble variants of proteins

Photocatalytic degradation of ciprofloxacin·HCl using Aeroxide® P-25 TiO2 photocatalyst: Comparative evaluation of solar and artificial radiation

The photocatalytic degradation of ciprofloxacin (CFX) has been investigated using Aeroxide® P-25 TiO2 photocatalyst in the presence of solar as well as artificial radiation. The effects of different operating parameters like initial concentration of CFX, catalyst loading, pH of solution and effect of co-existing ions on photocatalytic degradation of CFX have been investigated with a view to establish the optimum operating conditions. It is observed that as the initial concentration of CFX increases, the rate of photocatalytic degradation decreases. Optimum catalyst loading is observed at 1 g L-1 for CFX concentration of 100 mg L-1. Over the pH range 3-11, maximum degradation rate occurs at pH 9. The mechanism and intermediates formed during the photocatalytic degradation of CFX are discussed based on UPLC-MS/MS analysis. From kinetic studies, it is found that the photocatalytic degradation obeys pseudo-first order kinetics. The degradation rate constant using solar radiation is about 1.7 times higher than that under artificial radiation

Sulfur Nanoparticles Synthesis and Characterization from H2S Gas, Using Novel Biodegradable Iron Chelates in W/O Microemulsion

Sulfur nanoparticles were synthesized from hazardous H2S gas using novel biodegradable iron chelates in w/o microemulsion system. Fe3+–malic acid chelate (0.05 M aqueous solution) was studied in w/o microemulsion containing cyclohexane, Triton X-100 andn-hexanol as oil phase, surfactant, co-surfactant, respectively, for catalytic oxidation of H2S gas at ambient conditions of temperature, pressure, and neutral pH. The structural features of sulfur nanoparticles have been characterized by X-ray diffraction (XRD), transmission electron microscope (TEM), energy dispersive spectroscopy (EDS), diffused reflectance infra-red Fourier transform technique, and BET surface area measurements. XRD analysis indicates the presence of α-sulfur. TEM analysis shows that the morphology of sulfur nanoparticles synthesized in w/o microemulsion system is nearly uniform in size (average particle size 10 nm) and narrow particle size distribution (in range of 5–15 nm) as compared to that in aqueous surfactant systems. The EDS analysis indicated high purity of sulfur (>99%). Moreover, sulfur nanoparticles synthesized in w/o microemulsion system exhibit higher antimicrobial activity (against bacteria, yeast, and fungi) than that of colloidal sulfur