Simultaneous amperometric determination of lignin peroxidase and manganese peroxidase activities in compost bioremediation using artificial neural networks

Artificial neural networks in the modeling of the catalytic activity of a biosensor composed of conjugated polymers and urease

Thin films of conjugated polymer and enzyme can be used to unravel the interaction between components in a biosensor. Using artificial neural networks (ANNs) improves data interpretability and helps construct models with great capacity for classifying and processing information. The present work used kinetic data from the catalytic activity of urease immobilized in different conjugated polymers to create ANN models using time, substrate concentration, and absorbance as input variables since the models had absorbance in a posterior instant as output value to explore the predictivity of the ANNs. The performance of the models was evaluated by Pearson's correlation coefficient (ρ) and mean squared error (MSE) values. After the learning process, a series of new experiments were performed to verify the generality of the models. As the main results, the best ANN model presented 0.9980 and 3.0736 × 10-5 for ρ and MSE, respectively. For the simulation step, intermediary values of substrate concentration were used. The mean absolute percentage error (MAPE) values were 3.34, 3.07, and 3.78 for 12 mM, 22 mM, and 32 mM concentrations, respectively. Overall, with the simulations, it was possible to ascertain the interpolatory capacity of the model, which has a learning mechanism based on absorbance and time as variables. Thus, the potential of ANNs would be in their use in pre-evaluations, helping to determine the substrate concentration at which there is higher catalytic activity or in determining the linear range of the sensor.

Enhancing the compost maturation of swine manure and rice straw by applying bioaugmentation

Microorganisms capable of decomposing cellulose, xylan, starch and protein were individually isolated from swine manure compost and soil in this study. The correlations with pH, carbon source concentration, C/N ratio and enzyme activity among these isolated microorganisms were also investigated. Furthermore, the effect of additional inoculation in the compost was studied by measuring variations in the C/N ratio, enzyme activity and compost maturation rate. The inoculated microorganisms used in this study included four bacterial isolates and one commercial microorganism Phanerochaete chrysosporium. The results indicated that the isolated Kitasatospora phosalacinea strain C1, which is a cellulose-degraded microorganism, presented the highest enzyme activity at 31 ℃ and pH 5.5, while the C/N ratio was 0.8%. The isolated xylan-degraded microorganism Paenibacillus glycanilyticus X1 had the highest enzyme activity at 45 ℃ and pH 7.5, while the C/N ratio was 0.5%. The starch-degraded microorganism was identified as Bacillus licheniformis S3, and its highest enzyme activities were estimated to be 31 ℃ and pH 7.5 while the C/N ratio was 0.8%. The highest enzyme activity of the protein-degraded microorganism Brevinacillus agri E4 was obtained at 45 ℃ and pH 8.5, while the C/N ratio was 1.0%. The rate of temperature increase in the compost inoculated with P. chrysosporium was only higher than that of the compost without inoculation, and its compost maturation level was also lower than that of other composts with additional inoculation. The optimal initial C/N ratio of the compost was 27.5 and the final C/N ratio was 18.9. The composting results also indicated that the secondary inoculation would benefit compost maturation, and the lowest final C/N ratio of 17.0 was obtained.

A review of mathematical models for composting

Composting is a valuable method to treat and valorize organic waste. However, the process is defined by its dynamic nature and governed by a multitude of operating parameters. As such, mathematical modelling of the process offers a powerful tool to simulate and predict the variable outcomes of the process, allowing for its optimization. This can include improving efficiency, lowering costs and reducing environmental impact. To aid with the development of future models, we provide an up to date review and assessment on the state of the art of composting modelling. By reviewing 40 years of literature, this review paints the most complete picture of the field to date. This includes an analysis of trends in composting modelling: looking at the type of systems that are targeted, the aim of the models and the approaches to kinetics and mass and heat transfer. Regarding modelling approaches, we explore the fractionation of both substrates and microorganisms, the biological processes that can be included (disintegration, hydrolysis, uptake and death) and their kinetics (first-order, Monod-type), energy balances (biological generation, convection, conduction) and mass balances. We also provide a review of the results of sensitivity analyses performed on composting models, finding that models are most sensitive to microbial growth and death rates, as well as consumption rates and product yields. In the final portion of the review, we identify, explore, and provide guiding recommendations for work on emerging areas and areas requiring development in composting modelling (volume change, pH, maturation, artificial intelligence, etc.).

Influence of inoculating white-rot fungi on organic matter transformations and mobility of heavy metals in sewage sludge based composting

White-rot fungi, Phanerochaete chrysosporium was inoculated to sewage sludge composting. Its effect on transformation of organic matter and mobility of heavy metals (Zn, Pb, Cu, and Ni) was studied. Detailed sampling was performed to measure C contents in humic extracts (HE), humic acids (HAs), fulvic acids (FAs), humin and distribution of heavy metals, including acid exchangeable fraction (AE), reducible fraction (RED), oxidization fraction (OXI) and residual fraction (RES). In our study, it is evident that the HE, HAs increased obviously and hydrolyzed humin decreased markedly in inoculation. The stabilization rate ((OXI+RES)/(AE+RED)) of Zn, Pb, Cu, and Ni was 20.31%, 7%, 14.3% and 19.79% higher in inoculating reactor. Additionally, the changes of heavy metals fractions could be explained by the organic variables. The results of this study demonstrated that Phanerochaete chrysosporium passivates the heavy metal by provoking the formation of humus.

Lignin degradation in corn stalk by combined method of H2O2 hydrolysis and Aspergillus oryzae CGMCC5992 liquid-state fermentation

BACKGROUND

Lignin peroxidase (LiP) is the primary enzyme responsible for lignin degradation. In our previous work, in order to shorten the pretreatment time and increase the lignin degradation, we have pretreated the corn stalk (CS) using a combination of Aspergillus oryzae CGMCC 5992 solid-state fermentation and H2O2 treatment.

RESULTS

In the present study, one-factor-at-a-time design and response surface design were applied to optimize the nutritional constituents for LiP production in liquid-state fermentation by A. oryzae CGMCC 5992 and the conditions for CS degradation by A. oryzae CGMCC 5992. The optimal medium included CS of 30 g/L, glucose of 4.6 g/L, sodium nitrate of 1.2 g/L, corn steep liquor of 1 g/L, yeast extract of 1.2 g/L, and vitamin B1 of 0.15 g/L. Under these optimal conditions, the LiP production reached its maximum of 652.34 U/L. The optimal condition for CS degradation included CS of 20 g, A. oryzae CGMCC 5992 broth of 50 mL, 1.5 % H2O2 solution of 80 mL, H2O2 flow rate of 0.4 mL/min, water volume of 240 mL (water/material ratio of 12:1), hydrolysis temperature of 39 °C, and hydrolysis time of 8 h. Before hydrolysis, CS and water were pretreated at 113 °C for 11 min. Under these optimal conditions, the sugar yield reached its maximum of 46.28 %.

CONCLUSIONS

Our newly developed method had great advantages in pretreatment of CS due to its quickness, convenience, safety, no special equipment and high sugar yield.Graphical abstractThe schematic diagram of corn straw hydrolysis.

Study on biodegradation process of lignin by FTIR and DSC

The biodegradation process of lignin by Penicillium simplicissimum was studied to reveal the lignin biodegradation mechanisms. The biodegradation products of lignin were detected using Fourier transform infrared spectroscopy (FTIR), UV-Vis spectrophotometer, different scanning calorimeter (DSC), and stereoscopic microscope. The analysis of FTIR spectrum showed the cleavage of various ether linkages (1,365 and 1,110 cm(-1)), oxidation, and demethylation (2,847 cm(-1)) by comparing the different peak values in the corresponding curve of each sample. Moreover, the differences (Tm and ΔHm values) between the DSC curves indirectly verified the FTIR analysis of biodegradation process. In addition, the effects of adding hydrogen peroxide (H2O2) to lignin biodegradation process were analyzed, which indicated that H2O2 could accelerate the secretion of the MnP and LiP and improve the enzymes activity. What is more, lignin peroxidase and manganese peroxidase catalyzed the lignin degradation effectively only when H2O2 was presented.

Experimental and modeling approaches for food waste composting: a review

Composting has been used as a method to dispose food waste (FW) and recycle organic matter to improve soil structure and fertility. Considering the significance of composting in FW treatment, many researchers have paid their attention on how to improve FW composting efficiency, reduce operating cost, and mitigate the associated environmental damage. This review focuses on the overall studies of FW composting, not only various parameters significantly affecting the processes and final results, but also a number of simulation approaches that are greatly instrumental in well understanding the process mechanism and/or results prediction. Implications of many key ingredients on FW composting performance are also discussed. Perspects of effective laboratory experiments and computer-based simulation are finally investigated, demonstrating many demanding areas for enhanced research efforts, which include the screening of multi-functional additives, volatile organiccompound emission control, necessity of modeling and post-modeling analysis, and usefulness of developing more conjunctive AI-based process control techniques.

Optical detection of NADH based on biocatalytic growth of Au-Ag core-shell nanoparticles

We have developed an optical assay for NADH (Dihydronicotinamide adenine dinucleotide) based on the catalytic growth of gold-silver core-shell nanoparticles (Au-Ag-CSNPs). The nanoparticles were immobilized on pretreated glass slide and are shown to catalyze the NADH-mediated reduction of Ag(I) ions in the presence of 1,4-benzoquinone and cetyltrimethyl ammonium ion. This leads to the formation of Au-Ag-CSNPs on the glass. The absorption peak of the Au-Ag-CSNPs at 415 nm increases with the concentration of NADH in the solution used, and this can be measured by UV-vis photometry. High-resolution scanning electron microscopy analysis of the morphology of the surface of the Au-Ag-CSNPs before and after the catalytic reaction revealed a growth of their diameter. Under optimal conditions, NADH can be determined in the concentration range from 0.2 to 3.2mM, and the detection limit is 15.6 μM. The sensor has good precision and good storage stability, simple in operation, and can be fabricated at low costs, which made it suitable for the determination of NADH in complex biological systems and in related degradation processes of contaminants.

Immobilization of laccase on magnetic bimodal mesoporous carbon and the application in the removal of phenolic compounds

A novel magnetically separable laccase immobilized system was constructed by adsorbing laccase into bimodal carbon-based mesoporous magnetic composites (CMMC). A large adsorption capacity (491.7 mg g(-1)), excellent activity recovery (91.0%) and broader pH and temperature profiles than free laccase have been exhibited by the immobilized laccase. Thermal stability was enhanced to a great extent and operational stability was increased to a certain extent. The shift of kinetic parameters indicated affinity change between enzyme and substrate. Application of the immobilized system in phenol and p-chlorophenol removal was investigated in a batch system. Adsorption effects of the support were responsible for the quick removal rate in the first hour, and up to 78% and 84% of phenol and p-chlorophenol were removed in the end of the reaction, respectively, indicating that the magnetic bimodal mesoporous carbon is a promising carrier for both immobilization of laccase and further application in phenol removal.

Biodelignification of rice straw by Phanerochaete chrysosporium in the presence of dirhamnolipid

Lignin degradation by white-rot fungi has received considerable attention as a means for reducing accumulation of lignocellulosic wastes in the environment. The stimulatory effect of surfactants on fungal lignocellulose bioconversion also has attracted wide interest. In this study the influence of dirhamnolipid biosurfactant on biodegradation of rice straw by Phanerochaete chrysosporium was investigated. It was shown that the biodelignification process of rice straw can be significantly enhanced by the presence of dirhamnolipid biosurfactant. In particular, the dirhamnolipid at the concentration of 0.007% increased the peak activity of lignin peroxidase (LiP) by 86% without affecting the manganese peroxidase (MnP) activity. The water-soluble organic carbon (WSOC) contents in the straw substrates as well as the microbial growth and activity were effectively improved by dirhamnolipid, while the degradation rate of lignin increased by 54% with dirhamnolipid of 0.007%. Observed chemical structural and morphological changes showed that the straw substrates were delignified in the presence of dirhamnolipid with the formation of terrace-like fragments separated from the inner cellular fibers and the release of simple compounds. Variation partitioning analysis revealed that the dirhamnolipid addition induced a significant straw biodelignification which explained 22.1% (P = 0.013) of the variance.

Sensitive detection of lip genes by electrochemical DNA sensor and its application in polymerase chain reaction amplicons from Phanerochaete chrysosporium

An electrochemical DNA sensor based on the sandwich hybridization recognition of target sequence of lignin peroxidase (lip) genes on a gold electrode was developed. A monolayer of thiolated capture probe was formed on a gold electrode through self-assembling. Following hybridizations with target nucleic acid and biotinylated detection probe, streptavidin-horseradish peroxidase (HRP) conjugate was applied to the electrode. The DNA conformation and surface coverage on electrode were characterized by impedance spectroscopy and square wave voltammetry. The experimental variables were optimized to maximize the hybridization efficiency, detection sensitivity and speed up the assay time. The amperometric current response to HRP-catalyzed reaction was linearly related to the natural logarithm of the target nucleic acid concentration in the range from 0.6 to 30 nM, with the correlation coefficient of 0.9722. The detection limit was 0.03 nM. Synthesized oligonucleotide as well as Phanerochaete chrysosporium lip gene fragments amplified using polymerase chain reaction and digested by restriction endonucleases were tested. The DNA sensor exhibited good precision, stability, sensitivity, and selectivity, and discriminated satisfactorily against mismatched nucleic acid samples of similar lengths.