Solid-State Fermentation as a Novel Paradigm for Organic Waste Valorization : a Review

The abundance of organic solid waste throughout the world has become a common issue that needs complete management at every level. Also, the scarcity of fuel and the competition between food and substance as an alternative to a petroleum-based product has become a major problem that needs to be properly handled. An urge to find renewable substances for sustainable development results in a strategy to valorize organic solid waste using solid state fermentation (SSF) and to manage the issue of solid wastes in a green approach. This paper reviews management of solid wastes using SSF, with regard to its current application, advantages and challenges, downstream processing in SSF, economic viewpoint, and future perspectives

R software package based statistical optimization of process components to simultaneously enhance the bacterial growth, laccase production and textile dye decolorization with cytotoxicity study.

The thermophilic bacterium, Bacillus licheniformis U1 is used for the optimization of bacterial growth (R1), laccase production (R2) and synthetic disperse blue DBR textile dye decolorization (R3) in the present study. Preliminary optimization has been performed by one variable at time (OVAT) approach using four media components viz., dye concentration, copper sulphate concentration, pH, and inoculum size. Based on OVAT result further statistical optimization of R1, R2 and R3 performed by Box-Behnken design (BBD) using response surface methodology (RSM) in R software with R Commander package. The total 29 experimental runs conducted in the experimental design study towards the construction of a quadratic model. The model indicated that dye concentration 110 ppm, copper sulphate 0.2 mM, pH 7.5 and inoculum size 6% v/v were found to be optimum to maximize the laccase production and bacterial growth. Whereas, maximum dye decolorization achieved in media containing dye concentration 110 ppm, copper sulphate 0.6 mM, pH 6 and inoculum size 6% v/v. R package predicted R2 of R1, R2 and R3 were 0.9917, 0.9831 and 0.9703 respectively; likened to Design-Expert (Stat-Ease) (DOE) predicted R2 of R1, R2, and R3 were 0.9893, 0.9822 and 0.8442 respectively. The values obtained by R software were more precise, reliable and reproducible, compared to the DOE model. The laccase production was 1.80 fold increased, and 2.24 fold enhancement in dye decolorization was achieved using optimized medium than initial experiments. Moreover, the laccase-treated sample demonstrated the less cytotoxic effect on L132 and MCF-7 cell lines compared to untreated sample using MTT assay. Higher cell viability and lower cytotoxicity observed in a laccase-treated sample suggest the impending application of bacterial laccase in the reduction of toxicity of dye to design rapid biodegradation process

Metagenomic data of fungal internal transcribed Spacer and 18S rRNA gene sequences from Lonar lake sediment, India

The data in this article contains the sequences of fungal Internal Transcribed Spacer (ITS) and 18S rRNA gene from a metagenome of Lonar soda lake, India. Sequences were amplified using fungal specific primers, which amplified the amplicon lined between the 18S and 28S rRNA genes. Data were obtained using Fungal tag-encoded FLX amplicon pyrosequencing (fTEFAP) technique and used to analyze fungal profile by the culture-independent method. Primary analysis using PlutoF 454 pipeline suggests the Lonar lake mycobiome contained the 29 different fungal species. The raw sequencing data used to perform this analysis along with FASTQ file are located in the NCBI Sequence Read Archive (SRA) under accession No. SRX889598 (http://www.ncbi.nlm.nih.gov/sra/SRX889598)

Contour plots show the response surface effect of interaction on dye decolorization.

<p>(A) X₁ with X₃, and (B) X₂ with X₄.</p

Optimized medium component for validation of a model for responses R1, R2, and R3.

<p>Optimized medium component for validation of a model for responses R1, R2, and R3.</p

ANOVA of responses R1, R2, and R3 for BBD of process parameters using R software.

<p>ANOVA of responses R1, R2, and R3 for BBD of process parameters using R software.</p

Contour plots show the response surface effect of interaction on laccase activity.

<p>(A) X₁ with X₃, and (B) X₂ with X₄.</p

Comparison of predictive capability between R Software package and DOE.

<p>Comparison of predictive capability between R Software package and DOE.</p

Contour plots show the response surface effect of interaction on the growth of isolates.

<p>(A) X₁ with X₂, and (B) X₁ with X₄.</p