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

Abstract

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.

Microbial contamination screening and interpretation for biological laboratory environments

“…Biome summarize-table for counting the number, average number, and total number of sequences contained in each sample, alpha_diversity.py and beta_diversity_through_plots.py for analyzing the diversity of samples. Statistical analysis and visualization were then performed in R (https://www.r-project.org/)[17] using the package ggplot2. We then used SPSS (https://www.ibm.com/analytics/datascience-/predictive-analytics/spss)[18] to perform ANOVA on the alpha diversity results of samples to compare the difference of microbial community composition among the three sites.…”

Optimization of ultimate tensile strength of welded Inconel 625 and duplex 2205

“…This is accomplished by separating the total variability of the S/N ratios, which is measured by the sum of the squared deviations from the total mean of the S/N ratio, into contributions by each welding process parameter and the error. The test for significance of the regression model, the test for significance on individual model coefficients and the lack-of-fit test were performed using Design Expert 7 software [15]. ANOVA tables summarise the analysis of three variances of the responses and show the significant models [16].…”

Photocatalytic degradation of swine wastewater on aqueous TiO2 suspensions: optimization and modeling via Box-Behnken design

“…Each selected factor was studied at three levels, low (-1), medium (0), and high (þ1), for a total of 15 experiments (Table 1). R open-source software with R Commander (Rcmdr) package [25] was used for BBD design and statistical analysis of the experimental data as demonstrated by [26]. Functional relationships between response (Y) and the set of factors (X 1 , X 2 , and X 3 ) were inferred by fitting a second-order polynomial quadratic model of Eq.…”