What question did this study set out to answer?

To identify dye-decolorizing bacteria from textile effluents and optimize the decolorization process.

February 19, 2026Water Practice & TechnologyOpen Access

Identification of dye-decolorizing bacteria from textile industry effluents and optimization of decolorization process using response surface methodology

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Research identifies effective bacteria for dye removal in textile pollution, suggesting sustainable bioremediation strategies.

To identify dye-decolorizing bacteria from textile effluents and optimize the decolorization process.
Isolated bacterial strain S-3 from textile effluent
Tested dye decolorization activity on various dyes
Conducted 16S rRNA gene sequencing for identification
Used response surface methodology to optimize pH, temperature, and dye concentration
Developed quadratic regression models for decolorization efficiency
Strain S-3 achieved over 85% decolorization of Reactive Red, Reactive Yellow, and Basic Blue within 48 hours
Identified as Pseudomonas aeruginosa with 97.49% 16S rRNA similarity
Found optimal conditions: pH 7.2-7.6, temperature 31.8-34 °C, dye concentration 56.6-72.4 ppm
Models showed strong correlation with experimental data (R2 = 0.9-1.0)

Noman et al. (2026) studied this question.

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February 19, 2026Water Practice & TechnologyOpen Access

View Full Paper

Ask AI

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Authors

Research identifies effective bacteria for dye removal in textile pollution, suggesting sustainable bioremediation strategies.

To identify dye-decolorizing bacteria from textile effluents and optimize the decolorization process.
Isolated bacterial strain S-3 from textile effluent
Tested dye decolorization activity on various dyes
Conducted 16S rRNA gene sequencing for identification
Used response surface methodology to optimize pH, temperature, and dye concentration
Developed quadratic regression models for decolorization efficiency
Strain S-3 achieved over 85% decolorization of Reactive Red, Reactive Yellow, and Basic Blue within 48 hours
Identified as Pseudomonas aeruginosa with 97.49% 16S rRNA similarity
Found optimal conditions: pH 7.2-7.6, temperature 31.8-34 °C, dye concentration 56.6-72.4 ppm
Models showed strong correlation with experimental data (R2 = 0.9-1.0)

Noman et al. (2026) studied this question.

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