Innovative Bacterial Consortia for Simulated Dairy Wastewater Treatment: Improving COD Removal Efficiency

Innovative Bacterial Consortia for Simulated Dairy Wastewater Treatment: Improving COD Removal Efficiency | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Innovative Bacterial Consortia for Simulated Dairy Wastewater Treatment: Improving COD Removal Efficiency Manjiri Patil, Pranav Kshirsagar, Prashant Dhakephalkar, Suneeti Gore, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6410986/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract The dairy industry generates wastewater characterized by organic components, predominantly composed of proteins and fats, which can be effectively treated through biological processes. The present study aimed to develop a bacterial consortium for bioaugmentation to enhance the treatment of simulated dairy wastewater. A total of 75 bacterial isolates were obtained using Direct Isolation (DI) and Enrichment Isolation (EI) methods. Among these, four strains exhibiting the highest proteolytic and lipolytic activities within 24 hours were selected for further investigation. The isolates were screened based on their extracellular enzyme activities (proteinase and lipase), as well as their maximum lipolytic (0.3–0.7 mm/h) and proteolytic activity (0.67–0.83 mm/h) by a novel approach of rate of diffusion on Tributyrin Agar (TA) and Modified Skimmed Milk Agar (MSMA), respectively. The selected strains were identified by 16S rRNA gene sequencing as Massilia haematophila (DSSC1) , Brevibacillus agri (ENAT1) , Pseudomonas guguanensis (ENOG5) , and Lysinibacillus fusiformis (ETOG2 ) . The biodegradation potential of individual strains and their consortium was assessed through Chemical Oxygen Demand (COD) reduction in simulated dairy wastewater. The individual bacterial strains achieved COD reductions from an initial concentration of 3815 mg/L to 2950, 2813, 2480, and 2893 mg/L. In contrast, bioaugmentation with the bacterial consortia reduced COD to 2190 mg/L, resulting in a 26–86% higher reduction compared to the individual strains. This study presents the first report on the use of a novel approach of diffusion-based assay to develop an effective and innovative bacterial consortium for efficient dairy wastewater treatment. These findings highlight the potential of this approach towards enhancing biodegradation efficiency and advancing sustainable wastewater management practice. Dairy Wastewater Lipolytic Proteolytic consortium bioaugmentation COD reduction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Dairies are places where raw milk is processed either for direct consumption or utilized as a raw material for allied industries [ 1 ]. Dairy is among the most significant sectors of agricultural industries, as milk is an important food in our daily lives. Even so, they encounter challenges in the sustainable production of milk, especially in water conservation, energy-efficient industry, and waste management [ 2 ]. The milk and dairy products processing industries represent important commodities that demand huge amounts of water for processing and cleaning [ 3 ]. Wastewater is generated from cleaning and washing machinery, equipment like cans or tankers of the milk processing plant, floor washing, spillage and leakages of milk and milk products, boiler blowdown, softener regeneration water, bleed from the condenser, etc. Wastewater is generated in batches as the cleaning and washing take place after the complete production of each batch [ 4 ]. It is a highly polluting industry in terms of both the quantity and quality of wastewater it generates. The wastewater generated is about 0.2 to 10 L L − 1 of milk processed [ 5 ]. It is estimated that approximately 2% of processed milk is lost within the wastewater stream. Wastewater from dairy is characterized by a high organic load, which contains mainly milk fats, casein, lactose, inorganic salts, detergents, and sanitizers used for washing [ 6 ]. As dairy generates highly biodegradable wastewater, biological treatment strategies will be more effective. The degradation of organic pollutants by microorganisms is an easy, uncomplicated, cost-effective, and eco-friendly process [ 7 ]. There are aerobic and anaerobic biological treatments present to treat dairy wastewater. Most conventional aerobic biological processes, such as the activated sludge process and sequential batch reactor, are based on extended aeration, which requires more hydraulic retention time. Hence, its major drawbacks are high energy consumption and land requirements. Also, as there is quantitative and qualitative variation in the composition of influent generated by dairy industries, the failure rate of conventional biological treatment is higher [ 8 ]. Conversely, anaerobic biological processes also present several limitations for the treatment of dairy wastewater, such as due to the presence of oil and fats, less transfer through the membrane–aqueous interface, contributing to foul odour and blockages in the reactor, which ultimately reduces the efficiency of the reactor [ 9 ]. Additionally, the hydrolysis of lipids in an anaerobic reactor leads to the formation of long-chain fatty acids, which hinder methane production [ 10 ]. However, anaerobic treatments are very sensitive to their operating conditions, like organic loading, pH, etc., and they cannot tolerate shock loads. This states that anaerobic biological treatments are unfavorable for dairy wastewater treatment. Also, in a comparative study conducted by Custodio et al. [ 11 ] showed that aerobic treatment had a higher COD removal efficiency (80%) than anaerobic processes (58.6%) for dairy wastewater treatment. Despite these disadvantages, most research has predominantly focused on anaerobic reactors for dairy wastewater treatment [ 12 – 17 ]. It shows the need for research in advance aerobic biological treatment processes like bioaugmentation for dairy effluent. It has been observed that the efficiency of any biological treatment process not only depends on the characteristics and concentration of the constituents present in the effluent but also on the concentration and type of bacteria present in the effluent [ 18 ]. For the efficient design of the treatment process, one must know the micro-biota present in the wastewater, the biochemical and metabolic characteristics of organisms, and the genesis of pollutants [ 19 ]. However, the biodegradation activity by inherent bacteria might not be adequate to achieve an efficient and reliable treatment process [ 20 ]. Microbial engineering, like bioaugmentation and bioremediation, by utilizing microorganisms and genetic engineering approaches, increases the effectiveness of treatment procedures [ 21 ]. The introduction of highly efficient microbial consortia for wastewater bioremediation has shown promising results in improving water quality and makes it suitable for safe disposal or reuse by reducing key contaminants such as chemical oxygen demand (COD), and reducing sludge volume without causing adverse consequences. Velmurugan & Pandian [ 3 ] highlighted the economic benefits of microbial bioaugmentation which reduces sludge generation, 50% cost in transportation and disposal of sludge; the cost of chemicals and energy required for disinfection by bypassing the disinfection process post effluent treatment. Further, if bioaugmentation of bacterial consortia is with high substrate–specific biodegradation capacity then it enhances the efficiency of the treatment process [ 22 ]. Importantly, these selected microorganisms should exhibit resilience to the concentration of polluting constituents and able to degrade these contaminants [ 19 ]. Hence, bio-augmentation of microorganisms derived from a similar habitat presents significant benefits, as they can readily adapt to the environmental conditions and function concurrently with the indigenous microflora [ 8 ]. Also, as stated by Mazzucotelli et al. [ 23 ], microorganisms that exhibit two or more hydrolytic capacities are more efficient resources for the treatment of industrial waste. However, limited studies have explored the development of consortia composed of substrate-specific bacteria with two hydrolytic capacities for biological treatment [ 20 , 23 , 24 ]. Also, despite the significant advantages of bioaugmentation of consortia, there is a lack of comprehensive understanding of a systematic approach for the selection and development of consortia for dairy effluent treatment. In this context, the objective of the present research work was to develop an efficient bacterial consortium possessing hydrolytic (lipolytic and proteolytic) capacities for effective biological treatment of simulated dairy wastewater. The work involved isolating, characterizing, and screening lipase- and proteinase- producing bacterial strains from dairy effluent. A novel approach to the diffusion of enzymes through agar was adopted to confirm the maximum degradation capacities of bacteria. Based on this dual hydrolytic activity, the four most potent bacteria were selected and identified. The biodegradation capacity of these strains, individually and in consortia, was subsequently evaluated based on their COD removal efficacies. 2. Materials and Methods 2.1 Sampling of dairy effluent Wastewater samples were obtained from a dairy effluent treatment plant (DETP), located in Pune, Maharashtra. The samples were obtained at different stages of DETP, namely the screen chamber (SC), oil and grease outlet (O&G), equalization tank (ET), and return activated sludge (RAS). A total of 16 samples (4 from each stage) were obtained in 50 mL sterile falcon tubes and immediately brought to the laboratory at a controlled temperature. Further, the samples were used for the evaluation of physicochemical characteristics, direct isolation, and enrichment of bacterial cultures. The samples were stored at 4°C until they were used for further studies. 2.2 Wastewater analysis Dairy wastewater samples used to obtain isolates were analyzed to check their physico-chemical characteristics. The parameters that were analyzed included pH, temperature, colour, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), oil and grease (O&G), total solids (TS), total suspended solids (TSS), total volatile solids (TVS), volatile suspended solids (VSS), protein, sugar, total carbon, and total nitrogen. The pretreatment of samples and analytical methods used for analysis are mentioned in Table 1 . Table 1 Analytical methods used for wastewater characterization. Sr. No. Parameter analyzed The method used for the analysis Pretreatment of sample Reference / Instrument used 1. pH Potentiometric Raw Sample pH Meter / [ 34 ] 4500-H + B. 2. Temperature By using electrode Raw Sample pH Meter / [ 34 ] 4500-H + B. 3. Colour Physical Observation Raw Sample - 4. Dissolved Oxygen (DO) Titrimetric Samples pH were adjusted to 6.5–7.5 [ 34 ], 4500-O C. 5. Biological Oxygen Demand (BOD) Titrimetric Samples pH were adjusted to 6.5–7.5 [ 34 ], 5210 B. 6. Chemical Oxygen Demand (COD) Titrimetric Raw Sample [ 34 ], 5220 B (modified for a smaller volume of sample) 7. Oil and Grease Gravimetric Samples pH were adjusted to < 2 [ 34 ], 5520 B 8. Total Solids Gravimetric Raw Sample [ 34 ], 2540 B. 9. Total Suspended Solids (TSS) Gravimetric Raw Sample [ 34 ], 2540 D. 10. Total Volatile Solids (TVS) Gravimetric Raw Sample [ 34 ], 2540 E. 11. Volatile Suspended Solids (VSS) Gravimetric Raw Sample [ 34 ], 2540 E. 12. Protein Colorimetric Samples were centrifuged and filtered through a 0.2 µm filter Biuret colorimetric method [ 49 ] 13. Sugar Colorimetric Samples were centrifuged and filtered through a 0.2 µm filter DNSA and Anthron Method 14. Carbon Combustion Raw Sample CHNS Method 15. Nitrogen Combustion Raw Sample CHNS Method 2.3 Isolation & characterization of the bacteria The bacterial isolates were obtained by direct and enrichment isolation strategies [ 8 ]. The bacteria were isolated using a) Nutrient Agar (NA) [ 25 ], b) MacConkey’s Agar (MA), c) Tributyrin Agar (TA) [ 18 ], and d) Modified Skimmed Milk Agar (MSMA) media. The MSM was prepared as per Raj et al. [ 26 ] along with the addition of 2.5 g/L yeast extract powder. In the direct isolation method, collected samples were directly streaked on the above-stated four media supplemented with 2% agar-agar. In the enrichment-isolation method, 10% (V/V) samples were inoculated into the Erlenmeyer flask containing enrichment media (broth of above-stated media) and kept for incubation at 37°C up to 96 h. After enrichment, these samples were streaked on respective agar media. The agar plates were incubated at 37°C for up to 96 h. Morphologically different bacterial colonies were isolated and studied for colony morphological characteristics (size, shape, colour, margin, elevation, opacity, and consistency) [ 27 ]. Further, colonies showing a zone of clearance were isolated from TA and MSMA. All the obtained isolates were characterized for their cell morphology (cell shape, arrangement, motility, and Gram staining) [ 27 ]. The purity of isolates was confirmed by streaking isolates on nutrient agar media and was stored at 4℃ on nutrient agar slants [ 28 ]. 2.4 Development of a bacterial consortium through a three stage strategic screening 2.4.1 Screening of lipolytic and proteolytic bacteria As dairy wastewater contains higher concentrations of lipids (milk fats) and proteins (milk casein), the screening was accomplished based on lipolytic and proteolytic activity on TA and MSMA, respectively. A loopful of active (12 to 24 h old) bacterial culture was spot inoculated on TA and MSMA [ 28 ]. After incubation at 37°C for up to 48 to 96 h, isolates showing a zone of clearance on either one of the media were examined further for secondary quantitative analysis [ 29 ]. Similarly, isolates obtained from NA and MA were inoculated on TA as well as MSMA, whereas the isolates obtained on TA were inoculated only on MSMA, and the isolates obtained from MSMA were inoculated only on TA to test their lipolytic and proteolytic ability. 2.4.2 Secondary quantitative screening of lipolytic and proteolytic bacteria 2.4.2.1 Inoculum preparation The active bacterial cultures having equal cell density (adjusted to the same OD at A 600 ) were inoculated in nutrient broth and incubated for 24 h at 37°C. Post-incubation, cell-free supernatant was obtained by centrifugation at 10,000 rpm for 10 minutes at 4°C. This cell-free supernatant was employed as a source of crude enzyme extract (CEE) [ 23 ]. 2.4.2.2 Extracellular cell-free enzyme activity assay On TA and MSMA, 5-mm diameter bore wells were punched using a sterile cork borer under sterile conditions. The CEE (100 µL) was added to the well on agar plates and incubated at 37°C for 24 h. After incubation, the diameter of the zone of clearance (in mm) around the well was measured [ 30 ]. The zone of clearance around the well on TA and MSMA indicates positive extracellular lipase and protease activity, respectively. 2.4.3 Kinetics of lipid and protein degradation Isolates that were screened based on extracellular enzymatic activity were subjected to diffusion assay. For this assay, 24-h-old cultural broths were adjusted to the same cell density at A 600 nm. 10 µL of the broth was inoculated at the center of the petri plates containing TA or MSMA and incubated at 37°C. The diameter of the zone of clearance was measured every 6 h until it showed no further diffusion. The experiment was conducted in duplicate. The rate of diffusion was calculated as per expression 1. r d = \(\:\:\:\:\frac{\text{d}\text{D}\:}{\text{d}\text{T}}\) (1) Where, r d – rate of diffusion in mm/h dD – Difference in diameter of zone of clearance within time T in mm dT – Difference in time within which the zone of clearance was measured in h 2.5 Identification of the selected bacterial isolates The overnight-grown cells in nutrient broth were used to extract DNA using a Sigma GenElute Bacterial Genomic DNA kit as per the manufacturer's protocol. The quality and intactness of DNA were checked by running DNA extraction on 0.8% agarose gel. PCR amplification of the 16S rRNA gene was done by using GT-PCR master mix of Takara Emerald ® . The universal primers (forward primer 27 F-AGAGTTTGATCMTGGCTCAG and reverse primer 1492 R - TACGGYTACCTTGTTACGACTT) were used for the PCR reaction mixture. The PCR reaction was performed with the following conditions: Initial denaturation was done at 95°C for 5 minutes, followed by 35 amplification cycles at 94°C for 1 minute, the annealing temperature of primers was 55°C for 1 minute, and extension at 72°C for 1.30 minutes. The final extension was done at 72°C for 10 minutes. The quality of the PCR product was checked by running the product on 1.5% agarose gel. The resulting PCR products were purified using the FavorPrem™ GEL/PCR purification kit as per the manufacturer's protocol. The PCR-purified product was sequenced using the Applied Biosystems® BigDyeTM Terminator Cycle Sequencing Kit. The sequencing reaction was performed with the following conditions: Initial denaturation was done at 96°C for 1 minute; subsequently, 25 amplification cycles were done at 96°C for 10 seconds, the annealing temperature of primers was 50°C for 5 seconds and extension was at 60°C for 2 minutes. The final extension was done at 4°C. The cleanup was performed using SAM’s Xterminator solution and further loaded for Sanger sequencing in the Seg Studio Genome Analyzer, Thermo Fisher. The National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST) similarity search tool was used to compare obtained 16S rRNA gene sequences. 2.6 Growth curve and doubling time of the screened cultures Log phase growing cells of selected cultures were inoculated in sterile (autoclaved at 121°C for 20 minutes) nutrient broth (pH 7) and adjusted to the same optical density at A 600 . These cultures were incubated at 25 and 37°C. The absorbance at 600 nm was noted down every 30 minutes by using the spectrophotometer DR 2700 [ 31 ]. Specific growth rate and doubling time were calculated by expressions 2 and 3, respectively [ 32 ]. Specific growth rate, µ/min= \(\:\frac{(\text{Log}\text{10}\:\text{Z}-\:\text{Log}\text{10}\text{Z}\text{0})\text{*}2.303)\:}{\left(\:\text{T}-\text{T}\text{0}\right)}\) (2) Doubling Time (h) = \(\:\frac{0.693\:\:\:\:\:}{\text{S}\text{p}\text{e}\text{c}\text{i}\text{f}\text{i}\text{c}\:\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}\:\text{r}\text{a}\text{t}\text{e}\:}\) (3) Where, Z - optical density at the end of the exponential phase of the growth Z 0 - optical density at the beginning of an exponential phase of the growth T- time at the end of the exponential phase of the growth, h T 0 - time at the beginning of an exponential phase of the growth, h 2.7 Enzyme activity of selected bacteria 2.7.1 Lipase activity assay The lipase activity was computed by the spectrophotometric method described by [ 18 ] with a few modifications. The 24-h-old cultural broth was centrifuged at 10,000 rpm for 10 minutes at 4°C to obtain cell-free supernatant (CFS). For the lipase assay, the mixture of 0.5 mL supernatant, 0.5 mL of 0.02 M phosphate buffer of pH 7.0, and 1 mL of the substrate (8 mM p-Nitrophenyl Laurate in isopropanol) was prepared and incubated at 37℃ for 15 & 30 minutes of the time intervals. After incubation, 0.5 mL of 3 M HCl was added to stop the reaction. The reaction mixture was centrifuged at 10,000 rpm for 10 minutes at 4℃ and 1 mL of supernatant was collected in a test tube. The pH of the supernatant was made alkaline by adding 1 mL of 2 M NaOH, and absorbance was noted at 420 nm. Reagent blank (2 mL phosphate buffer), substrate blank (1 mL of phosphate buffer + 1 mL of the substrate), and enzyme blank (0.5 mL of supernatant of tributyrin broth without strain + 0.5 mL of phosphate buffer + 1 mL of substrate solution) were also conducted similarly. The lipase concentrations from the strains were determined by using the p-NP (p-Nitrophenol) standard curve, which was derived by using different volumes of 1 mM p-NP solution. Unit enzyme activity was calculated as the amount of enzyme required to release 1 mM of p-NP from p-NPL in one minute. 2.7.2 Protease activity assay The protease activity was computed by the spectrophotometric method described by [ 33 ] with few modifications. Following 24 hours of incubation, CFS was obtained by centrifugation of bacterial culture broth at 10,000 rpm at 4ºC for 10 minutes. The substrate solution contains 2% (w/v) casein in 0.02M phosphate buffer of pH 7. To 1 mL of substrate solution, 1 mL of enzyme solution was added, and the mixture was incubated at 37℃ for 30 and 60 minute time intervals. Further, 2 mL of 10% (w/v) trichloroacetic acid (TCA) was added to stop the reaction. The supernatant was obtained by centrifugation of the reaction mixture at 3000 rpm for 10 minutes at 4℃. The 2.5 mL of 0.4 M Na 2 CO 3 and 0.5 mL Folin’s-Ciocalteu reagent were mixed with 0.5 mL of the supernatant. After mixing, it was incubated for 20 minutes at 40°C and cooled by using an ice bath, and the absorbance value was measured at 680 nm. Reagent blank (2 mL phosphate buffer), substrate blank (1 mL of substrate solution + 1 mL of 0.02 M phosphate buffer), and enzyme blank (1 mL of supernatant of skimmed milk broth without strain + 1 mL of substrate solution) were also conducted similarly. The tyrosine standard curve was obtained by using different volumes of 1000 µM tyrosine solution and was used as a reference to measure the concentration of protease from the strains. One unit of protease activity was defined as the amount of protease capable of hydrolyzing casein to generate 1 µg tyrosine under the above conditions. The protease activity was represented in the units of U/mL and U/mg, corresponding to the ratio of protease activity to the amount of protease solution and the protease activity per milligram protein, respectively. 2.8 Evaluation of biodegradation capacities based on the percentage of COD reduction: 2.8.1 Inoculum preparation A 10% (V/V) of freshly grown cultures were inoculated in a test tube containing nutrient broth. The test tubes were incubated at 37℃ for 24 hrs. Subsequently, the cultures were mixed together in equal proportion to form a consortium. The optical density of individual inoculum and of a consortium was measured at A 600 . 2.8.2 Evaluation of degradation capacity of strains The percentage of biodegradation efficacy of selected strains was evaluated based on COD reduction. Degradation studies were carried out in the test tubes containing simulated dairy wastewater (based on the equalization tank characteristics presented in Table 2 ). The simulated dairy wastewater was prepared by using 65 times diluted milk to a suspended solid concentration of 1885 mg/L, which was supplemented by tributyrin oil (to adjust C/N 24) and HCl/NaOH (to adjust pH 7). The inoculum (10% V/V) of the isolates and a consortium was added into the medium and incubated at 37°C for up to 24 h. The test tubes containing the un-inoculated medium were kept as a control. The Closed Reflux, Colorimetric Method (5220 D) [ 34 ] was used to measure COD. The CFS for COD analysis was obtained by centrifuging aliquots of samples at 10,000 rpm for 10 minutes at 4°C. All the experiments were conducted in triplicate, and the standard deviation was determined for confirmation of the reproducibility of the experimental trials. Table 2 Characteristics of dairy wastewater Parameters Screen Chamber Oil and Grease Outlet Equalization Tank Return Activated Sludge pH* 11 12 9 8 Temp 32 31 29 26 Colour Milky White Milky White Milky White Brown DO (mg/L) 827 800 787 813 COD (mg/L)* 1360 1240 1360 660 BOD (mg/L) * 467 427 667 133 Oil and Grease (mg/L) 740 255 280 50 Total Solids (mg/L)* 1080 1565 1885 3155 Total Volatile Solids (mg/L) 170 810 910 1530 Total Suspended Solids (mg/L) 580 520 350 4000 Volatile Suspended Solids (mg/L) 45 15 0 960 Protein (mg/L) 304 358 971 73 Total Carbohydrates (mg/L) 195 219 215 246 Reducing sugar (mg/L) 34 200 77 14 Carbon %* 35 21 27 33 Nitrogen %* 2 2 1 5 * the parameter considered for bio−mimicking the dairy waste effluent 2.9 Statistical analysis The screening of lipolytic and proteolytic bacteria, enzyme assay, and the evaluation of the degradation capacity of the screened strains were performed in independent triplicates. The results obtained were expressed as the mean and standard deviation. The data were analyzed statistically using GraphPad Prism 10.4.0 software. A one-way ANOVA (Analysis of Variance) followed by the Tukey post-hoc multiple comparisons test was performed to statistically assess the differences between the mean values. Values that differ by more than 95% from each other (i.e., at P < 0.05) were considered statistically significant. 3. Results & Discussion 3.1 Characteristics of dairy industry wastewater The characteristics of dairy effluent vary widely depending on the characteristics of raw milk, the type of system, the operational procedure used for manufacturing the different products, and the amount of freshwater used for washing and cleaning the machinery [ 8 , 4 ]. The detailed dairy wastewater characteristics were studied and summarized in Table 2 to simulate the dairy wastewater conditions. The pH, COD, TS, and C/N ratio are important factors considered to formulate the simulated wastewater composition. These factors have a significant impact on the performance of biological wastewater treatment. The pH indicated that the dairy effluent was alkaline, ranging between 11 and 12. This result is analogous to the statement given by Saxena et al., [ 24 ], which mentioned that the pH of the dairy wastewater varies between 6.6 and 12.2. The alkaline pH is due to the presence of nutrients, organic matter, and detergents used for cleaning [ 6 ]. The present analysis indicated that the COD of the effluent was 1360 mg/L, which was slightly reduced to 1240 mg/L at the outlet of the O&G removal tank but increased again to 1360 mg/L in the equalization tank. The higher COD is attributed to fats, nutrients, casein, lactose, and salts [ 7 ]. Since fats contributed to higher COD, its removal at the O & G outlet resulted in a slight reduction in COD. However, adding acid or alkali to adjust the pH (from 12 to 9) in the equalization tank led to an increase in COD. The COD and BOD values fall within the range specified by Mehrotra et al., [ 4 ] for Indian dairy industries. Another important factor that has been discussed less frequently is the Biodegradability Index (B.I.), which is the ratio of BOD to COD [ 35 ]. It is a significant indicator for selecting the wastewater treatment process. The present characteristics showed 0.49 B.I., which indicates that dairy effluent, can be treated by biological processes (Metcalf and Eddy, 2003). However, it requires bioaugmentation of potent microorganisms to initiate the biological process [ 35 ], which is a key objective of the present study. During any dairy wastewater treatment process, primary treatments such as SC and O&G removal are necessary. Hence, the simulated wastewater was prepared to match the characteristics of the equalization tank and was used throughout all the evaluation experiments. This simulated dairy effluent was employed in all the evaluation experiments involving selected strains to minimize interference from the natural flora in wastewater. 3.2 Isolation and characterization of bacteria A total of 75 morphologically different bacterial colonies were obtained from 16 samples from four different stages of DETP. Amongst them, 42 isolates (from NA-20, MA-12, and TA-10) were obtained by enrichment isolation (EI), and 33 isolates (from NA-12, MA-7, TA-11, and MSMA-3) were obtained by direct isolation (DI). EI gives more isolates than DI because the enrichment allows the growth of even less dominant bacteria in a controlled environment. The large sample size helps to obtain the maximum number of efficient bacterial isolates. The colony morphology reveals that 83% of isolated colonies were circular and 17% were irregular in shape with an entire or serrated margin. The diameter of isolated colonies varies from pinpoint to 15 mm. The colour of isolated colonies was yellow, cream, black, or buff. These colonies had either flat or elevated surfaces with opaque or translucent opacity. The colonies had either normal or sticky consistency. Cell morphology shows that the majority of the cells were in rod shape, and very few of them were in circular shape. The cell arrangement was in a chain, singly present, or in a bunch. Some of the cells of isolated colonies were motile, while others were non-motile. Further, cell morphological characteristics show that the majority of isolates from DETP were Gram-negative (62 isolates), and only 13 isolates were Gram-positive bacteria. 3.3 Strategic screening of lipolytic and proteolytic bacteria The screening of obtained isolates was employed through a very organized approach. In the present study, the following three important stages were used for the first time for screening and development of consortia for degradation of dairy wastewater: hydrolytic activity of the isolates, extracellular enzyme-producing isolates, and rate of diffusion. Out of 75, the 4 most potent bacterial isolates were selected after meticulous screening to form consortia for the effective degradation of simulated dairy wastewater. Out of these four isolates, 3 were obtained by enrichment isolation, and 1 was obtained by direct isolation. The Fig. 1 shows colony and morphological characteristics as well as extracellular hydrolytic activity of selected four strains. As stated by Mazzucotelli et al. [ 23 ], the likelihood of obtaining an organism with specific hydrolytic capabilities increased when the isolation was performed from wastewater plentiful with a specific substrate of enzymatic interest. The similar principle was followed for primary screening of bacterial isolates with specific substrate degradation capacities. Consequently, a primary qualitative screening was carried out to select lipolytic and proteolytic bacteria, as lipids and proteins constitute the predominant carbon sources in dairy wastewater. The results of the primary screening reveal that 56 out of 75 (74.667%) isolates exhibited hydrolytic activity. Specifically, 45 isolates showed lipolytic, 3 showed proteolytic, and 8 showed lipolytic as well as proteolytic activity (Table 3 ). Notably, the majority of strains from dairy effluent are lipolytic bacteria that hydrolyze fatty acids in the process of fat degradation. It shows strong lipolytic activity, which is important to break fats and oil during the bioremediation process. Therewithal, the present results were in line with the study by Kulzhanova et al. [ 36 ], which suggests that the number of proteolytic bacteria that break down proteins found in dairy wastewater is low. Table 3 Isolates obtained from direct and enrichment isolation and screening of lipolytic and proteolytic bacteria. Sr. No. Nutrient Agar MacConkey’s Agar Tributyrin Agar Skimmed Milk Agar DI EI DI EI DI EI DI EI 1. DNSC2 ENSC1 DMSC1 * EMSC1 DTSC1 * ETSC1 * DSSC1 *+ 2. DNSC3 * ENSC2 * DMSC2 EMSC2 * DTSC4 * ETSC2 * DSOG1 + 3. DNSC4 ENSC3 *+ DMSC3 * EMSC3 DTSC8 * ETOG1 * DSCT1 + 4. DNOG1 + ENSC4 * DMSC4 * EMSC4 DTAT1 * ETOG2 * 5. DNOG3 ENSC5 * DMAT1 * EMAT1 DTAT2 * ETOG3 * 6. DNAT1 * ENOG1 *+ DMAT2 * EMAT2 DTAT3 * ETOG4 * 7. DNAT2 * ENOG2 *+ DMAT3 * EMAT3 DTCT1 * ETCT1 * 8. DNAT4 ENOG3 * EMCT1 * DTCT2 * ETCT2 * 9. DNAT5 ENOG4 *+ EMCT2 DTOG1 * ETCT3 * 10. DNCT2 * ENOG5 *+ EMCT3 * DTOG2 * ETAT1 * 11. DNCT3 * ENCT1 EMOG1 * DTOG3 * 12. DNCT4 ENCT2 EMOG2 * 13. ENCT3 14. ENCT4 15. ENCT5 *+ 16. ENAT1 *+ 17. ENAT2 * 18. ENAT3 * 19. ENAT4 * 20. ENAT5 * DI – direct isolation, EI – enrichment isolation *: isolates that show hydrolytic lipolytic activity, +: isolates that show hydrolytic proteolytic activity *+: isolates that show both lipolytic as well as proteolytic activity In the second stage of screening, the extracellular enzyme activity of selected isolates was assessed on TA and MSMA in triplicate. The mean and standard deviation of the diameter of the zone of clearance around the well on TA and MSMA are shown in Fig. 2 and Fig. 3 , respectively. The results indicate that almost all isolates exhibited extracellular enzyme activity. Only 9 out of 56 isolates failed to demonstrate such activity. Among these exoenzyme-producing isolates, 15 were identified as significant extracellular lipase-producing bacteria, with a zone of clearance ranging from 15 to 18 mm within 24 h of incubation on TA. The maximum zone of clearance of 18 mm in diameter was observed for strain DMAT1. Additionally, 3 isolates demonstrated extracellular proteinase activity with a considerable zone of clearance between 12–15 mm within 24 h of incubation on MSMA. Among these, isolate DSSC1 exhibited the highest proteolytic activity, with a maximal clearance zone of 15 mm in diameter. In consequence, 10 isolates were selected for further screening, which is based on a diffusion assay. These extracellular lipases and proteinases are predominantly associated with the hydrolysis /biodegradation of lipids and proteins [ 30 ]. These extracellular enzyme-producing bacteria have wide industrial applications as well as are efficient for bioremediation processes of effluent containing high concentrations of oil and fats [ 37 ]. The significance of selecting exoenzyme-producing bacteria is that these exoenzymes hydrolyze proteins, and lipids into soluble monomers making them easily accessible for bacterial absorption and metabolism to generate energy and support cell growth [ 36 ]. Consequently, the present screening approach facilitates the selection of exoenzyme-producing bacteria with the potential to enhance the degradation efficiency of target contaminants, such as fats and proteins [ 11 ]. A one-way ANOVA was performed to compare the means of the diameter of the zone of clearance on TA as well as MSMA. A one-way ANOVA revealed that there was a statistically significant difference in the mean diameter of the zone of clearance (P < 0.05). As there was a significant difference among means of diameter, a Tukey post-hoc test was performed. As shown in Fig. 2 and Fig. 3 , bars sharing the same letter are not significantly different according to Tukey multiple comparisons analysis. In the final stage of screening, 4 out of 10 isolates were selected based on the diffusion kinetics. In this assay, isolates with a higher rate of diffusion as well as exhibited two hydrolytic activities were selected to form a consortium for the bioaugmentation strategy. ETOG2 showed the fastest rate of lipid degradation with a rate of diffusion of 0.71 mm/h on TA with an 8.5 mm increase in the zone of clearance within 12 h of incubation. Whereas the fastest protein degradation was shown by isolate ENOG5 with a maximum rate of diffusion of 0.83 mm/h on MSMA with a 5 mm increase in the zone of clearance within 6 h of incubation. Figure 4 represents data based on the rate of diffusion of these four selected isolates. Among these four isolates, three isolates (DSSC1, ENOG5, and ENAT1) showed lipolytic as well as proteolytic activity, and ETOG2 showed only lipolytic activity but with a very high rate of diffusion. The lipolytic and proteolytic bacteria produce a zone of hydrolysis when grown on tributyrin and skimmed milk agar, respectively. During hydrolysis, these bacteria produce lipase and proteinase enzymes which are diffused through the agar medium and degrade the substrate present in the agar medium. It creates a zone of clearance around the colony. Hence, the agar diffusion assay is an easy method to check the biodegradation potential of bacteria. This innovative approach of agar diffusion assay measures the time required by bacterial enzymes to diffuse through and hydrolyze the specific substrate in agar medium. This technique gives more sensitive results as compared to others. The rate of diffusion also provides an outline of the hydraulic retention time required for biological treatment processes. 3.4 Phylogenetic identification and growth curve study of selected bacterial isolates The selected four most potential bacterial isolates showed 99% similarity with Massilia haematophila (DSSC1), Brevibacillus agri (ENAT1), Pseudomonas guguanensis (ENOG5), and Lysinibacillus fusiformis (ETOG2), within the NCBI database. The 16S rRNA gene sequences obtained was submitted to the NCBI database, and their accession numbers are stated in Table 4 . Table 4 Identification of selected potential bacterial strains and their similarity with respect to the NCBI database. Sr. No. Sources of isolates Isolation No. Name of closest related species Similarity (%) NCBI database accession number 1 Screen Chamber DSSC1 Massilia haematophila 99.62 OQ733357.2 2 Oil and Grease ENOG5 Pseudomonas guguanensis 99.38 OQ733358.2 3 ETOG2 Lysinibacillus fusiformis 100 OQ733359.2 4 Return Activated Sludge ENAT1 Brevibacillus agri 99.88 OQ733360.2 Among the four selected strains, Massilia haematophila was identified for the first time as a potential candidate for dairy wastewater treatment. This species remains relatively underexplored in the context of biodegradation of contaminants. Previous study have investigated the effective degradation of BTEX compound and Poly(3-hydroxybutyrate) by Massilia sp. [ 38 , 39 ]. The isolation of Lysinibacillus from a wastewater treatment system is common, as it is pervasive and diverse in the environment. It can catabolize various natural and xenobiotic compounds. The ability of Lysinibacillus sp. to survive under extremely harsh conditions makes it the most deserving strain for bioremediation in a contaminated environment [ 40 ]. The reported efficient lipase-producing bacteria Bacillus safensis , S9 Pseudomonas alcaliphila ED1, Neisseria ovis , Bacillus subtilis, Bacillus licheniformis, Bacillus pumilus, Bacillus acidophilus, Bacillus coagulans , and Bacillus stearothermophilus are isolated and identified from dairy wastewater [ 41 , 42 , 43 ]. Whereas proteinase enzyme producers Bacillus isronensis, Acinetobacter moffi, Micrococcus mycoides, Bacillus mycoides, Pseudomonas aeruginosa, Bacillus subtilis, and Pseudomonas fluorescence were isolated from dairy wastewater for different industrial applications [ 26 , 28 , 44 ]. Also, bacterial strains Bacillus subtilis, Escherichia coli, Lysinibacillus sphearicus, Bacillus cereus, Bacillus thuringiensis, Bacillus cereus , and Brevibacillus brevis were isolated and identified from dairy wastewater and dairy sludge by Garcha et al. [ 27 ]. These isolates were selected based on BOD, TSS, and O & G reduction efficiencies instead of lipolytic and/or proteolytic activity. From this literature, it is evident that Bacillus sp. and Pseudomonas sp. are found in high abundance in dairy wastewater which were also isolated from our wastewater. This shows that Bacillus sp. and Pseudomonas sp. seems to be dominant in dairy industry wastewater. Similarly, it is also stated by Loperena et al. [ 20 ], and Custodio et. al. [ 11 ], that an inoculum containing Pseudomonas sp. and Bacillus sp. might be a good option to enhance the biodegradable efficiency of dairy effluent. These strains are known for their good lipolytic and proteolytic activity. The growth curve study of selected isolates shows that isolate ENAT1 (0.029 µ/min) and ETOG2 (0.0085 µ/min) have maximum specific growth rates at 25℃ and 37℃, respectively. The doubling time of isolate DSSC1, ENOG5, ETOG2, and ENAT1 at 25℃ was 0.51, 0.46, 0.47, and 0.39 h, respectively, whereas the doubling time of isolate DSSC1, ENOG5, ETOG2, and ENAT1 at 37℃ was 1.41, 2.73, 1.35, and 2.5 h, respectively. Hence, the data reflects that these 4 strains took less than an hour for their growth and activity at nearly 25℃ temp or at ambient. 3.5 Study of lipase and proteinase enzyme activity The four strains were selected as lipolytic and proteolytic bacteria. Hence, validation of the results of the diffusion of extracellular enzymes was employed through enzyme assays. The lipase activity shown by DSSC1, ENOG5, ETOG2, and ENAT1 (represented in Fig. 5 a) was in the range of 0.0028 to 0.0063 U/mL/min, and protease activity shown by DSSC1, ENOG5, and ENAT1 was in the range of 13 to 14 U/mL/min (represented in Fig. 5 b), respectively. All these four strains showed an effective pattern of enzyme activity. Also, these strains showed maximum activity within 30 minutes of incubation time. The one-way ANOVA of the lipase activity (P = 0.08455) and proteinase activity (P = 0.13095) shows that there is no significant difference in the mean value of data obtained. These results showed the amount of enzymes produced by bacteria within a specific incubation period at a particular temperature and pH. The summary of results of the diameter of the zone of clearance by exoenzyme, enzyme assay, and rate of diffusion are summarized in Table 5 . The results indicate that the maximum zone of clearance (degradation zone) was observed for strain ENAT1 on TA, whereas maximum lipase production was observed for strain ETOG2. However, strain DSSC1 showed the maximum zone of clearance in MSMA, and maximum protease production was observed for ENAT1 (Table 5 ). Similarly, no correlation was found between enzyme production and degradation efficiency in the study by Hermes et al. [ 45 ] study. Both analyses were performed on the same samples, and the results show that the maximum COD reduction was 44.96% with a corresponding lipase production of 3.54 U/mL/min, whereas the maximum lipase production was 4.87 U/mL/min with a COD reduction of 33.54%. Table 5 Summary of the screening assays and enzyme assay Name of Isolate DSSC1 ENAT1 ENOG5 ETOG2 Name of closest related species Massilia haematophila Brevibacillus agri Pseudomonas guguanensis Lysinibacillus fusiformis Lipolytic Activity Diameter of zone of clearance by exoenzyme on TA (mm) 12 16 15 14 Rate of diffusion on TA (mm/h) 0.33 0.5 0.5 0.71 Lipase Assay (Units/mL/min) 0.0028 0.0027 0.0039 0.0062 Proteolytic Activity Diameter of zone of clearance by exoenzyme on MSMA (mm) 15 12 12 - Rate of diffusion on MSMA (mm/h) 0.75 0.67 0.83 - Proteinase Assay (Units/mL/min) 13.63 14.09 13.14 - In contrast, the study conducted by Kashyap and Dubey [ 18 ] suggests the correlation between the diameter of the zone of clearance and lipase enzyme production with BOD removal efficacy. The particular study indicates that the Bacillus strain showed the maximum zone of clearance on TA and lipase production with the highest BOD removal efficacy of dairy wastewater. However, these three tests were performed at different incubation temperatures. 3.6 COD reduction efficacy of selected strains The industrial effluent is toxic to the environment due to the presence of organic substances that are slowly biodegradable. This is the reason many industrial effluents often have higher COD than BOD [ 46 ]. Hence, the biodegradation capacity of individual strains and consortia was evaluated in terms of COD reduction efficacy, as COD represents the total concentration of organic matter in wastewater and is an important parameter for evaluating the efficiency of biological wastewater treatment [ 11 ]. The results of the percentage reduction of COD by selected isolates are presented in Fig. 6 . The corresponding environmental factors were 7 pH, 24 C/N, 10% inoculum concentration, 37℃ temperature, and 24 h incubation time. The results were statistically analyzed by one-way ANOVA, and the analysis suggests that the obtained results were 98.98% (P < 0.01) statistically significant. Hence, further pairwise comparison by Tukey post-hoc test was performed, and results are presented in Fig. 5 . The Tukey multiple comparison test indicates a significant difference between DSSC1-consortia (P = 0.016), ENAT1-consortia (P = 0.0293), and ETOG2–consortia (P = 0.0149). The result shows that the maximum efficacy of 35% was achieved by isolate ENGO5 with a corresponding 1335 mg/L reduction of COD, whereas the COD removal efficacy increased to 43% with a 1625 mg/L reduction of COD in the case of augmentation of consortia. The study was conducted with simulated dairy wastewater with fewer nitrogen and phosphorous sources. Hence, there is comparatively less COD removal, possibly due to the enervation of nitrogen and phosphorus in the medium [ 47 ]. According to the statement made by [ 19 ], the present study also confirms that the interactive effect of selected strains when used in a consortium does not vanquish the biodegradability of individual strains; instead, all strains cumulatively increase the degradation of organic contaminants. The interaction of microorganisms in a consortium will facilitate their successful establishment and functioning in the environment, where they will be bio-augmented for suitable treatment. The present study shows higher reduction in COD in 24 h of incubation. The study by [ 20 ], Bacillus sp. showed a higher reduction in COD, whereas a consortium of 8 isolates of Bacillus sp., Pseudomonas sp., and Acinetobacter sp. showed a similar efficiency of 57% reduction in COD. It shows that with a consortium consists of higher number of strains, there is also no significant difference in COD reduction efficacy within 48 h of incubation and 30℃ temperature. Similarly, Lactobacillus plantarum and Lactobacillus casei showed a reduction in COD by 71.6% and 60.8%, respectively, with a 1% inoculum, whereas a consortium of these Lactobacillus sp. showed a COD reduction of 75.8% with the same inoculum concentration, which is 6% higher than that of the individual strain [ 8 ]. The present study also indicates a higher COD reduction by bacterial strains than by yeast strains. Similarly, Porwal et al. [ 48 ], also reported a higher reduction by bacterial strains than by yeast strain. This study suggests that a higher reduction in COD (46.93%) was achieved by the Bacillus strain, whereas a consortium of bacteria and yeast strains increased COD reduction by 8%, reaching 50.65%. Furthermore, the COD reduction was greater in the microbial consortium-treated effluent than in the sludge that was activated without the microbial consortium [ 3 ]. From all these observations, it is evident that, in the present study, COD reduction was increased significantly by 26% by a consortium than the maximum COD reduction by individual strains. This shows that the novel approach of diffusion assay based on the kinetics of diffusion for the selection of bacterial strains gives more efficient bacterial consortia. Further study involves optimizing operating parameters to increase the efficiency of the treatment process by bioaugmentation of selected four strains in equal proportion. 4. Conclusion In conclusion, the development of bacterial consortia for bioaugmentation presents a promising approach to efficiently treat dairy wastewater. The selected strains, Massilia haematophila (DSSC1), Brevibacillus agri (ENAT1), Pseudomonas guguanensis (ENOG5), and Lysinibacillus fusiformis (ETOG2), demonstrated significant biodegradation potential, with consortia exhibiting a 26–86% higher reduction in chemical oxygen demand (COD) compared to individual strains. Among these strain Massilia sp. was reported for the first time for biological degradation of dairy wastewater. The consortia demonstrated COD removal of approximately 1700 mg/L, which is significantly higher than the average dairy effluent COD (~ 1300 mg/L). The findings of present study also suggest that the amount of enzyme produced by bacterial strains may not be directly proportion to the degradation of organic components. Hence, the diffusion-based screening method proved effective in selecting potent bacterial strains, offering a novel strategy for improving dairy wastewater treatment. This study highlights the potential of bacterial consortia as a sustainable solution for mitigating the environmental impact of dairy industry effluents. Declarations Acknowledgments: The authors acknowledge the Bioenergy Group, MACS-Agharkar Research Institute, Pune, India for providing lab facility and instrument facilities to carry out the present work, also Fergusson College and Department of Technology, SPPU, Pune for support. Competing interest: Conflict of interest: The authors declare no known competing financial interests or personal relationships with other people or organizations that could inappropriately influence (bias) the work reported in this paper. Credit authorship contribution statement: Manjiri Patil (MP): Execution, Formal Analysis, Data curation, Investigation, Methodology, Writing – original draft. Pranav Kshirsagar (PK): Conceptualization, and Methodology, Writing – review & editing. Prashant Dhakephalkar (PD): Conceptualization , review and editing, Suneeti Gore (SG): Conceptualization , Supervision, Writing – review and editing Vikram Lanjekar (VL): Conceptualization, Supervision, Writing – review & editing. Data Availability The authors do not have permission to share data. Funding Declaration The authors declare that there is no funding from any external source, References A. D. Patwardhan, A. D. Patwardhan. H. S. and S. K. Aws N. Al-Tayawi and Abstract, “We are IntechOpen, the world ’ s leading publisher of Open Access books Built by scientists, for scientists TOP 1%,” Intech, p. 13, 2012, [Online]. Available: http://dx.doi.org/10.1039/C7RA00172J%0Ahttps://www.intechopen.com/books/advanced-biometric-technologies/liveness-detection-in-biometrics%0Ahttp: //dx.doi.org/10.1016/j.colsurfa.2011.12.014 L. Velmurugan and K. D. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6410986","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447710068,"identity":"dfed55c0-09d4-4b3d-b8b2-41ede238e179","order_by":0,"name":"Manjiri Patil","email":"","orcid":"","institution":"Savitribai Phule Pune University","correspondingAuthor":false,"prefix":"","firstName":"Manjiri","middleName":"","lastName":"Patil","suffix":""},{"id":447710069,"identity":"3c5c1858-2a10-434e-b73a-d33468e4f888","order_by":1,"name":"Pranav Kshirsagar","email":"","orcid":"","institution":"Agharkar Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Pranav","middleName":"","lastName":"Kshirsagar","suffix":""},{"id":447710070,"identity":"e0ae99cf-0984-4268-a9e0-8e8afe2fcc29","order_by":2,"name":"Prashant Dhakephalkar","email":"","orcid":"","institution":"Agharkar Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Prashant","middleName":"","lastName":"Dhakephalkar","suffix":""},{"id":447710071,"identity":"2d3da75d-cdaf-42f9-8eac-0b1a4945015e","order_by":3,"name":"Suneeti Gore","email":"","orcid":"","institution":"Savitribai Phule Pune University","correspondingAuthor":false,"prefix":"","firstName":"Suneeti","middleName":"","lastName":"Gore","suffix":""},{"id":447710072,"identity":"3c6c2128-9fa0-4686-9864-a61dff66a9ff","order_by":4,"name":"Vikram Lanjekar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACCeYGBgYeBgMGBuYDDCCKCC2MMC1sCaRoAavlIUo9UOntxsbPBTI2xvyzz3z8XFFwJ3E7ewPj44pfeLTcOdgsPYMnzUziXO5myTMGzxJ39hxgNjzbh1uL5IzEBmkensM2DGd4N0g2GBxO3HAjgU2ysQevlubfPDz/beTP8Dz+CdZy/wF+LfwSiW1AWw6YGZzhYYPawgBk/MCjReZgmzUPT7Kx4Rk2M8sGg2fGG84kNhs2NuDWwibdfPg2b4+d4bwzzI9vNvy5I7vh+OGDDxv+4NYCBowIlx8AcRsYGNsIaGFAuPwAlCZkyygYBaNgFIwkAABan1cfb4d0ngAAAABJRU5ErkJggg==","orcid":"","institution":"Agharkar Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Vikram","middleName":"","lastName":"Lanjekar","suffix":""}],"badges":[],"createdAt":"2025-04-09 10:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6410986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6410986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81554173,"identity":"e9046b6f-29c9-4646-973d-d9da22cb763f","added_by":"auto","created_at":"2025-04-28 13:22:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":186391,"visible":true,"origin":"","legend":"\u003cp\u003eColony and morphological characteristics as well as extracellular hydrolytic activity of selected four strains.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6410986/v1/13efb1a3e2f428bed4ba8116.jpeg"},{"id":81554178,"identity":"90a5ede0-57a5-4d68-8712-02045b9b85e9","added_by":"auto","created_at":"2025-04-28 13:22:50","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":444175,"visible":true,"origin":"","legend":"\u003cp\u003eDiameter of zone of clearance by extracellular enzymes on TA.\u003c/p\u003e\n\u003cp\u003eMeans with same letters represent non-significant difference as per Tukey multiple comparisons test (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6410986/v1/03db4583004f49d9eb304c20.jpeg"},{"id":81554174,"identity":"7ebd5c23-9bcf-4b2c-bbbc-b957e729154c","added_by":"auto","created_at":"2025-04-28 13:22:50","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":262589,"visible":true,"origin":"","legend":"\u003cp\u003eDiameter of zone of clearance by extracellular enzymes on MSMA.\u003c/p\u003e\n\u003cp\u003eMeans with same letters represent non-significant difference as per Tukey multiple comparisons test (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6410986/v1/d96ecad5fa56fb437127676a.jpeg"},{"id":81556297,"identity":"d0bfbd49-113d-408e-b553-3138e95678bc","added_by":"auto","created_at":"2025-04-28 13:38:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46598,"visible":true,"origin":"","legend":"\u003cp\u003eRate of diffusion (mm/h) by isolate a) DSSC1 on TA, b) ENAT1 on TA, c) ENOG5 on TA, d) ETOG2 on TA, e) DSSC1 on MSMA, f) ENAT1 on MSMA, g) ENOG5 on MSMA\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6410986/v1/7b63ebf3b5ec610f15de45db.png"},{"id":81555355,"identity":"1b9319d0-162d-4469-9a3e-f788b4b288eb","added_by":"auto","created_at":"2025-04-28 13:30:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":19707,"visible":true,"origin":"","legend":"\u003cp\u003eEnzyme activity by selected strains a) Lipase activity and b) Proteinase Activity\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6410986/v1/4970e33577e6f416eb978e55.png"},{"id":81554175,"identity":"cb3c68c8-2092-408e-8947-5a068b3ea271","added_by":"auto","created_at":"2025-04-28 13:22:50","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":128629,"visible":true,"origin":"","legend":"\u003cp\u003eCOD reduction efficiency by selected bacteria and Consortia.\u003c/p\u003e\n\u003cp\u003eThe one-way ANOVA following the Tukey post hoc multiple comparison analysis was performed to check statistical significance of the results obtained.\u003c/p\u003e\n\u003cp\u003e‘*’ indicates statistical significance (P \u0026lt; 0.05) between two groups.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6410986/v1/6f4957ff9a9c4906cd1dc019.jpeg"},{"id":81556980,"identity":"7128c59a-023f-491f-be3c-010cbaf40ab8","added_by":"auto","created_at":"2025-04-28 13:46:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2825791,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6410986/v1/a1503f7d-08b0-4266-b962-d676b3f0963b.pdf"},{"id":81555357,"identity":"9edc5d36-b7e9-43b8-ac43-996949ea27d0","added_by":"auto","created_at":"2025-04-28 13:30:50","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":254358,"visible":true,"origin":"","legend":"","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6410986/v1/a2a4486d8a0a723bbfd1bfa9.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Innovative Bacterial Consortia for Simulated Dairy Wastewater Treatment: Improving COD Removal Efficiency","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDairies are places where raw milk is processed either for direct consumption or utilized as a raw material for allied industries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Dairy is among the most significant sectors of agricultural industries, as milk is an important food in our daily lives. Even so, they encounter challenges in the sustainable production of milk, especially in water conservation, energy-efficient industry, and waste management [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The milk and dairy products processing industries represent important commodities that demand huge amounts of water for processing and cleaning [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Wastewater is generated from cleaning and washing machinery, equipment like cans or tankers of the milk processing plant, floor washing, spillage and leakages of milk and milk products, boiler blowdown, softener regeneration water, bleed from the condenser, etc. Wastewater is generated in batches as the cleaning and washing take place after the complete production of each batch [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is a highly polluting industry in terms of both the quantity and quality of wastewater it generates. The wastewater generated is about 0.2 to 10 L L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of milk processed [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is estimated that approximately 2% of processed milk is lost within the wastewater stream. Wastewater from dairy is characterized by a high organic load, which contains mainly milk fats, casein, lactose, inorganic salts, detergents, and sanitizers used for washing [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs dairy generates highly biodegradable wastewater, biological treatment strategies will be more effective. The degradation of organic pollutants by microorganisms is an easy, uncomplicated, cost-effective, and eco-friendly process [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. There are aerobic and anaerobic biological treatments present to treat dairy wastewater. Most conventional aerobic biological processes, such as the activated sludge process and sequential batch reactor, are based on extended aeration, which requires more hydraulic retention time. Hence, its major drawbacks are high energy consumption and land requirements. Also, as there is quantitative and qualitative variation in the composition of influent generated by dairy industries, the failure rate of conventional biological treatment is higher [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Conversely, anaerobic biological processes also present several limitations for the treatment of dairy wastewater, such as due to the presence of oil and fats, less transfer through the membrane\u0026ndash;aqueous interface, contributing to foul odour and blockages in the reactor, which ultimately reduces the efficiency of the reactor [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additionally, the hydrolysis of lipids in an anaerobic reactor leads to the formation of long-chain fatty acids, which hinder methane production [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, anaerobic treatments are very sensitive to their operating conditions, like organic loading, pH, etc., and they cannot tolerate shock loads. This states that anaerobic biological treatments are unfavorable for dairy wastewater treatment. Also, in a comparative study conducted by Custodio et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] showed that aerobic treatment had a higher COD removal efficiency (80%) than anaerobic processes (58.6%) for dairy wastewater treatment. Despite these disadvantages, most research has predominantly focused on anaerobic reactors for dairy wastewater treatment [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It shows the need for research in advance aerobic biological treatment processes like bioaugmentation for dairy effluent.\u003c/p\u003e \u003cp\u003eIt has been observed that the efficiency of any biological treatment process not only depends on the characteristics and concentration of the constituents present in the effluent but also on the concentration and type of bacteria present in the effluent [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For the efficient design of the treatment process, one must know the micro-biota present in the wastewater, the biochemical and metabolic characteristics of organisms, and the genesis of pollutants [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the biodegradation activity by inherent bacteria might not be adequate to achieve an efficient and reliable treatment process [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Microbial engineering, like bioaugmentation and bioremediation, by utilizing microorganisms and genetic engineering approaches, increases the effectiveness of treatment procedures [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The introduction of highly efficient microbial consortia for wastewater bioremediation has shown promising results in improving water quality and makes it suitable for safe disposal or reuse by reducing key contaminants such as chemical oxygen demand (COD), and reducing sludge volume without causing adverse consequences. Velmurugan \u0026amp; Pandian [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] highlighted the economic benefits of microbial bioaugmentation which reduces sludge generation, 50% cost in transportation and disposal of sludge; the cost of chemicals and energy required for disinfection by bypassing the disinfection process post effluent treatment.\u003c/p\u003e \u003cp\u003eFurther, if bioaugmentation of bacterial consortia is with high substrate\u0026ndash;specific biodegradation capacity then it enhances the efficiency of the treatment process [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Importantly, these selected microorganisms should exhibit resilience to the concentration of polluting constituents and able to degrade these contaminants [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Hence, bio-augmentation of microorganisms derived from a similar habitat presents significant benefits, as they can readily adapt to the environmental conditions and function concurrently with the indigenous microflora [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Also, as stated by Mazzucotelli et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], microorganisms that exhibit two or more hydrolytic capacities are more efficient resources for the treatment of industrial waste.\u003c/p\u003e \u003cp\u003eHowever, limited studies have explored the development of consortia composed of substrate-specific bacteria with two hydrolytic capacities for biological treatment [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Also, despite the significant advantages of bioaugmentation of consortia, there is a lack of comprehensive understanding of a systematic approach for the selection and development of consortia for dairy effluent treatment.\u003c/p\u003e \u003cp\u003eIn this context, the objective of the present research work was to develop an efficient bacterial consortium possessing hydrolytic (lipolytic and proteolytic) capacities for effective biological treatment of simulated dairy wastewater. The work involved isolating, characterizing, and screening lipase- and proteinase- producing bacterial strains from dairy effluent. A novel approach to the diffusion of enzymes through agar was adopted to confirm the maximum degradation capacities of bacteria. Based on this dual hydrolytic activity, the four most potent bacteria were selected and identified. The biodegradation capacity of these strains, individually and in consortia, was subsequently evaluated based on their COD removal efficacies.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sampling of dairy effluent\u003c/h2\u003e \u003cp\u003eWastewater samples were obtained from a dairy effluent treatment plant (DETP), located in Pune, Maharashtra. The samples were obtained at different stages of DETP, namely the screen chamber (SC), oil and grease outlet (O\u0026amp;G), equalization tank (ET), and return activated sludge (RAS). A total of 16 samples (4 from each stage) were obtained in 50 mL sterile falcon tubes and immediately brought to the laboratory at a controlled temperature. Further, the samples were used for the evaluation of physicochemical characteristics, direct isolation, and enrichment of bacterial cultures. The samples were stored at 4\u0026deg;C until they were used for further studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Wastewater analysis\u003c/h2\u003e \u003cp\u003eDairy wastewater samples used to obtain isolates were analyzed to check their physico-chemical characteristics. The parameters that were analyzed included pH, temperature, colour, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), oil and grease (O\u0026amp;G), total solids (TS), total suspended solids (TSS), total volatile solids (TVS), volatile suspended solids (VSS), protein, sugar, total carbon, and total nitrogen. The pretreatment of samples and analytical methods used for analysis are mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalytical methods used for wastewater characterization.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter analyzed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe method used for the analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePretreatment of sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference / Instrument used\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaw Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epH Meter / [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] 4500-H\u0026thinsp;+\u0026thinsp;B.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBy using electrode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaw Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epH Meter / [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] 4500-H\u0026thinsp;+\u0026thinsp;B.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical Observation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaw Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDissolved Oxygen (DO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTitrimetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSamples pH were adjusted to 6.5\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], 4500-O C.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological Oxygen Demand (BOD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTitrimetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSamples pH were adjusted to 6.5\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], 5210 B.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemical Oxygen Demand (COD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTitrimetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaw Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], 5220 B (modified for a smaller volume of sample)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOil and Grease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGravimetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSamples pH were adjusted to \u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], 5520 B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Solids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGravimetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaw Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], 2540 B.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Suspended Solids (TSS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGravimetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaw Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], 2540 D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Volatile Solids (TVS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGravimetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaw Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], 2540 E.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolatile Suspended Solids (VSS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGravimetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaw Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], 2540 E.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColorimetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSamples were centrifuged and filtered through a 0.2 \u0026micro;m filter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBiuret colorimetric method [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColorimetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSamples were centrifuged and filtered through a 0.2 \u0026micro;m filter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNSA and Anthron Method\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombustion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaw Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCHNS Method\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombustion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaw Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCHNS Method\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Isolation \u0026amp; characterization of the bacteria\u003c/h2\u003e \u003cp\u003eThe bacterial isolates were obtained by direct and enrichment isolation strategies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The bacteria were isolated using a) Nutrient Agar (NA) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], b) MacConkey\u0026rsquo;s Agar (MA), c) Tributyrin Agar (TA) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and d) Modified Skimmed Milk Agar (MSMA) media. The MSM was prepared as per Raj et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] along with the addition of 2.5 g/L yeast extract powder. In the direct isolation method, collected samples were directly streaked on the above-stated four media supplemented with 2% agar-agar. In the enrichment-isolation method, 10% (V/V) samples were inoculated into the Erlenmeyer flask containing enrichment media (broth of above-stated media) and kept for incubation at 37\u0026deg;C up to 96 h. After enrichment, these samples were streaked on respective agar media.\u003c/p\u003e \u003cp\u003eThe agar plates were incubated at 37\u0026deg;C for up to 96 h. Morphologically different bacterial colonies were isolated and studied for colony morphological characteristics (size, shape, colour, margin, elevation, opacity, and consistency) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Further, colonies showing a zone of clearance were isolated from TA and MSMA. All the obtained isolates were characterized for their cell morphology (cell shape, arrangement, motility, and Gram staining) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The purity of isolates was confirmed by streaking isolates on nutrient agar media and was stored at 4℃ on nutrient agar slants [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Development of a bacterial consortium through a three stage strategic screening\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Screening of lipolytic and proteolytic bacteria\u003c/h2\u003e \u003cp\u003eAs dairy wastewater contains higher concentrations of lipids (milk fats) and proteins (milk casein), the screening was accomplished based on lipolytic and proteolytic activity on TA and MSMA, respectively. A loopful of active (12 to 24 h old) bacterial culture was spot inoculated on TA and MSMA [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. After incubation at 37\u0026deg;C for up to 48 to 96 h, isolates showing a zone of clearance on either one of the media were examined further for secondary quantitative analysis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, isolates obtained from NA and MA were inoculated on TA as well as MSMA, whereas the isolates obtained on TA were inoculated only on MSMA, and the isolates obtained from MSMA were inoculated only on TA to test their lipolytic and proteolytic ability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Secondary quantitative screening of lipolytic and proteolytic bacteria\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section4\"\u003e \u003ch2\u003e2.4.2.1 Inoculum preparation\u003c/h2\u003e \u003cp\u003eThe active bacterial cultures having equal cell density (adjusted to the same OD at A\u003csub\u003e600\u003c/sub\u003e) were inoculated in nutrient broth and incubated for 24 h at 37\u0026deg;C. Post-incubation, cell-free supernatant was obtained by centrifugation at 10,000 rpm for 10 minutes at 4\u0026deg;C. This cell-free supernatant was employed as a source of crude enzyme extract (CEE) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e2.4.2.2 Extracellular cell-free enzyme activity assay\u003c/h2\u003e \u003cp\u003eOn TA and MSMA, 5-mm diameter bore wells were punched using a sterile cork borer under sterile conditions. The CEE (100 \u0026micro;L) was added to the well on agar plates and incubated at 37\u0026deg;C for 24 h. After incubation, the diameter of the zone of clearance (in mm) around the well was measured [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The zone of clearance around the well on TA and MSMA indicates positive extracellular lipase and protease activity, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Kinetics of lipid and protein degradation\u003c/h2\u003e \u003cp\u003eIsolates that were screened based on extracellular enzymatic activity were subjected to diffusion assay. For this assay, 24-h-old cultural broths were adjusted to the same cell density at A\u003csub\u003e600\u003c/sub\u003e nm. 10 \u0026micro;L of the broth was inoculated at the center of the petri plates containing TA or MSMA and incubated at 37\u0026deg;C. The diameter of the zone of clearance was measured every 6 h until it showed no further diffusion. The experiment was conducted in duplicate. The rate of diffusion was calculated as per expression 1.\u003c/p\u003e \u003cp\u003er\u003csub\u003ed\u003c/sub\u003e =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\frac{\\text{d}\\text{D}\\:}{\\text{d}\\text{T}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eWhere, r\u003csub\u003ed\u003c/sub\u003e \u0026ndash; rate of diffusion in mm/h\u003c/p\u003e \u003cp\u003edD \u0026ndash; Difference in diameter of zone of clearance within time T in mm\u003c/p\u003e \u003cp\u003edT \u0026ndash; Difference in time within which the zone of clearance was measured in h\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Identification of the selected bacterial isolates\u003c/h2\u003e \u003cp\u003eThe overnight-grown cells in nutrient broth were used to extract DNA using a Sigma GenElute Bacterial Genomic DNA kit as per the manufacturer's protocol. The quality and intactness of DNA were checked by running DNA extraction on 0.8% agarose gel. PCR amplification of the 16S rRNA gene was done by using GT-PCR master mix of Takara Emerald\u003csup\u003e\u0026reg;\u003c/sup\u003e. The universal primers (forward primer 27 F-AGAGTTTGATCMTGGCTCAG and reverse primer 1492 R - TACGGYTACCTTGTTACGACTT) were used for the PCR reaction mixture. The PCR reaction was performed with the following conditions: Initial denaturation was done at 95\u0026deg;C for 5 minutes, followed by 35 amplification cycles at 94\u0026deg;C for 1 minute, the annealing temperature of primers was 55\u0026deg;C for 1 minute, and extension at 72\u0026deg;C for 1.30 minutes. The final extension was done at 72\u0026deg;C for 10 minutes. The quality of the PCR product was checked by running the product on 1.5% agarose gel. The resulting PCR products were purified using the FavorPrem\u0026trade; GEL/PCR purification kit as per the manufacturer's protocol. The PCR-purified product was sequenced using the Applied Biosystems\u0026reg; BigDyeTM Terminator Cycle Sequencing Kit. The sequencing reaction was performed with the following conditions: Initial denaturation was done at 96\u0026deg;C for 1 minute; subsequently, 25 amplification cycles were done at 96\u0026deg;C for 10 seconds, the annealing temperature of primers was 50\u0026deg;C for 5 seconds and extension was at 60\u0026deg;C for 2 minutes. The final extension was done at 4\u0026deg;C. The cleanup was performed using SAM\u0026rsquo;s Xterminator solution and further loaded for Sanger sequencing in the Seg Studio Genome Analyzer, Thermo Fisher. The National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST) similarity search tool was used to compare obtained 16S rRNA gene sequences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Growth curve and doubling time of the screened cultures\u003c/h2\u003e \u003cp\u003eLog phase growing cells of selected cultures were inoculated in sterile (autoclaved at 121\u0026deg;C for 20 minutes) nutrient broth (pH 7) and adjusted to the same optical density at A\u003csub\u003e600\u003c/sub\u003e. These cultures were incubated at 25 and 37\u0026deg;C. The absorbance at 600 nm was noted down every 30 minutes by using the spectrophotometer DR 2700 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpecific growth rate and doubling time were calculated by expressions 2 and 3, respectively [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpecific growth rate, \u0026micro;/min=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{(\\text{Log}\\text{10}\\:\\text{Z}-\\:\\text{Log}\\text{10}\\text{Z}\\text{0})\\text{*}2.303)\\:}{\\left(\\:\\text{T}-\\text{T}\\text{0}\\right)}\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e \u003cp\u003eDoubling Time (h) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{0.693\\:\\:\\:\\:\\:}{\\text{S}\\text{p}\\text{e}\\text{c}\\text{i}\\text{f}\\text{i}\\text{c}\\:\\text{g}\\text{r}\\text{o}\\text{w}\\text{t}\\text{h}\\:\\text{r}\\text{a}\\text{t}\\text{e}\\:}\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/p\u003e \u003cp\u003eWhere,\u003c/p\u003e \u003cp\u003eZ - optical density at the end of the exponential phase of the growth\u003c/p\u003e \u003cp\u003eZ\u003csub\u003e0\u003c/sub\u003e - optical density at the beginning of an exponential phase of the growth\u003c/p\u003e \u003cp\u003eT- time at the end of the exponential phase of the growth, h\u003c/p\u003e \u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e - time at the beginning of an exponential phase of the growth, h\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Enzyme activity of selected bacteria\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.7.1 Lipase activity assay\u003c/h2\u003e \u003cp\u003eThe lipase activity was computed by the spectrophotometric method described by [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] with a few modifications. The 24-h-old cultural broth was centrifuged at 10,000 rpm for 10 minutes at 4\u0026deg;C to obtain cell-free supernatant (CFS). For the lipase assay, the mixture of 0.5 mL supernatant, 0.5 mL of 0.02 M phosphate buffer of pH 7.0, and 1 mL of the substrate (8 mM p-Nitrophenyl Laurate in isopropanol) was prepared and incubated at 37℃ for 15 \u0026amp; 30 minutes of the time intervals. After incubation, 0.5 mL of 3 M HCl was added to stop the reaction. The reaction mixture was centrifuged at 10,000 rpm for 10 minutes at 4℃ and 1 mL of supernatant was collected in a test tube. The pH of the supernatant was made alkaline by adding 1 mL of 2 M NaOH, and absorbance was noted at 420 nm. Reagent blank (2 mL phosphate buffer), substrate blank (1 mL of phosphate buffer\u0026thinsp;+\u0026thinsp;1 mL of the substrate), and enzyme blank (0.5 mL of supernatant of tributyrin broth without strain\u0026thinsp;+\u0026thinsp;0.5 mL of phosphate buffer\u0026thinsp;+\u0026thinsp;1 mL of substrate solution) were also conducted similarly. The lipase concentrations from the strains were determined by using the p-NP (p-Nitrophenol) standard curve, which was derived by using different volumes of 1 mM p-NP solution. Unit enzyme activity was calculated as the amount of enzyme required to release 1 mM of p-NP from p-NPL in one minute.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.7.2 Protease activity assay\u003c/h2\u003e \u003cp\u003eThe protease activity was computed by the spectrophotometric method described by [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] with few modifications. Following 24 hours of incubation, CFS was obtained by centrifugation of bacterial culture broth at 10,000 rpm at 4\u0026ordm;C for 10 minutes. The substrate solution contains 2% (w/v) casein in 0.02M phosphate buffer of pH 7. To 1 mL of substrate solution, 1 mL of enzyme solution was added, and the mixture was incubated at 37℃ for 30 and 60 minute time intervals. Further, 2 mL of 10% (w/v) trichloroacetic acid (TCA) was added to stop the reaction. The supernatant was obtained by centrifugation of the reaction mixture at 3000 rpm for 10 minutes at 4℃. The 2.5 mL of 0.4 M Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e and 0.5 mL Folin\u0026rsquo;s-Ciocalteu reagent were mixed with 0.5 mL of the supernatant. After mixing, it was incubated for 20 minutes at 40\u0026deg;C and cooled by using an ice bath, and the absorbance value was measured at 680 nm. Reagent blank (2 mL phosphate buffer), substrate blank (1 mL of substrate solution\u0026thinsp;+\u0026thinsp;1 mL of 0.02 M phosphate buffer), and enzyme blank (1 mL of supernatant of skimmed milk broth without strain\u0026thinsp;+\u0026thinsp;1 mL of substrate solution) were also conducted similarly.\u003c/p\u003e \u003cp\u003eThe tyrosine standard curve was obtained by using different volumes of 1000 \u0026micro;M tyrosine solution and was used as a reference to measure the concentration of protease from the strains. One unit of protease activity was defined as the amount of protease capable of hydrolyzing casein to generate 1 \u0026micro;g tyrosine under the above conditions. The protease activity was represented in the units of U/mL and U/mg, corresponding to the ratio of protease activity to the amount of protease solution and the protease activity per milligram protein, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Evaluation of biodegradation capacities based on the percentage of COD reduction:\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.8.1 Inoculum preparation\u003c/h2\u003e \u003cp\u003eA 10% (V/V) of freshly grown cultures were inoculated in a test tube containing nutrient broth. The test tubes were incubated at 37℃ for 24 hrs. Subsequently, the cultures were mixed together in equal proportion to form a consortium. The optical density of individual inoculum and of a consortium was measured at A\u003csub\u003e600\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.8.2 Evaluation of degradation capacity of strains\u003c/h2\u003e \u003cp\u003eThe percentage of biodegradation efficacy of selected strains was evaluated based on COD reduction. Degradation studies were carried out in the test tubes containing simulated dairy wastewater (based on the equalization tank characteristics presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The simulated dairy wastewater was prepared by using 65 times diluted milk to a suspended solid concentration of 1885 mg/L, which was supplemented by tributyrin oil (to adjust C/N 24) and HCl/NaOH (to adjust pH 7). The inoculum (10% V/V) of the isolates and a consortium was added into the medium and incubated at 37\u0026deg;C for up to 24 h. The test tubes containing the un-inoculated medium were kept as a control. The Closed Reflux, Colorimetric Method (5220 D) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] was used to measure COD. The CFS for COD analysis was obtained by centrifuging aliquots of samples at 10,000 rpm for 10 minutes at 4\u0026deg;C. All the experiments were conducted in triplicate, and the standard deviation was determined for confirmation of the reproducibility of the experimental trials.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of dairy wastewater\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScreen Chamber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOil and Grease Outlet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEqualization\u003c/p\u003e \u003cp\u003eTank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReturn Activated Sludge\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilky White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMilky White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMilky White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBrown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOD (mg/L)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOD (mg/L) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil and Grease (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Solids (mg/L)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Volatile Solids (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Suspended Solids (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolatile Suspended Solids (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Carbohydrates (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReducing sugar (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon %*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen %*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*\u003csup\u003ethe parameter considered for bio\u0026minus;mimicking the dairy waste effluent\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe screening of lipolytic and proteolytic bacteria, enzyme assay, and the evaluation of the degradation capacity of the screened strains were performed in independent triplicates. The results obtained were expressed as the mean and standard deviation. The data were analyzed statistically using GraphPad Prism 10.4.0 software. A one-way ANOVA (Analysis of Variance) followed by the Tukey post-hoc multiple comparisons test was performed to statistically assess the differences between the mean values. Values that differ by more than 95% from each other (i.e., at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results \u0026 Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of dairy industry wastewater\u003c/h2\u003e \u003cp\u003eThe characteristics of dairy effluent vary widely depending on the characteristics of raw milk, the type of system, the operational procedure used for manufacturing the different products, and the amount of freshwater used for washing and cleaning the machinery [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The detailed dairy wastewater characteristics were studied and summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to simulate the dairy wastewater conditions. The pH, COD, TS, and C/N ratio are important factors considered to formulate the simulated wastewater composition. These factors have a significant impact on the performance of biological wastewater treatment.\u003c/p\u003e \u003cp\u003eThe pH indicated that the dairy effluent was alkaline, ranging between 11 and 12. This result is analogous to the statement given by Saxena et al., [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which mentioned that the pH of the dairy wastewater varies between 6.6 and 12.2. The alkaline pH is due to the presence of nutrients, organic matter, and detergents used for cleaning [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The present analysis indicated that the COD of the effluent was 1360 mg/L, which was slightly reduced to 1240 mg/L at the outlet of the O\u0026amp;G removal tank but increased again to 1360 mg/L in the equalization tank. The higher COD is attributed to fats, nutrients, casein, lactose, and salts [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Since fats contributed to higher COD, its removal at the O \u0026amp; G outlet resulted in a slight reduction in COD. However, adding acid or alkali to adjust the pH (from 12 to 9) in the equalization tank led to an increase in COD. The COD and BOD values fall within the range specified by Mehrotra et al., [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] for Indian dairy industries.\u003c/p\u003e \u003cp\u003eAnother important factor that has been discussed less frequently is the Biodegradability Index (B.I.), which is the ratio of BOD to COD [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It is a significant indicator for selecting the wastewater treatment process. The present characteristics showed 0.49 B.I., which indicates that dairy effluent, can be treated by biological processes (Metcalf and Eddy, 2003). However, it requires bioaugmentation of potent microorganisms to initiate the biological process [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which is a key objective of the present study.\u003c/p\u003e \u003cp\u003eDuring any dairy wastewater treatment process, primary treatments such as SC and O\u0026amp;G removal are necessary. Hence, the simulated wastewater was prepared to match the characteristics of the equalization tank and was used throughout all the evaluation experiments. This simulated dairy effluent was employed in all the evaluation experiments involving selected strains to minimize interference from the natural flora in wastewater.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Isolation and characterization of bacteria\u003c/h2\u003e \u003cp\u003eA total of 75 morphologically different bacterial colonies were obtained from 16 samples from four different stages of DETP. Amongst them, 42 isolates (from NA-20, MA-12, and TA-10) were obtained by enrichment isolation (EI), and 33 isolates (from NA-12, MA-7, TA-11, and MSMA-3) were obtained by direct isolation (DI). EI gives more isolates than DI because the enrichment allows the growth of even less dominant bacteria in a controlled environment. The large sample size helps to obtain the maximum number of efficient bacterial isolates.\u003c/p\u003e \u003cp\u003eThe colony morphology reveals that 83% of isolated colonies were circular and 17% were irregular in shape with an entire or serrated margin. The diameter of isolated colonies varies from pinpoint to 15 mm. The colour of isolated colonies was yellow, cream, black, or buff. These colonies had either flat or elevated surfaces with opaque or translucent opacity. The colonies had either normal or sticky consistency. Cell morphology shows that the majority of the cells were in rod shape, and very few of them were in circular shape. The cell arrangement was in a chain, singly present, or in a bunch. Some of the cells of isolated colonies were motile, while others were non-motile. Further, cell morphological characteristics show that the majority of isolates from DETP were Gram-negative (62 isolates), and only 13 isolates were Gram-positive bacteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Strategic screening of lipolytic and proteolytic bacteria\u003c/h2\u003e \u003cp\u003eThe screening of obtained isolates was employed through a very organized approach. In the present study, the following three important stages were used for the first time for screening and development of consortia for degradation of dairy wastewater: hydrolytic activity of the isolates, extracellular enzyme-producing isolates, and rate of diffusion. Out of 75, the 4 most potent bacterial isolates were selected after meticulous screening to form consortia for the effective degradation of simulated dairy wastewater. Out of these four isolates, 3 were obtained by enrichment isolation, and 1 was obtained by direct isolation. The Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows colony and morphological characteristics as well as extracellular hydrolytic activity of selected four strains.\u003c/p\u003e \u003cp\u003eAs stated by Mazzucotelli et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the likelihood of obtaining an organism with specific hydrolytic capabilities increased when the isolation was performed from wastewater plentiful with a specific substrate of enzymatic interest. The similar principle was followed for primary screening of bacterial isolates with specific substrate degradation capacities. Consequently, a primary qualitative screening was carried out to select lipolytic and proteolytic bacteria, as lipids and proteins constitute the predominant carbon sources in dairy wastewater. The results of the primary screening reveal that 56 out of 75 (74.667%) isolates exhibited hydrolytic activity. Specifically, 45 isolates showed lipolytic, 3 showed proteolytic, and 8 showed lipolytic as well as proteolytic activity (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, the majority of strains from dairy effluent are lipolytic bacteria that hydrolyze fatty acids in the process of fat degradation. It shows strong lipolytic activity, which is important to break fats and oil during the bioremediation process. Therewithal, the present results were in line with the study by Kulzhanova et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which suggests that the number of proteolytic bacteria that break down proteins found in dairy wastewater is low.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIsolates obtained from direct and enrichment isolation and screening of lipolytic and proteolytic bacteria.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSr. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNutrient Agar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMacConkey\u0026rsquo;s Agar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eTributyrin Agar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eSkimmed Milk Agar\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eEI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNSC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDMSC1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMSC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDTSC1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETSC1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDSSC1 *+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNSC3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSC2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDMSC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMSC2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDTSC4 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETSC2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDSOG1 +\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNSC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSC3 *+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDMSC3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMSC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDTSC8 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETOG1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDSCT1 +\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNOG1 +\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSC4 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDMSC4 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMSC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDTAT1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETOG2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNOG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSC5 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDMAT1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDTAT2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETOG3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNAT1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENOG1 *+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDMAT2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMAT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDTAT3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETOG4 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNAT2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENOG2 *+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDMAT3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMAT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDTCT1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETCT1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNAT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENOG3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMCT1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDTCT2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETCT2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNAT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENOG4 *+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMCT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDTOG1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETCT3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNCT2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENOG5 *+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMCT3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDTOG2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETAT1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNCT3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENCT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMOG1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDTOG3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNCT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENCT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEMOG2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENCT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENCT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENCT5 *+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENAT1 *+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENAT2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENAT3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENAT4 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENAT5 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003eDI \u0026ndash; direct isolation, EI \u0026ndash; enrichment isolation\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003e*: isolates that show hydrolytic lipolytic activity,\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003e+: isolates that show hydrolytic proteolytic activity\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003e*+: isolates that show both lipolytic as well as proteolytic activity\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the second stage of screening, the extracellular enzyme activity of selected isolates was assessed on TA and MSMA in triplicate. The mean and standard deviation of the diameter of the zone of clearance around the well on TA and MSMA are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively. The results indicate that almost all isolates exhibited extracellular enzyme activity. Only 9 out of 56 isolates failed to demonstrate such activity. Among these exoenzyme-producing isolates, 15 were identified as significant extracellular lipase-producing bacteria, with a zone of clearance ranging from 15 to 18 mm within 24 h of incubation on TA. The maximum zone of clearance of 18 mm in diameter was observed for strain DMAT1. Additionally, 3 isolates demonstrated extracellular proteinase activity with a considerable zone of clearance between 12\u0026ndash;15 mm within 24 h of incubation on MSMA. Among these, isolate DSSC1 exhibited the highest proteolytic activity, with a maximal clearance zone of 15 mm in diameter. In consequence, 10 isolates were selected for further screening, which is based on a diffusion assay. These extracellular lipases and proteinases are predominantly associated with the hydrolysis /biodegradation of lipids and proteins [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These extracellular enzyme-producing bacteria have wide industrial applications as well as are efficient for bioremediation processes of effluent containing high concentrations of oil and fats [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The significance of selecting exoenzyme-producing bacteria is that these exoenzymes hydrolyze proteins, and lipids into soluble monomers making them easily accessible for bacterial absorption and metabolism to generate energy and support cell growth [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Consequently, the present screening approach facilitates the selection of exoenzyme-producing bacteria with the potential to enhance the degradation efficiency of target contaminants, such as fats and proteins [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA one-way ANOVA was performed to compare the means of the diameter of the zone of clearance on TA as well as MSMA. A one-way ANOVA revealed that there was a statistically significant difference in the mean diameter of the zone of clearance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As there was a significant difference among means of diameter, a Tukey post-hoc test was performed. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e, bars sharing the same letter are not significantly different according to Tukey multiple comparisons analysis.\u003c/p\u003e \u003cp\u003eIn the final stage of screening, 4 out of 10 isolates were selected based on the diffusion kinetics. In this assay, isolates with a higher rate of diffusion as well as exhibited two hydrolytic activities were selected to form a consortium for the bioaugmentation strategy. ETOG2 showed the fastest rate of lipid degradation with a rate of diffusion of 0.71 mm/h on TA with an 8.5 mm increase in the zone of clearance within 12 h of incubation. Whereas the fastest protein degradation was shown by isolate ENOG5 with a maximum rate of diffusion of 0.83 mm/h on MSMA with a 5 mm increase in the zone of clearance within 6 h of incubation. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e represents data based on the rate of diffusion of these four selected isolates. Among these four isolates, three isolates (DSSC1, ENOG5, and ENAT1) showed lipolytic as well as proteolytic activity, and ETOG2 showed only lipolytic activity but with a very high rate of diffusion. The lipolytic and proteolytic bacteria produce a zone of hydrolysis when grown on tributyrin and skimmed milk agar, respectively. During hydrolysis, these bacteria produce lipase and proteinase enzymes which are diffused through the agar medium and degrade the substrate present in the agar medium. It creates a zone of clearance around the colony. Hence, the agar diffusion assay is an easy method to check the biodegradation potential of bacteria. This innovative approach of agar diffusion assay measures the time required by bacterial enzymes to diffuse through and hydrolyze the specific substrate in agar medium. This technique gives more sensitive results as compared to others. The rate of diffusion also provides an outline of the hydraulic retention time required for biological treatment processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Phylogenetic identification and growth curve study of selected bacterial isolates\u003c/h2\u003e \u003cp\u003eThe selected four most potential bacterial isolates showed 99% similarity with \u003cem\u003eMassilia haematophila\u003c/em\u003e (DSSC1), \u003cem\u003eBrevibacillus agri\u003c/em\u003e (ENAT1), \u003cem\u003ePseudomonas guguanensis\u003c/em\u003e (ENOG5), and \u003cem\u003eLysinibacillus fusiformis\u003c/em\u003e (ETOG2), within the NCBI database. The 16S rRNA gene sequences obtained was submitted to the NCBI database, and their accession numbers are stated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdentification of selected potential bacterial strains and their similarity with respect to the NCBI database.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSources of isolates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsolation No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eName of closest related species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimilarity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNCBI database accession\u003c/p\u003e \u003cp\u003enumber\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScreen Chamber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDSSC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMassilia haematophila\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOQ733357.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOil and Grease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENOG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas guguanensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOQ733358.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eETOG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLysinibacillus fusiformis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOQ733359.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReturn Activated Sludge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBrevibacillus agri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOQ733360.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the four selected strains, \u003cem\u003eMassilia haematophila\u003c/em\u003e was identified for the first time as a potential candidate for dairy wastewater treatment. This species remains relatively underexplored in the context of biodegradation of contaminants. Previous study have investigated the effective degradation of BTEX compound and Poly(3-hydroxybutyrate) by \u003cem\u003eMassilia\u003c/em\u003e sp. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe isolation of \u003cem\u003eLysinibacillus\u003c/em\u003e from a wastewater treatment system is common, as it is pervasive and diverse in the environment. It can catabolize various natural and xenobiotic compounds. The ability of \u003cem\u003eLysinibacillus\u003c/em\u003e sp. to survive under extremely harsh conditions makes it the most deserving strain for bioremediation in a contaminated environment [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe reported efficient lipase-producing bacteria \u003cem\u003eBacillus safensis\u003c/em\u003e, S9 \u003cem\u003ePseudomonas alcaliphila\u003c/em\u003e ED1, \u003cem\u003eNeisseria ovis\u003c/em\u003e, \u003cem\u003eBacillus subtilis, Bacillus licheniformis, Bacillus pumilus, Bacillus acidophilus, Bacillus coagulans\u003c/em\u003e, and \u003cem\u003eBacillus stearothermophilus\u003c/em\u003e are isolated and identified from dairy wastewater [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Whereas proteinase enzyme producers \u003cem\u003eBacillus isronensis, Acinetobacter moffi, Micrococcus mycoides, Bacillus mycoides, Pseudomonas aeruginosa, Bacillus subtilis, and Pseudomonas fluorescence\u003c/em\u003e were isolated from dairy wastewater for different industrial applications [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Also, bacterial strains \u003cem\u003eBacillus subtilis, Escherichia coli, Lysinibacillus sphearicus, Bacillus cereus, Bacillus thuringiensis, Bacillus cereus\u003c/em\u003e, and \u003cem\u003eBrevibacillus brevis\u003c/em\u003e were isolated and identified from dairy wastewater and dairy sludge by Garcha et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These isolates were selected based on BOD, TSS, and O \u0026amp; G reduction efficiencies instead of lipolytic and/or proteolytic activity.\u003c/p\u003e \u003cp\u003eFrom this literature, it is evident that \u003cem\u003eBacillus\u003c/em\u003e sp. and \u003cem\u003ePseudomonas\u003c/em\u003e sp. are found in high abundance in dairy wastewater which were also isolated from our wastewater. This shows that \u003cem\u003eBacillus\u003c/em\u003e sp. and \u003cem\u003ePseudomonas\u003c/em\u003e sp. seems to be dominant in dairy industry wastewater. Similarly, it is also stated by Loperena et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and Custodio et. al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], that an inoculum containing \u003cem\u003ePseudomonas\u003c/em\u003e sp. and \u003cem\u003eBacillus\u003c/em\u003e sp. might be a good option to enhance the biodegradable efficiency of dairy effluent. These strains are known for their good lipolytic and proteolytic activity.\u003c/p\u003e \u003cp\u003eThe growth curve study of selected isolates shows that isolate ENAT1 (0.029 \u0026micro;/min) and ETOG2 (0.0085 \u0026micro;/min) have maximum specific growth rates at 25℃ and 37℃, respectively. The doubling time of isolate DSSC1, ENOG5, ETOG2, and ENAT1 at 25℃ was 0.51, 0.46, 0.47, and 0.39 h, respectively, whereas the doubling time of isolate DSSC1, ENOG5, ETOG2, and ENAT1 at 37℃ was 1.41, 2.73, 1.35, and 2.5 h, respectively. Hence, the data reflects that these 4 strains took less than an hour for their growth and activity at nearly 25℃ temp or at ambient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Study of lipase and proteinase enzyme activity\u003c/h2\u003e \u003cp\u003eThe four strains were selected as lipolytic and proteolytic bacteria. Hence, validation of the results of the diffusion of extracellular enzymes was employed through enzyme assays. The lipase activity shown by DSSC1, ENOG5, ETOG2, and ENAT1 (represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) was in the range of 0.0028 to 0.0063 U/mL/min, and protease activity shown by DSSC1, ENOG5, and ENAT1 was in the range of 13 to 14 U/mL/min (represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), respectively. All these four strains showed an effective pattern of enzyme activity. Also, these strains showed maximum activity within 30 minutes of incubation time. The one-way ANOVA of the lipase activity (P\u0026thinsp;=\u0026thinsp;0.08455) and proteinase activity (P\u0026thinsp;=\u0026thinsp;0.13095) shows that there is no significant difference in the mean value of data obtained.\u003c/p\u003e \u003cp\u003eThese results showed the amount of enzymes produced by bacteria within a specific incubation period at a particular temperature and pH. The summary of results of the diameter of the zone of clearance by exoenzyme, enzyme assay, and rate of diffusion are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The results indicate that the maximum zone of clearance (degradation zone) was observed for strain ENAT1 on TA, whereas maximum lipase production was observed for strain ETOG2. However, strain DSSC1 showed the maximum zone of clearance in MSMA, and maximum protease production was observed for ENAT1 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Similarly, no correlation was found between enzyme production and degradation efficiency in the study by Hermes et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] study. Both analyses were performed on the same samples, and the results show that the maximum COD reduction was 44.96% with a corresponding lipase production of 3.54 U/mL/min, whereas the maximum lipase production was 4.87 U/mL/min with a COD reduction of 33.54%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the screening assays and enzyme assay\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eName of Isolate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDSSC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENAT1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eENOG5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eETOG2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eName of closest related species\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMassilia haematophila\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBrevibacillus agri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas guguanensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eLysinibacillus fusiformis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLipolytic Activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDiameter of zone of clearance by exoenzyme on TA (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRate of diffusion on TA (mm/h)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLipase Assay (Units/mL/min)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eProteolytic Activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDiameter of zone of clearance by exoenzyme on MSMA (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRate of diffusion on MSMA (mm/h)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eProteinase Assay (Units/mL/min)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn contrast, the study conducted by Kashyap and Dubey [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] suggests the correlation between the diameter of the zone of clearance and lipase enzyme production with BOD removal efficacy. The particular study indicates that the \u003cem\u003eBacillus\u003c/em\u003e strain showed the maximum zone of clearance on TA and lipase production with the highest BOD removal efficacy of dairy wastewater. However, these three tests were performed at different incubation temperatures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.6 COD reduction efficacy of selected strains\u003c/h2\u003e \u003cp\u003eThe industrial effluent is toxic to the environment due to the presence of organic substances that are slowly biodegradable. This is the reason many industrial effluents often have higher COD than BOD [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Hence, the biodegradation capacity of individual strains and consortia was evaluated in terms of COD reduction efficacy, as COD represents the total concentration of organic matter in wastewater and is an important parameter for evaluating the efficiency of biological wastewater treatment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of the percentage reduction of COD by selected isolates are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The corresponding environmental factors were 7 pH, 24 C/N, 10% inoculum concentration, 37℃ temperature, and 24 h incubation time. The results were statistically analyzed by one-way ANOVA, and the analysis suggests that the obtained results were 98.98% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) statistically significant. Hence, further pairwise comparison by Tukey post-hoc test was performed, and results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The Tukey multiple comparison test indicates a significant difference between DSSC1-consortia (P\u0026thinsp;=\u0026thinsp;0.016), ENAT1-consortia (P\u0026thinsp;=\u0026thinsp;0.0293), and ETOG2\u0026ndash;consortia (P\u0026thinsp;=\u0026thinsp;0.0149). The result shows that the maximum efficacy of 35% was achieved by isolate ENGO5 with a corresponding 1335 mg/L reduction of COD, whereas the COD removal efficacy increased to 43% with a 1625 mg/L reduction of COD in the case of augmentation of consortia. The study was conducted with simulated dairy wastewater with fewer nitrogen and phosphorous sources. Hence, there is comparatively less COD removal, possibly due to the enervation of nitrogen and phosphorus in the medium [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the statement made by [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], the present study also confirms that the interactive effect of selected strains when used in a consortium does not vanquish the biodegradability of individual strains; instead, all strains cumulatively increase the degradation of organic contaminants. The interaction of microorganisms in a consortium will facilitate their successful establishment and functioning in the environment, where they will be bio-augmented for suitable treatment.\u003c/p\u003e \u003cp\u003eThe present study shows higher reduction in COD in 24 h of incubation. The study by [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], \u003cem\u003eBacillus\u003c/em\u003e sp. showed a higher reduction in COD, whereas a consortium of 8 isolates of \u003cem\u003eBacillus\u003c/em\u003e sp., \u003cem\u003ePseudomonas\u003c/em\u003e sp., \u003cem\u003eand Acinetobacter\u003c/em\u003e sp. showed a similar efficiency of 57% reduction in COD. It shows that with a consortium consists of higher number of strains, there is also no significant difference in COD reduction efficacy within 48 h of incubation and 30℃ temperature.\u003c/p\u003e \u003cp\u003eSimilarly, \u003cem\u003eLactobacillus plantarum\u003c/em\u003e and \u003cem\u003eLactobacillus casei\u003c/em\u003e showed a reduction in COD by 71.6% and 60.8%, respectively, with a 1% inoculum, whereas a consortium of these \u003cem\u003eLactobacillus\u003c/em\u003e sp. showed a COD reduction of 75.8% with the same inoculum concentration, which is 6% higher than that of the individual strain [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The present study also indicates a higher COD reduction by bacterial strains than by yeast strains. Similarly, Porwal et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], also reported a higher reduction by bacterial strains than by yeast strain. This study suggests that a higher reduction in COD (46.93%) was achieved by the \u003cem\u003eBacillus\u003c/em\u003e strain, whereas a consortium of bacteria and yeast strains increased COD reduction by 8%, reaching 50.65%. Furthermore, the COD reduction was greater in the microbial consortium-treated effluent than in the sludge that was activated without the microbial consortium [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom all these observations, it is evident that, in the present study, COD reduction was increased significantly by 26% by a consortium than the maximum COD reduction by individual strains. This shows that the novel approach of diffusion assay based on the kinetics of diffusion for the selection of bacterial strains gives more efficient bacterial consortia. Further study involves optimizing operating parameters to increase the efficiency of the treatment process by bioaugmentation of selected four strains in equal proportion.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn conclusion, the development of bacterial consortia for bioaugmentation presents a promising approach to efficiently treat dairy wastewater. The selected strains, \u003cem\u003eMassilia haematophila\u003c/em\u003e (DSSC1), \u003cem\u003eBrevibacillus agri\u003c/em\u003e (ENAT1), \u003cem\u003ePseudomonas guguanensis\u003c/em\u003e (ENOG5), and \u003cem\u003eLysinibacillus fusiformis\u003c/em\u003e (ETOG2), demonstrated significant biodegradation potential, with consortia exhibiting a 26\u0026ndash;86% higher reduction in chemical oxygen demand (COD) compared to individual strains. Among these strain \u003cem\u003eMassilia\u003c/em\u003e sp. was reported for the first time for biological degradation of dairy wastewater. The consortia demonstrated COD removal of approximately 1700 mg/L, which is significantly higher than the average dairy effluent COD (~\u0026thinsp;1300 mg/L). The findings of present study also suggest that the amount of enzyme produced by bacterial strains may not be directly proportion to the degradation of organic components. Hence, the diffusion-based screening method proved effective in selecting potent bacterial strains, offering a novel strategy for improving dairy wastewater treatment. This study highlights the potential of bacterial consortia as a sustainable solution for mitigating the environmental impact of dairy industry effluents.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the Bioenergy Group, MACS-Agharkar Research Institute, Pune, India for providing lab facility and instrument facilities to carry out the present work, also Fergusson College and Department of Technology, SPPU, Pune for support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConflict of interest: The authors declare no known competing financial interests or personal relationships with other people or organizations that could inappropriately influence (bias) the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eManjiri Patil (MP):\u0026nbsp;\u003c/strong\u003eExecution, Formal Analysis, Data curation, Investigation, Methodology, Writing \u0026ndash; original draft. \u003cstrong\u003ePranav Kshirsagar (PK):\u0026nbsp;\u003c/strong\u003eConceptualization, and Methodology, Writing \u0026ndash;\u0026nbsp;review \u0026amp;\u0026nbsp;editing.\u003cstrong\u003e\u0026nbsp;Prashant Dhakephalkar (PD):\u0026nbsp;\u003c/strong\u003eConceptualization\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003ereview and editing, \u003cstrong\u003eSuneeti Gore (SG): \u0026nbsp;\u0026nbsp;\u003c/strong\u003eConceptualization\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eSupervision,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash;\u0026nbsp;review and editing\u003cstrong\u003e\u0026nbsp;Vikram Lanjekar (VL):\u0026nbsp;\u003c/strong\u003eConceptualization, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors do not have permission to share data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no funding from any external source,\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eA. 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Yuvaraja\u0026rsquo;s College, Mysore.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bioprocess-and-biosystems-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Bioprocess and Biosystems Engineering](https://www.springer.com/journal/449)","snPcode":"449","submissionUrl":"https://submission.nature.com/new-submission/449/3","title":"Bioprocess and Biosystems Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dairy Wastewater, Lipolytic, Proteolytic, consortium, bioaugmentation, COD reduction","lastPublishedDoi":"10.21203/rs.3.rs-6410986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6410986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe dairy industry generates wastewater characterized by organic components, predominantly composed of proteins and fats, which can be effectively treated through biological processes. The present study aimed to develop a bacterial consortium for bioaugmentation to enhance the treatment of simulated dairy wastewater. A total of 75 bacterial isolates were obtained using Direct Isolation (DI) and Enrichment Isolation (EI) methods. Among these, four strains exhibiting the highest proteolytic and lipolytic activities within 24 hours were selected for further investigation. The isolates were screened based on their extracellular enzyme activities (proteinase and lipase), as well as their maximum lipolytic (0.3–0.7 mm/h) and proteolytic activity (0.67–0.83 mm/h) by a novel approach of rate of diffusion on Tributyrin Agar (TA) and Modified Skimmed Milk Agar (MSMA), respectively. The selected strains were identified by 16S rRNA gene sequencing as \u003cem\u003eMassilia haematophila \u003c/em\u003e(DSSC1)\u003cem\u003e, Brevibacillus agri \u003c/em\u003e(ENAT1)\u003cem\u003e, Pseudomonas guguanensis \u003c/em\u003e(ENOG5)\u003cem\u003e, and Lysinibacillus fusiformis \u003c/em\u003e(ETOG2\u003cem\u003e)\u003c/em\u003e. The biodegradation potential of individual strains and their consortium was assessed through Chemical Oxygen Demand (COD) reduction in simulated dairy wastewater. The individual bacterial strains achieved COD reductions from an initial concentration of 3815 mg/L to 2950, 2813, 2480, and 2893 mg/L. In contrast, bioaugmentation with the bacterial consortia reduced COD to 2190 mg/L, resulting in a 26–86% higher reduction compared to the individual strains. This study presents the first report on the use of a novel approach of diffusion-based assay to develop an effective and innovative bacterial consortium for efficient dairy wastewater treatment. These findings highlight the potential of this approach towards enhancing biodegradation efficiency and advancing sustainable wastewater management practice.\u003c/p\u003e","manuscriptTitle":"Innovative Bacterial Consortia for Simulated Dairy Wastewater Treatment: Improving COD Removal Efficiency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 13:22:45","doi":"10.21203/rs.3.rs-6410986/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-04T20:32:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-04T20:12:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306964473333862147648981624790901900095","date":"2025-05-29T00:20:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-25T23:01:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-25T04:09:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-24T11:50:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102454698871226383000806754989903084202","date":"2025-05-15T07:51:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120005337291031512644597319061721145826","date":"2025-05-14T20:22:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82717927924303217155414083979551991087","date":"2025-05-12T05:53:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46476647755324134694463202739136271077","date":"2025-05-04T12:09:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-23T00:20:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-15T02:00:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-15T00:32:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bioprocess and Biosystems Engineering","date":"2025-04-09T10:34:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bioprocess-and-biosystems-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Bioprocess and Biosystems Engineering](https://www.springer.com/journal/449)","snPcode":"449","submissionUrl":"https://submission.nature.com/new-submission/449/3","title":"Bioprocess and Biosystems Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"91669624-fdf5-4f08-b429-c4f13e5e6a5a","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-01T18:38:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-28 13:22:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6410986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6410986","identity":"rs-6410986","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}