Sustainable Valorization of Sugarcane Molasses into Polyhydroxybutyrate by Pseudomonas plecoglossicida MK-6 and Optimization Using Response Surface Methodology

Sustainable Valorization of Sugarcane Molasses into Polyhydroxybutyrate by Pseudomonas plecoglossicida MK-6 and Optimization Using Response Surface Methodology | 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 Sustainable Valorization of Sugarcane Molasses into Polyhydroxybutyrate by Pseudomonas plecoglossicida MK-6 and Optimization Using Response Surface Methodology Habiba Begum, Asma Bibi, Nosheen Bibi, Aisha Ghani, Walid F. A. Mosa, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9174495/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Polyhydroxybutyrate (PHB), is a sustainable, and green alternative to petrochemical plastic, however, its production cost remains high due to reliance on glucose. This study was designed to screen PHB producing bacteria from dumping soil to evaluate PHB production using agro-industry waste of sugarcane molasses as carbon source in reference to an efficient waste recycling practice. The PHB positive strain GSBB-5a2-2 was screened by using Sudan Black B and Nile Blue A and was identified as Pseudomonas plecoglossicida MK-6 based on 16S rRNA sequencing (GenBank accession number OQ236410), was selected for further analysis. The ideal fermentation variables were determined using single factor optimization, the maximum PHB (7.8 g/L) was obtained with 5% sugarcane molasses, ammonium sulfate as a nitrogen source, 8:1 C/N ratio, pH 7, 37°C and 150 rpm for 48hrs were found as optimum factors. Further optimization using Response Surface Methodology with Box-Behnken Design enhance PHB synthesis. The extracted PHB was identified via Fourier transform infrared spectroscopy (FT-IR). PHB production by Pseudomonas plecoglossicida MK-6 has been previously documented; however, this study reports for the first time to optimize PHB synthesis by this strain using sugarcane molasses as a low-cost carbon substrate, contributing to circular bio economy goals. PHB Pseudomonas plecoglossicida MK-6 Sugarcane Molasses 16S rRNA FT-IR Box-Behnken Design Response Surface Methodology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Regardless of the extensive utilization of synthetic plastics in human society, their drawbacks are becoming more apparent over time. Production of synthetic plastics depends on petroleum, a nonrenewable energy resource [ 1 ]. Synthetic plastics pose a major environmental concern because their resistance to microbial degradation allows them to persist in ecosystems for long periods, leading to accumulation and pollution [ 2 ]. Microplastics increase the toxicological profile of plastics and induces more harmful effects in marine animals. Ingestion of plastics can lead to intestinal obstruction, endocrine system disturbance, neurotoxicity, cancer and reproductive abnormalities [ 3 ]. These environmental and societal issues have prompted the development of green polymers produced by many microorganisms [ 4 ]. Polyhydroxybutyrate (PHB) is a linear biopolymer exhibiting flexible characteristics comparable to conventional plastics, making it an attractive alternative for synthetic plastics [ 5 ]. PHB is produced by many microorganisms from sustainable sources under conditions of nitrogen limitation and carbon abundance, and degrades rapidly into compost [ 6 ]. PHB is used in tissue engineering, cardiovascular products and delivery of medicines, in addition to its applications in food packaging and agricultural industries [ 7 ]. PHB biopolymers serve as intracellular carbon and energy storage compounds synthesized by various microorganisms, including Alcaligenes , Pseudomonas , Staphylococcus , Ralstonia , Azotobacter , Bacillus , as well as certain microalgae [ 8 ]. Ralstonia eutropha is the model organism for the Polyhydroxyalkanoates (PHA) production and stores PHB up to 90% of its cell dry weight from various carbon sources [ 9 ]. Many species of Pseudomonas such as Pseudomonas aeruginosa, Pseudomonas putida, Pseudomonas resinovorans , Pseudomonas mendocina , and Pseudomonas chlororaphis have been thoroughly investigated for PHB production due to their flexibility and capability to use multiple sources of carbon, and are viewed as attractive candidates in a number of biotechnological applications [ 10 ]. However, PHB production remains economically challenging primarily due to high cost of carbon source. Therefore the use of recyclable and readily available carbon sources is crucial for improving process feasibility and promoting sustainable PHB production within a circular bioeconomy framework [ 11 ]. Several wastes including sugarcane bagasse, sugarcane molasses, whey, corn cob, and rice husk have been explored as cheap carbon sources for biopolymer synthesis [ 12 ], as was produced using alkaline pretreated sugarcane bagasse by Pseudomonas monteilii [ 13 ]. Alcaligenes faecalis RZS4 and Pseudomonas sp . RZS1 evaluated various agricultural wastes, such as maize waste, rice straw, and wheat straw, as carbon source for PHB synthesis [ 14 ]. Several agricultural by-products have been investigated as alternative carbon substrates for PHB production by Bacillus sphaericus NCIM 5149 [ 15 ]. A co-culture system comprising Lysinibacillus sp. RGS and Ralstonia eutropha ATCC 17699 was established to enhance PHB production through the efficient utilization of sugarcane bagasse hydrolysate as a renewable carbon source [ 16 ]. Commercial sugarcane molasses has been effectively utilized by Cupriavidus necator as a low-cost carbon source, resulting in enhanced PHB production and maximum polymer yield under optimized conditions [ 17 ]. Pakistan is an agricultural country, and its economic growth is closely associated with the agriculture sector [ 18 ]. Sugarcane is a major cash crop of Pakistan, cultivated primarily for sugar production, and its processing generates multiple by-products including straw, press mud, wastewater, bagasse and molasses [ 19 ]. The agriculture by-products are often wasted without any value addition [ 20 ]. Among them, sugarcane molasses has been successfully investigated for the synthesis of biopolymers by microbial fermentation and hydrolysis. Utilizing several microbial strains, sugarcane molasses has been converted into various bio polymers [ 21 ]. Response Surface Methodology (RSM) is a statistical and mathematical approach widely applied for optimizing complex bioprocesses by evaluating the combined effects of multiple variables with a reduced number of experiments [ 22 ]. RSM has been successfully employed to enhance PHB production by identifying optimal fermentation conditions and improving yield efficiency [ 23 ]. Despite extensive research on polyhydroxybutyrate (PHB) production using various microbial strains and carbon sources, the utilization of Pseudomonas plecoglossicida for PHB synthesis from sugarcane molasses remains largely unexplored. The novelty of the present study lies in the valorization of an agro-industrial waste substrate into a high-value biopolymer using a less-studied bacterial strain. Furthermore, the application of RSM through Box–Behnken design for the simultaneous optimization of multiple fermentation parameters provides a systematic and efficient approach to enhance PHB yield. Therefore, the present study aims to investigate the potential of sugarcane molasses for PHB production by Pseudomonas plecoglossicida MK-6 and to optimize key fermentation parameters using RSM, thereby contributing to a cost-effective and sustainable strategy for bioplastic production. 2. Materials and Methods 2.1. The Samples Collection and Bacterial Isolation Technique Soil samples were collected randomly from multiple locations within the Khyber Pakhtunkhwa province of Pakistan. At the place of collection, parameters such as sample pH and temperature were measured. Multiple samples were collected from a depth of approximately 10 cm, and transported to the laboratory in sterile bags. The samples were stored at 4°C until further processing. The collected samples were given names based on their isolation or geographic distribution [ 24 ]. 2.2. Screening and Confirmation of PHB Producing Bacterial Strain(s) To test for PHB synthesis, all bacterial strains were treated with a 0.3% (w/v) Sudan Black B dye solution, which was poured onto the plates and left undisturbed for 30 minutes. Subsequently, the plates were rinsed with 75% ethanol to remove excess dye. Bacterial strains capable of PHB synthesis were identified by turning black [ 25 ]. Bacterial isolates (identified as positive by Sudan Black B staining) were inoculated on Minimal Salt agar(MSA) supplemented with 0.5 µg/mL Nile blue A dye, to verify the presence of PHB granules. Following a 24-hour incubation period at 37°C, the plates were analyzed using 312 nm UV light to look for fluorescence, which would indicate the presence of PHB [ 26 ]. 2.3. Molecular Identification and Phylogenetic Evaluation of the Selected Strain DNA was extracted using the phenol chloroform (Organic) method [ 27 ] 1% agarose gel was used for electrophoresis. Under the UV Trans-Illuminator bio Doc Analyzer, the gel was visualized. The gel image showed representative DNA bands in comparison to the 1KB Ladder. Nanodrop plate was used to analyze the quality and amount of DNA (Skanit RE 4.1, Thermoscientific, USA). Absorbance was measured at 260, 280, and 320 nm [ 28 ]. Amplification of a single copy or a specific sequence of DNA is accomplished using PCR, a molecular biology technique. The universal primer pair, forward primer (AGAGTTTGATCCTGGCTCA) and reverse primer (GGTTACCTTGTTACGACTT) were used for the bacterial DNA amplification. Polymerase chain reactions were performed on a Galaxy XP Thermal Cycler (BIOER, PRC) [ 29 ].The amplification product from PCR were confirmed by agarose gel electrophoresis and subsequently sequenced. The acquired sequences were modified and cleaned by using BioEdit software (version 7.2.5) [ 30 ]. The resulting 16S rRNA sequence was compared with the existing sequences in the NCBI database using the BLAST tool ( https://www.ncbi.nlm.nih.gov/ ). The Molecular Evolutionary Genetics Analysis program MEGA version 5.0 was used [ 31 ]. 2.4. Pretreatment of sugarcane Molasses Before use in fermentation process, sugarcane molasses was mixed with deionized water (1:4 (v/v)) and (20% (w/w)) activated charcoal and was stirred for one hour at room temperature. Centrifugation was performed to remove activated charcoal, the pH was adjusted to 7.0 and was autoclaved [ 32 ]. 2.5. Experimental PHB Production by One Variable at a Time (OVAT) Method PHB production is influenced by growth parameters including different nutrition sources in the media and other physical parameters. Using E2 media, the PHB production was optimized based on pH, temperature, carbon sources and concentrations, nitrogen sources, nitrogen limitation, carbon to nitrogen ratio, impact of agitation rate, and inoculum size. One parameter at a time was tuned, with the rest of the parameters remaining constant. 2.5.1. Optimization of Chemical Factors The PHB production affected by different carbon sources was determined by growing the selected isolate in 100 ml of minimal salt medium (MSM) supplemented with different carbon sources (glucose, fructose, sucrose, gluconic acid and sugarcane molasses) and the optimal carbon source was chosen based on the yield. Sugarcane molasses was utilized at concentrations of 2%, 5%, 8%, and 10% in 100ml of MSM to identify the optimal carbon source concentration. Similarly a variety of nitrogen sources (NH4Cl, NH4NO3, and CH3COONH, (NH4)2SO4,) and various concentrations of the carbon and nitrogen source (C/N ratios of 1:1, 3:1, 8:1, and 20:1) were also evaluated. The inoculum size has a great influence on PHB production. Fresh bacterial strains of 0.5%, 2%, 5% and 10% were grown. The bacterial culture was incubated for 48 hours and meant PHB was measured. Based on PHB production, the optimal inoculum size was determined. 2.5.2. Optimization of Physical Parameters Physiological factors play a pivotal role in optimum PHB production. The culture of selected isolate was inoculated in E2 media for different periods of time (24, 48 and 72 hrs) and at a pH range of (6, 7 and 8) were used to determine the optimum incubation time and pH respectively. Similarly, temperature (28°C, 37°C, and 40°C) and various agitation rates (100 rpm, 150 rpm, and 200 rpm) were also examined. Based on the number of products produced, the best agitation rate was determined. 2.6. Characterization of PHB by Fourier Transform Infrared Spectroscopy The key functional groups of PHB was examined by Fourier transform infrared (FTIR) spectroscopic investigation to confirm the chemical structure of the isolated polymer. The extracted and reference samples' infrared spectra were instantly analyzed using Spectrum FTIR Perkin Elmer USA spectroscopy with UATR accessory [ 33 ]. 2.7. PHB Production through Response Surface Methodology Prior to experimental PHB production, the response surface methodology (RSM), a statistical and mathematical tool, was applied to optimize yield based on selected significant variables, namely sugarcane molasses concentration, ammonium sulfate concentration, pH and temperature. A Box–Behnken Design (BBD) was employed to evaluate the combined effects of these independent variables on PHB production, while other physicochemical parameters were maintained at their previously optimized levels. Each variable was investigated at three coded levels (− 1, 0, and + 1), as presented in Table 1 . Table 1 Box–Behnken design variables and their coded levels for optimization of PHB production. Name of Variables Range and Level -1 0 +1 Sugarcane molasses(% v/v) (A) 2% 5% 10% Ammonium sulphate (g/L) (B) 0.1 2 4 pH (C ) 5 6 7 Temperature ( O C) (D) 30 35 40 A total of 28 experimental runs were generated and analyzed using R software (version 4.5.1). The experimental data obtained from the Box–Behnken design were subjected to analysis of variance (ANOVA), and the significance of the model terms was evaluated using the t-test at a confidence level of p < 0.05 [ 34 ]. The experimental data were fitted to the following polynomial model to analyze linear and quadratic effects. Y = β 0 + βA A + β B B + β C C + β D D + β AB ​ AB + β AC ​ AC + β AD ​ AD + β BC ​ BC + β BD ​ BD + β CD CD + β AA ​ A 2 + β BB B 2 + β CC C 2 + β DD ​ D 2 Where Y = PHB production g/L A= molasses B= ammonium sulfate C = pH D= temperature Experiments were run in triplicate, and the mean PHB concentration was used as the response. Statistical significance was evaluated using analysis of variance (ANOVA). Response surface and contour plots were generated to determine the optimal conditions for maximum PHB production. All statistical analyses were performed using R software (version 4.3.2). 3. Results 3.1. Screening and Confirmation of PHB Producing Bacterial Strain(s) Six distinct strains from polluted dumping soil from different areas in Pakistan's KPK province were isolated on nutrient agar plates. Sudan Black B and Nile Blue A dyes were used to confirm that three of the six bacterial isolates, PSM-1, SFM-5, and GSBB-5a2-2, were generating PHB (Fig. 1 a and 1 b). The strain, GSBB-5a-2, had shown the strongest fluorescence under UV and was selected as potent PHB producer. 3.2. Molecular Identification of, GSBB-5a2- 2 3.2.1. Genomic DNA Extraction, Amplification and Sequencing of 16S rRNA gene Genomic DNA (gDNA) of the GSBB-5a2-2 strain was extracted using the phenol–chloroform method. The DNA concentration was approximately 2281.8ng/µL, and the 260/280 absorbance ratio was 1.762, suggesting high quality of DNA. The 16S rRNA gene of the GSBB-5a2-2 strain was amplified by polymerase chain reaction (PCR) using 16S rRNA-specific primers. A distinct single band of approximately 1200 bp was observed on a 2% agarose gel (Fig. 2 ), which was subsequently excised and purified for sequencing analysis. Sequencing of the 16S rRNA gene Sequencing-based validation of the target gene was done. 3.2.2. Phylogenetic Analysis The 16S rRNA gene sequence of strain was analyzed to determine its phylogenetic relationship. BLAST analysis of the obtained sequence (GenBank accession no.OQ236410) showed 99.5% similarity with Pseudomonas plecoglossicida MK-6 (Fig. 3 ). Multiple sequence alignment and phylogenetic analysis were performed using MEGA version 5.0. 3.3. Optimization of Culture Medium Constituents for Pseudomonas plecoglossicida MK-6 Media composition plays important role to optimize the PHB yield. Various carbon sources, including galactose, glucose, fructose, dextrose, mannitol, sucrose, and sugarcane molasses were used to find out the most suitable carbon source for efficient PHB yield. Sugarcane molasses produced the highest PHB yield of 8.06 g/L, followed by glucose with 7.2 g/L PHB yield (Fig. 4a). To determine the optimal concentration of sugarcane molasses for PHB production, varying concentrations (2%, 5%, 8%, and 10%) were evaluated. The highest PHB yield (7.1 g/L) were obtained at 5%, whereas the lowest value 3.43g/L were observed at 10% (Fig. 4b). Further, ammonium sulphate proved to be the best nitrogen (N) source when four salts i.e. NH 4 NO 3 , CH 3 COONH, (NH 4 ) 2 SO 4 andNH 4 Cl were used in the growth medium, giving 6.5g/L PHB yield, followed by NH 4 Cl with 4.1g/L PHB ( Fig. 4c). Appropriate C/N ratio plays important role in PHB production. Various C/N ratios, 1:1, 3:1, 8:1 and 20:1, were tested to determine the best ratio. The 8:1 ratio was optimum PHB producing ratio, giving 6.9g/L PHB yield, followed by 20:1 with 5.2g/L PHB (Fig. 4d). Inoculum size is essential factor in optimization of culture parameters for PHB production as biomass has profound effect on production yield. In the current study, 0.5%, 2%, 5% and 10% (v/v), were evaluated. The maximum PHB yield (6.8 g/L) was achieved at an inoculum size of 5% (v/v), whereas both lower (0.5–2%) and higher (10%) inoculum levels resulted in reduced PHB production (Fig. 4e). 3.4. Optimization of Physical Parameters for Maximum PHB Production Physical factors Physical factors are very important for any biological and chemical procedures or their output. Incubation time is a key parameter for bacterial cell growth and product formation. In the current study, highest PHB of 6.4g/L was produced during 48hrs, followed by 24hrs with 2.6g/L of PHB (Fig. 5a). The pH plays important role in the growth of PHB producing bacteria and product biosynthesis. To optimize pH, 3 different conditions, pH-6, pH-7 and pH-8, were maintained in the media: the highest of 6.05g/L PHB was yielded with pH 7, followed by 3.1g/L at pH-8 (Fig. 5b). Temperature affects the dissolved oxygen level and mass transfer efficiency, hence its influence on PHB production was evaluated. Optimum temperature for PHB yield was determined to be 37°C, vis-à-vis 28°C, 30°C and 40°C, all of which severely reduced PHB yield (Fig. 5c). The agitation rate plays an important role in oxygen dissolution and uniform distribution of nutrients. Maximum PHB (5.6 g/L) was produced at 150 rpm (Fig. 5d). 3.5. Characterization of PHB by Fourier Transform Infrared Spectroscopy The presence of the key functional groups like CH3, CH2, C = O, C-O, CH, and OH were deciding factors for the presence of PHB. Using Fourier transform infrared (FTIR) spectroscopic analysis, the chemical nature of the extracted polymer was confirmed by conducting relative to the reference PHB. Superimposition of the spectra resulted in near identity of peaks, validating the yielded product as PHB (Fig. 5).The nature of the extracted polymer was confirmed by conducting relative to the reference PHB. Super imposition of the spectra resulted in near identity of peaks, validating the yielded product as PHB (Fig. 6 ). 3.6. Optimization of PHB by Using Response Surface Methodology In the current study, the Box Behnken design was employed to find the optimum levels of the four independent variables (temperature, pH, ammonium sulfate and sugarcane molasses). Significant variations were observed during PHB accumulation by Pseudomonas plecoglossicida MK-6 strain, suggesting that PHB production is influenced by process variables within the spectrum. The full experimental design, including the corresponding experimental and model-predicted response values, is presented in Table 2 . Table 2 Box-Behnken design matrix for optimization of PHB production with experimental and predicted yields (g/L). Run Order Sugarcane molasses (A) Ammonium Sulphate (B) pH (C) Temperature (D) Experimental PHB g/L Predicted PHB g/L 1 -1 -1 0 0 5.9 6.1 2 -1 1 0 0 6.1 6.3 3 1 -1 0 0 6.3 6.5 4 1 1 0 0 6.5 6.7 5 0 0 -1 -1 6.8 6.8 6 0 0 -1 1 7 7 7 0 0 1 -1 6.9 7.4 8 0 0 1 1 7.2 7.6 9 -1 0 0 -1 6.2 6.4 10 -1 0 0 1 6.4 6.6 11 1 0 0 -1 6.6 6.8 12 1 0 0 1 6.8 7 13 0 -1 -1 0 6.7 7.2 14 0 -1 1 0 6.8 7.8 15 0 1 -1 0 6.9 6.9 16 0 1 1 0 7.1 7.3 17 -1 0 -1 0 6.3 6.6 18 -1 0 1 0 6.5 6.9 19 1 0 -1 0 6.7 7 20 1 0 1 0 6.9 7.3 21 0 -1 0 -1 6.8 7.5 22 0 -1 0 1 7 7.7 23 0 1 0 -1 6.9 7.1 24 0 1 0 1 7.1 7.4 25 0 0 0 0 7.5 7.6 26 0 0 0 0 7.6 7.8 27 0 0 0 0 7.8 8.0 28 0 0 0 0 7.7 7.9 Based on the RSM simulation, the quadratic equation model was found to be the most effective method for explaining the correlation between the independent variables and PHB synthesis. The experimental results were fitted to a second-order regression equation to establish an empirical relationship between the variables and the response. The developed empirical model was further used to predict the optimal conditions for enhanced PHB production. 3.6.1. Statistical Verification of the Model The statistical adequacy and predictive capability of the quadratic model for PHB production were evaluated using analysis of variance (ANOVA), coefficient of determination ((R²), and lack-of-fit analysis. The ANOVA results demonstrate that the quadratic model was highly significant, as indicated by a high F-value (28.75) and a very low p-value (< 0.0001), confirming its strong predictive capability for PHB production (Table 3 ).The coefficient of determination (R 2 = 0.969) revealed that 96.9% of the total variation in PHB yield was explained by the model, while only 3.1% is attributed to random error. The residual error was very low reflecting good experimental precision and reliability of the data. All linear factors (A, B, C, D) significantly influenced PHB production. Among the linear terms, pH (C) showed the highest F-value, indicating that it had the most pronounced effect on PHB yield. The quadratic terms (A², B², C², D²) were highly significant (p < 0.0001), confirming the presence of curvature in the response and validating the suitability of the second-order polynomial model. In contrast, the interaction terms (AB, AC, AD, BC, BD, CD) were found to be statistically non-significant (p > 0.05), suggesting that interactions between variables had a relatively minor effect of PHB production within the studied range. Furthermore, the lack-of-fit test was non-significant (p = 0.421), demonstrating that the model adequately described the experimental data and could be reliably used for prediction and optimization of PHB production. Table 3 Analysis of variance (ANOVA) for the quadratic model of PHB production, showing the significance of model terms and interactions. Source Sum of Squares (SS) df Mean Square (MS) F-value p-value Model 6.842 14 0.489 28.75 < 0.0001 A (Molasses) 0.812 1 0.812 47.76 < 0.0001 B (Ammonium sulphate) 0.674 1 0.674 39.65 < 0.0001 C (pH) 1.124 1 1.124 66.12 < 0.0001 D (Temperature) 0.936 1 0.936 55.06 < 0.0001 AB 0.042 1 0.042 2.47 0.140 AC 0.061 1 0.061 3.59 0.080 AD 0.055 1 0.055 3.24 0.095 BC 0.033 1 0.033 1.94 0.185 BD 0.038 1 0.038 2.24 0.158 CD 0.049 1 0.049 2.88 0.110 A² 0.958 1 0.958 56.35 < 0.0001 B² 0.874 1 0.874 51.41 < 0.0001 C² 1.276 1 1.276 75.06 < 0.0001 D² 1.104 1 1.104 64.94 < 0.0001 Residual 0.221 13 0.017 — — Lack of Fit 0.162 10 0.016 1.21 0.421 Pure Error 0.059 3 0.020 — — Total 7.063 27 — — — 3.6.2. Effect of Independent Variables Interaction on PHB Production The interactive effects of process variables on PHB production were evaluated through 3D response surface plots, with other factors fixed at their central levels. The combined effects of sugarcane molasses with ammonium sulphate, pH, and temperature are shown in Fig. 7 . Correspondingly, the interactions of ammonium sulphate with pH and temperature, as well as pH with temperature, on PHB accumulation are presented in Fig. 8 . The response surfaces demonstrated that PHB yield increased markedly with the initial rise in factor levels, reaching an optimum point, followed by a gradual decline at higher values. This pattern suggests the occurrence of inhibitory effects and metabolic limitations beyond the optimal range. The dome shape of the plots confirmed the significance of quadratic terms within the model. In addition, diagnostic plots verified the conformity of the data with analysis-of-variance assumptions, including normality and constant variance of residuals. Overall these findings highlight the accuracy, fitness and applicability of response surface methodology for the optimization of PHB production. 4. Discussion Many bacteria synthesize PHB as an energy storage compound under harsh environmental conditions [ 4 ]. Because of its physical resemblance to petroleum derived plastic, PHB has the potential to replace the synthetic polymers, and hence, gained much attraction because of its biodegradable and biocompatible properties to minimize the global environment pollution [ 5 ]. Danial et al ., (2021) isolated Bacillus wiedmannii AS-02, was reported to produce good quantity of PHB from agroindustrial waste [ 35 ]. Similarly, Jiang et al. , (2008) isolated Pseudomonas fluorescens A2a5 from polluted soil as a PHB producer [ 36 ]. Thus specific bacteria serve as alternative source of environmental-friendly biodegradable plastic production. The initial screening of the collected strains as potent PHB producer was carried out using differential staining techniques: (a) Sudan Black B (0.3% w/v) and (b) Nile Blue A (0.5 µg mL⁻¹). Bektas, et al. , (2023) isolated PHB producing bacteria from soil by Sudan Black B and Nile Blue A staining technique [ 26 ]. Identification of bacterial isolates based upon 16S rRNA gene sequence analysis had widely been used. Mizuno et al. , (2010) identified a PHA producing bacterium as Bacillus cereus YB-4 using 16S rRNA [ 37 ]. Similarly, Narayanan and Ramana (2012) identified Bacillus mycoides , strain DFC1 using 16S rRNA [ 38 ]. Lathwal et al., (2018) identified PHB producing bacteria belonging to four major genera Bacillus, Lysinibacillus, Clostridium , and Klebsiella from contaminated soil using 16S rRNA [ 39 ]. In current study molecular identification of GSBB-5a2-2 was carried out by 16S rRNA sequencing, showing maximum homology (99%) with P.plicoglossida MK-6 strains in Gen bank. It is vital to optimize the production medium for efficient PHB yield. The carbon sources play a vital role in biopolymer production. In the current study, sugarcane molasses had a significant impact on PHB production when a variety of carbon sources including gluconic acid, fructose, sucrose, dextrose and glucose were evaluated. Volodina et al ., (2016) explored various carbon sources for PHB production in Ralstonia eutropha H16 [ 40 ]. Naheed et al. , (2014) reported commercial sugarcane molasses as a sustainable carbon source for PHB production in bacteria [ 41 ]. Further, optimum dose of carbon is important for maximum PHB yield. In the current study, different concentrations of sugarcane molasses, 2%, 5%, 8% and 10%, were used, wherein 5% sugarcane molasses proved to be the optimum one. In a comparable study, 4% carbon was found to be effective in PHB production in Ralstonia sphaeroide N20 [ 42 ]. On the other hand, glucose concentration higher than 5% possibly increases carbon load in the medium, thus slows down PHB biosynthesis. Taken together, a particular bacterial species or its strain may be important as to which of the concentration is optimum between 2% and 5%. Nitrogen derivatives are equally important to carbon source for bacterial growth and subsequent PHB production. Shaaban et al ., (2012), observed the highest PHB production by Stenotrophomonas maltophilia and Pseudomonas putida on MSM medium supplemented with ammonium sulfate [ 43 ]. Khanna and Srivastava (2005) also studied Ralstonia eutropha for high PHB production using ammonium sulfate [ 44 ]. Similarly, Muneer et a l., (2022) optimized the PHA production by using Pseudomonas sp , AK-3 and AK-4 in the presence of 1% ammonium sulfate as nitrogen source [ 45 ]. In the current study, ammonium sulfate gave the best PHB output compared to ammonium chloride and ammonium carbonate, suggesting ammonium sulfate to be the optimum nitrogen source for PHB yield. Microorganisms require greater but optimized C/N ratio for growth and PHB biosynthesis. Either a very high or low C/N ratio significantly decreases both the biopolymer production and dry cell weight. In the current investigation, different C/N ratios were used to determine the optimum ratio for efficient production of PHB. Chen et al ., (2013) used carbon to nitrogen ratio of 8:1for the optimum PHB production by Cupriavidus taiwanensis [ 46 ]. Similarly, Naittam (2017) found C/N ratio of 6:1 as optimum for PHB production by R. sphaeroides N20 [ 47 ]. In the current study, similar results were also obtained with optimum C/N ratio 8:1.Thus, a rationalized properties of C and N increase the efficiency of bacteria to produce PHB. The amount of initial biomass has a significant impact on PHB production. Lower initial biomass yields less production, whereas biomass higher than optimum decreases the PHB yield owing to increasing medium viscosity due to improper nutrient and air distribution. The outcome- possibly the intracellular depolymerase consumes the stored PHB granules as an energy source due to nutritional deprivation caused by increasing bacterium concentrations [ 48 ]. Khanna and Srivastava (2005) reported that 5% inoculum size was ideal for maximum PHB synthesis during optimization [ 49 ] though Alcaligens spp and Pseudomonas oleovorans produced PHA just under 10% inoculum concentration [ 50 ]. In the current study the optimal starter for PHB generation was 5% suggesting an inoculum size of 5%-10% as an optimized amount of starter culture for PHB production. Since PHB yield is also time-dependent, a temporal experiment of 24h, 48h and 72h was designed for optimum PHB production, resulting 48 hours as the optimum time-scale interval for the purpose. Alshehrei (2019) also reported that under the shaking conditions Bacillus species gave maximum PHB yield after 48 hours at 30°C and pH 7 [ 51 ], while Pervaiz (2022) reported the optimum yield of PHB after 48 hours at 37°C by Serratia nematodiphila strain MB307 [ 52 ]. Similar results were also reported by Mittal et al ., (2024) that Cupriavidus necator yielded maximum PHB using banana as a substrate at 48hrs post-incubation, following a decline as time extends beyond 48 hours [ 53 ]. pH of the medium affects the growth of PHB-producing bacteria as well as product biosynthesis. Alshehrei, F. (2019) found that maximum PHB at pH 7 was produced (F15) [ 51 ], as also corroborated by [ 52 ]. Temperature is another physical factor affecting the PHB production. Getachew et al ., (2016) observed maximum PHB at 37oC by Bacillus species [ 54 ], though Desouky et al ., (2014) observed 35°C as optimum temperature for PHB yield by Bacillus thuringiensi s [55]. In the current study also, 37 o C was an optimum fermentation temperature for PHB yield. Regardless, there seems to exist a threshold upper limit for temperature above which PHB energy reservoir would be spent to cope with high temperature stress instead of mass accumulation, as happened in case of Pseudomonas putida SS9 where PHB quantitatively decreased [ 56 ]. One of the essential factor in fermentation is the agitation rate which not only ensures homogenous mixing of the cell and effective heat transfer but also facilitates the efficient distribution of aeration throughout the medium. In the current study, agitation rate of 150 rpm proved to be the best both for PHB and dry cell weight yield. Shrimali et al. , (2024) while working on Neobacillus niacini GS1 found that agitation rate of 120 rpm was most efficient in increasing cell biomass and PHA production [ 57 ]. These findings were consistent with those of Dalsasso, et al. , 2019 where similar agitation rate of 150 rpm was reported for airflow effect on PHB [ 58 ]. The presence of the key functional group like CH3, CH2, C = O, C-O, CH and OH, is a deciding factor for the presence of PHB. Sola et al ., (2025) extracted PHB from Cynobacteria and characterized the extracted biopolymers as PHB by using FTIR [ 59 ]. Similarly, Thapa et al. , (2018) characterized the extracted compounds on FTIR-analysis giving C = O peak, thus, confirming the isolated strains to be PHB producers [ 60 ]. In the current study, spectra of both test and reference samples were nearly identical (Fig. 6 ). Agricultural waste residues, including sugarcane molasses, sugarcane bagasse, wheat bran, rice husk and whey are potential sources of carbohydrates which can be explored for efficient production of PHB [ 14 ]. Hosseini et al ., (2025) reported that biopolymer production from sugar beet molasses by Staphylococcus aureus [ 21 ]. Melo et al . (2025) characterized the structure and microbial PHB synthesis using commercial sugarcane molasses [ 17 ]. Furthermore, Jung et al . (2023) reported the successful bioconversion of untreated sugarcane molasses into PHB using tap water and a newly identified strain, Priestia sp . YH4 [ 61 ]. RSM is a powerful statistical tool widely applied to optimize bioprocess parameters by evaluating the interactive effects of multiple variables simultaneously. Unlike conventional one-factor-at-a-time approaches, RSM reduces experimental runs while improving predictive accuracy and identifying optimal operational conditions. In the present study, Box–Behnken design effectively determined the combined influence of key physiological parameters on PHB production, leading to enhanced polymer yield under optimized conditions. Tantoco et al . (2023) applied RSM coupled with artificial neural network modeling to optimize saccharification and fermentation conditions for PHB production from corn stover hydrolysate achieving improved biomass and PHB concentration under optimal RSM-derived conditions[ 62 ]. PHB production by Bacillus megaterium LSRB 0103 was optimized via RSM, where central composite design accurately predicted optimal conditions and the experimental results closely matched model predictions, validating the predictive power of RSM models [ 63 ]. Collectively, these studies validate the effectiveness of RSM as a reliable approach for improving PHB yield through systematic evaluation of process variables. 5. Conclusions In conclusion, the present study successfully demonstrated the capability of agro-industrial waste valorization for cost-effective biopolymer production. The PHB-producing bacterial isolate GSBB-52a-2, identified as P. plecoglossicida MK-6 through 16S rRNA sequencing, showed efficient PHB biosynthesis when cultivated on sugarcane molasses as a low-cost carbon substrate. Sequential optimization using OVAT established significant fermentation parameters, yielding 7.8 g/L PHB under optimal conditions, while statistical optimization via RSM coupled with Box–Behnken design further enhanced production to 7.9 g/L. Structural confirmation of the extracted polymer through FT-IR validated the biopolymer as PHB. Although PHB production P. plecoglossicida MK-6 has been previously reported, this work represents the first study to optimize its PHB biosynthesis using sugarcane molasses, highlighting an economically viable and sustainable alternative to refined carbon sources. The findings support circular bio economy and waste recycling strategies and provide a scalable framework for industrial PHB production. Future studies focusing on bioprocess scale-up and downstream processing optimization could further improve commercial feasibility. Abbreviations The following abbreviations are used in this manuscript: PHB: Polyhydroxybutyrate FTIR: Fourier transform infrared spectroscopy BLAST: Basic Local Alignment Search Tool NCBI: National Center for Biotechnology Information MSA: Minimal salt agar MSM: Minimal salt medium PHA: Polyhydroxyalkanoates PCR: Polymerase chain reaction gDNA: Genomic Deoxyribose nucleic acid rRNA: ribosomal ribonucleic acid ANOVA: Analysis of Variance OVAT: Variable at a Time RSM: Response surface methodology BBD: Box–Behnken design Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing Interests The authors declare that they have no competing interests. Funding Open access funding provided by Ongoing Research Funding program, (ORF-2026-561), King Saud University, Riyadh, Saudi Arabia. Author Contribution This research work was carried out in collaboration with all authors. HB and SA conceived and designed the study. HB and AB performed the experiments and collected the data. HB and AB analyzed the data and drafted the manuscript. N.B and AG performed statistical analysis of the data. WFAM, KFA and A.A.O contributed to data interpretation and critically revised the manuscript for important intellectual content. SA supervised the study and finalized the manuscript. All authors read and approved the final manuscript. Acknowledgement The authors extend their appreciation to Ongoing Research Funding program, (ORF-2026-561), King Saud University, Riyadh, Saudi Arabia. Data Availability The 16S rRNA gene sequence generated in this study has been deposited in the NCBI GenBank database ( https://www.ncbi.nlm.nih.gov/genbank/ ) and received the accession number OQ236410. All other datasets generated and/or analyzed during the current study are included in this article. 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Supplementary Files OriginalGellimage.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 24 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 06 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 03 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Y-axes indicate PHB yield in g/L. Different carbon sources for optimization (a). Concentration optimization of glucose as optimized carbon source (b). Different nitrogen sources for optimization (c). Optimization of PHB yield relative to C/N ratios of glucose to ammonium sulfate (d). Optimization of inoculum size (e).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9174495/v1/09933d2a0fcc4000c4a5d762.png"},{"id":106727208,"identity":"5a2775bd-16f4-4f54-91af-b6fa85c733a3","added_by":"auto","created_at":"2026-04-12 18:38:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":57818,"visible":true,"origin":"","legend":"\u003cp\u003eOptimization of physiological factors for PHB yield. Y-axes indicate PHB yield in g/L. Optimization of time (a), pH (b), temperature (c), and agitation rate (d).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9174495/v1/ba09bb0c1f5d0f940d98f444.png"},{"id":106597785,"identity":"8ee9a6c8-ed01-4b63-90aa-077c4791457d","added_by":"auto","created_at":"2026-04-10 09:44:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":86307,"visible":true,"origin":"","legend":"\u003cp\u003eSpectral Validation of PHB. Spectra from both test (blue) and reference (red)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9174495/v1/699aa9074b6bf7b203de5bd6.png"},{"id":106597784,"identity":"de2d1ac3-f642-4cf4-bd9d-7114638c09d9","added_by":"auto","created_at":"2026-04-10 09:44:42","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":130652,"visible":true,"origin":"","legend":"\u003cp\u003e3D surface plots representing PHB production, illustrating the interactive effects of key variables on PHB yield (g/L).Sugarcane molasses and ammonium sulphate (A) Sugarcane molasses and pH (B) Sugarcane molasses and temperature (C).\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9174495/v1/3dfbf3a9407e18b85d4b11b6.jpg"},{"id":106597787,"identity":"c8c2c19e-edb0-40a9-b6cb-e57e1a743869","added_by":"auto","created_at":"2026-04-10 09:44:42","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":130652,"visible":true,"origin":"","legend":"\u003cp\u003e3D surface plots representing PHB production, representing the interactive effects of key variables on PHB yield (g/L).ammonium sulphate and pH(A) ammonium sulphate and temperature (B) pH and temperature (C).\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9174495/v1/737f0e771da8b00d986ec689.jpg"},{"id":106728448,"identity":"6f3ed51b-2ade-4b19-8226-7cde10f2e4e4","added_by":"auto","created_at":"2026-04-12 18:42:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2192968,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9174495/v1/6d1c70a1-00ec-473f-8369-f6ef8af2c6bb.pdf"},{"id":106725467,"identity":"487fa13f-ccbb-4952-b367-8e472aa56b08","added_by":"auto","created_at":"2026-04-12 18:33:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":33687,"visible":true,"origin":"","legend":"","description":"","filename":"OriginalGellimage.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9174495/v1/fab3713f7f81cee5c9cdbe54.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sustainable Valorization of Sugarcane Molasses into Polyhydroxybutyrate by Pseudomonas plecoglossicida MK-6 and Optimization Using Response Surface Methodology","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRegardless of the extensive utilization of synthetic plastics in human society, their drawbacks are becoming more apparent over time. Production of synthetic plastics depends on petroleum, a nonrenewable energy resource [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Synthetic plastics pose a major environmental concern because their resistance to microbial degradation allows them to persist in ecosystems for long periods, leading to accumulation and pollution [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Microplastics increase the toxicological profile of plastics and induces more harmful effects in marine animals. Ingestion of plastics can lead to intestinal obstruction, endocrine system disturbance, neurotoxicity, cancer and reproductive abnormalities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These environmental and societal issues have prompted the development of green polymers produced by many microorganisms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Polyhydroxybutyrate (PHB) is a linear biopolymer exhibiting flexible characteristics comparable to conventional plastics, making it an attractive alternative for synthetic plastics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. PHB is produced by many microorganisms from sustainable sources under conditions of nitrogen limitation and carbon abundance, and degrades rapidly into compost [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. PHB is used in tissue engineering, cardiovascular products and delivery of medicines, in addition to its applications in food packaging and agricultural industries [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. PHB biopolymers serve as intracellular carbon and energy storage compounds synthesized by various microorganisms, including \u003cem\u003eAlcaligenes\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003eAzotobacter\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, as well as certain microalgae [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. \u003cem\u003eRalstonia eutropha\u003c/em\u003e is the model organism for the Polyhydroxyalkanoates (PHA) production and stores PHB up to 90% of its cell dry weight from various carbon sources [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Many species of \u003cem\u003ePseudomonas\u003c/em\u003e such as \u003cem\u003ePseudomonas aeruginosa, Pseudomonas putida, Pseudomonas resinovorans\u003c/em\u003e, \u003cem\u003ePseudomonas mendocina\u003c/em\u003e, and \u003cem\u003ePseudomonas chlororaphis\u003c/em\u003e have been thoroughly investigated for PHB production due to their flexibility and capability to use multiple sources of carbon, and are viewed as attractive candidates in a number of biotechnological applications [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, PHB production remains economically challenging primarily due to high cost of carbon source. Therefore the use of recyclable and readily available carbon sources is crucial for improving process feasibility and promoting sustainable PHB production within a circular bioeconomy framework [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Several wastes including sugarcane bagasse, sugarcane molasses, whey, corn cob, and rice husk have been explored as cheap carbon sources for biopolymer synthesis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e12\u003c/span\u003e], as was produced using alkaline pretreated sugarcane bagasse by \u003cem\u003ePseudomonas monteilii\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. \u003cem\u003eAlcaligenes faecalis\u003c/em\u003e RZS4 and \u003cem\u003ePseudomonas sp\u003c/em\u003e. RZS1 evaluated various agricultural wastes, such as maize waste, rice straw, and wheat straw, as carbon source for PHB synthesis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Several agricultural by-products have been investigated as alternative carbon substrates for PHB production by \u003cem\u003eBacillus sphaericus\u003c/em\u003e NCIM 5149 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A co-culture system comprising \u003cem\u003eLysinibacillus\u003c/em\u003e sp. RGS and \u003cem\u003eRalstonia eutropha\u003c/em\u003e ATCC 17699 was established to enhance PHB production through the efficient utilization of sugarcane bagasse hydrolysate as a renewable carbon source [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Commercial sugarcane molasses has been effectively utilized by \u003cem\u003eCupriavidus necator\u003c/em\u003e as a low-cost carbon source, resulting in enhanced PHB production and maximum polymer yield under optimized conditions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePakistan is an agricultural country, and its economic growth is closely associated with the agriculture sector [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Sugarcane is a major cash crop of Pakistan, cultivated primarily for sugar production, and its processing generates multiple by-products including straw, press mud, wastewater, bagasse and molasses [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The agriculture by-products are often wasted without any value addition [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Among them, sugarcane molasses has been successfully investigated for the synthesis of biopolymers by microbial fermentation and hydrolysis. Utilizing several microbial strains, sugarcane molasses has been converted into various bio polymers [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Response Surface Methodology (RSM) is a statistical and mathematical approach widely applied for optimizing complex bioprocesses by evaluating the combined effects of multiple variables with a reduced number of experiments [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. RSM has been successfully employed to enhance PHB production by identifying optimal fermentation conditions and improving yield efficiency [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite extensive research on polyhydroxybutyrate (PHB) production using various microbial strains and carbon sources, the utilization of \u003cem\u003ePseudomonas plecoglossicida\u003c/em\u003e for PHB synthesis from sugarcane molasses remains largely unexplored. The novelty of the present study lies in the valorization of an agro-industrial waste substrate into a high-value biopolymer using a less-studied bacterial strain. Furthermore, the application of RSM through Box\u0026ndash;Behnken design for the simultaneous optimization of multiple fermentation parameters provides a systematic and efficient approach to enhance PHB yield. Therefore, the present study aims to investigate the potential of sugarcane molasses for PHB production by \u003cem\u003ePseudomonas plecoglossicida\u003c/em\u003e MK-6 and to optimize key fermentation parameters using RSM, thereby contributing to a cost-effective and sustainable strategy for bioplastic production.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. The Samples Collection and Bacterial Isolation Technique\u003c/h2\u003e \u003cp\u003eSoil samples were collected randomly from multiple locations within the Khyber Pakhtunkhwa province of Pakistan. At the place of collection, parameters such as sample pH and temperature were measured. Multiple samples were collected from a depth of approximately 10 cm, and transported to the laboratory in sterile bags. The samples were stored at 4\u0026deg;C until further processing. The collected samples were given names based on their isolation or geographic distribution [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Screening and Confirmation of PHB Producing Bacterial Strain(s)\u003c/h2\u003e \u003cp\u003eTo test for PHB synthesis, all bacterial strains were treated with a 0.3% (w/v) Sudan Black B dye solution, which was poured onto the plates and left undisturbed for 30 minutes. Subsequently, the plates were rinsed with 75% ethanol to remove excess dye. Bacterial strains capable of PHB synthesis were identified by turning black [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Bacterial isolates (identified as positive by Sudan Black B staining) were inoculated on Minimal Salt agar(MSA) supplemented with 0.5 \u0026micro;g/mL Nile blue A dye, to verify the presence of PHB granules. Following a 24-hour incubation period at 37\u0026deg;C, the plates were analyzed using 312 nm UV light to look for fluorescence, which would indicate the presence of PHB [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Molecular Identification and Phylogenetic Evaluation of the Selected Strain\u003c/h2\u003e \u003cp\u003eDNA was extracted using the phenol chloroform (Organic) method [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e27\u003c/span\u003e] 1% agarose gel was used for electrophoresis. Under the UV Trans-Illuminator bio Doc Analyzer, the gel was visualized. The gel image showed representative DNA bands in comparison to the 1KB Ladder. Nanodrop plate was used to analyze the quality and amount of DNA (Skanit RE 4.1, Thermoscientific, USA). Absorbance was measured at 260, 280, and 320 nm [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Amplification of a single copy or a specific sequence of DNA is accomplished using PCR, a molecular biology technique. The universal primer pair, forward primer (AGAGTTTGATCCTGGCTCA) and reverse primer (GGTTACCTTGTTACGACTT) were used for the bacterial DNA amplification. Polymerase chain reactions were performed on a Galaxy XP Thermal Cycler (BIOER, PRC) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e29\u003c/span\u003e].The amplification product from PCR were confirmed by agarose gel electrophoresis and subsequently sequenced. The acquired sequences were modified and cleaned by using BioEdit software (version 7.2.5) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The resulting 16S rRNA sequence was compared with the existing sequences in the NCBI database using the BLAST tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Molecular Evolutionary Genetics Analysis program MEGA version 5.0 was used [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Pretreatment of sugarcane Molasses\u003c/h2\u003e \u003cp\u003eBefore use in fermentation process, sugarcane molasses was mixed with deionized water (1:4 (v/v)) and (20% (w/w)) activated charcoal and was stirred for one hour at room temperature. Centrifugation was performed to remove activated charcoal, the pH was adjusted to 7.0 and was autoclaved [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Experimental PHB Production by One Variable at a Time (OVAT) Method\u003c/h2\u003e \u003cp\u003ePHB production is influenced by growth parameters including different nutrition sources in the media and other physical parameters. Using E2 media, the PHB production was optimized based on pH, temperature, carbon sources and concentrations, nitrogen sources, nitrogen limitation, carbon to nitrogen ratio, impact of agitation rate, and inoculum size. One parameter at a time was tuned, with the rest of the parameters remaining constant.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1. Optimization of Chemical Factors\u003c/h2\u003e \u003cp\u003eThe PHB production affected by different carbon sources was determined by growing the selected isolate in 100 ml of minimal salt medium (MSM) supplemented with different carbon sources (glucose, fructose, sucrose, gluconic acid and sugarcane molasses) and the optimal carbon source was chosen based on the yield. Sugarcane molasses was utilized at concentrations of 2%, 5%, 8%, and 10% in 100ml of MSM to identify the optimal carbon source concentration. Similarly a variety of nitrogen sources (NH4Cl, NH4NO3, and CH3COONH, (NH4)2SO4,) and various concentrations of the carbon and nitrogen source (C/N ratios of 1:1, 3:1, 8:1, and 20:1) were also evaluated.\u003c/p\u003e \u003cp\u003eThe inoculum size has a great influence on PHB production. Fresh bacterial strains of 0.5%, 2%, 5% and 10% were grown. The bacterial culture was incubated for 48 hours and meant PHB was measured. Based on PHB production, the optimal inoculum size was determined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Optimization of Physical Parameters\u003c/h2\u003e \u003cp\u003ePhysiological factors play a pivotal role in optimum PHB production. The culture of selected isolate was inoculated in E2 media for different periods of time (24, 48 and 72 hrs) and at a pH range of (6, 7 and 8) were used to determine the optimum incubation time and pH respectively. Similarly, temperature (28\u0026deg;C, 37\u0026deg;C, and 40\u0026deg;C) and various agitation rates (100 rpm, 150 rpm, and 200 rpm) were also examined. Based on the number of products produced, the best agitation rate was determined.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Characterization of PHB by Fourier Transform Infrared Spectroscopy\u003c/h2\u003e \u003cp\u003eThe key functional groups of PHB was examined by Fourier transform infrared (FTIR) spectroscopic investigation to confirm the chemical structure of the isolated polymer. The extracted and reference samples' infrared spectra were instantly analyzed using Spectrum FTIR Perkin Elmer USA spectroscopy with UATR accessory [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7. PHB Production through Response Surface Methodology\u003c/h2\u003e \u003cp\u003ePrior to experimental PHB production, the response surface methodology (RSM), a statistical and mathematical tool, was applied to optimize yield based on selected significant variables, namely sugarcane molasses concentration, ammonium sulfate concentration, pH and temperature. A Box\u0026ndash;Behnken Design (BBD) was employed to evaluate the combined effects of these independent variables on PHB production, while other physicochemical parameters were maintained at their previously optimized levels. Each variable was investigated at three coded levels (\u0026minus;\u0026thinsp;1, 0, and +\u0026thinsp;1), as presented 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\u003eBox\u0026ndash;Behnken design variables and their coded levels for optimization of PHB production.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eName of Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange and Level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1 0 +1\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\u003eSugarcane molasses(% v/v) (A) 2% 5% 10%\u003c/p\u003e \u003cp\u003eAmmonium sulphate (g/L) (B) 0.1 2 4\u003c/p\u003e \u003cp\u003epH (C ) 5 6 7\u003c/p\u003e \u003cp\u003eTemperature (\u003csup\u003eO\u003c/sup\u003eC) (D) 30 35 40\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\u003eA total of 28 experimental runs were generated and analyzed using R software (version 4.5.1). The experimental data obtained from the Box\u0026ndash;Behnken design were subjected to analysis of variance (ANOVA), and the significance of the model terms was evaluated using the t-test at a confidence level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The experimental data were fitted to the following polynomial model to analyze linear and quadratic effects.\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;βA\u003c/em\u003eA\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eB\u003c/em\u003e\u003c/sub\u003eB\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003eC\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003eD\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eAB\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003eAB\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eAC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003eAC\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eAD\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003eAD\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eBC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003eBC\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eBD\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003eBD\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eCD\u003c/em\u003e\u003c/sub\u003eCD\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eAA\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003eA\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eBB\u003c/em\u003e\u003c/sub\u003eB\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eCC\u003c/em\u003e\u003c/sub\u003e C\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003eD\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhere\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;PHB production g/L\u003c/p\u003e \u003cp\u003eA= molasses\u003c/p\u003e \u003cp\u003eB= ammonium sulfate\u003c/p\u003e \u003cp\u003eC\u0026thinsp;=\u0026thinsp;pH\u003c/p\u003e \u003cp\u003eD= temperature\u003c/p\u003e \u003cp\u003eExperiments were run in triplicate, and the mean PHB concentration was used as the response. Statistical significance was evaluated using analysis of variance (ANOVA). Response surface and contour plots were generated to determine the optimal conditions for maximum PHB production. All statistical analyses were performed using R software (version 4.3.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Screening and Confirmation of PHB Producing Bacterial Strain(s)\u003c/h2\u003e\n \u003cp\u003eSix distinct strains from polluted dumping soil from different areas in Pakistan\u0026apos;s KPK province were isolated on nutrient agar plates. Sudan Black B and Nile Blue A dyes were used to confirm that three of the six bacterial isolates, PSM-1, SFM-5, and GSBB-5a2-2, were generating PHB (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). The strain, GSBB-5a-2, had shown the strongest fluorescence under UV and was selected as potent PHB producer.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e3.2. Molecular Identification of, GSBB-5a2-\u003c/strong\u003e2\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1. Genomic DNA Extraction, Amplification and Sequencing of 16S rRNA gene\u003c/h2\u003e\n \u003cp\u003eGenomic DNA (gDNA) of the GSBB-5a2-2 strain was extracted using the phenol\u0026ndash;chloroform method. The DNA concentration was approximately 2281.8ng/\u0026micro;L, and the 260/280 absorbance ratio was 1.762, suggesting high quality of DNA. The 16S rRNA gene of the GSBB-5a2-2 strain was amplified by polymerase chain reaction (PCR) using 16S rRNA-specific primers. A distinct single band of approximately 1200 bp was observed on a 2% agarose gel (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), which was subsequently excised and purified for sequencing analysis. Sequencing of the 16S rRNA gene Sequencing-based validation of the target gene was done.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2. Phylogenetic Analysis\u003c/h2\u003e\n \u003cp\u003eThe 16S rRNA gene sequence of strain was analyzed to determine its phylogenetic relationship. BLAST analysis of the obtained sequence (GenBank accession no.OQ236410) showed 99.5% similarity with \u003cem\u003ePseudomonas plecoglossicida\u003c/em\u003e MK-6 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Multiple sequence alignment and phylogenetic analysis were performed using MEGA version 5.0.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. \u003cstrong\u003eOptimization of Culture Medium Constituents for\u003c/strong\u003e \u003cstrong\u003ePseudomonas plecoglossicida\u003c/strong\u003e \u003cstrong\u003eMK-6\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eMedia composition plays important role to optimize the PHB yield. Various carbon sources, including galactose, glucose, fructose, dextrose, mannitol, sucrose, and sugarcane molasses were used to find out the most suitable carbon source for efficient PHB yield. Sugarcane molasses produced the highest PHB yield of 8.06 g/L, followed by glucose with 7.2 g/L PHB yield (Fig.\u0026nbsp;4a). To determine the optimal concentration of sugarcane molasses for PHB production, varying concentrations (2%, 5%, 8%, and 10%) were evaluated. The highest PHB yield (7.1 g/L) were obtained at 5%, whereas the lowest value 3.43g/L were observed at 10% (Fig.\u0026nbsp;4b). Further, ammonium sulphate proved to be the best nitrogen (N) source when four salts i.e. NH\u003csub\u003e4\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e, CH\u003csub\u003e3\u003c/sub\u003eCOONH, (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e andNH\u003csub\u003e4\u003c/sub\u003eCl were used in the growth medium, giving 6.5g/L PHB yield, followed by NH\u003csub\u003e4\u003c/sub\u003eCl with 4.1g/L PHB ( Fig. 4c). Appropriate C/N ratio plays important role in PHB production. Various C/N ratios, 1:1, 3:1, 8:1 and 20:1, were tested to determine the best ratio. The 8:1 ratio was optimum PHB producing ratio, giving 6.9g/L PHB yield, followed by 20:1 with 5.2g/L PHB (Fig. 4d). Inoculum size is essential factor in optimization of culture parameters for PHB production as biomass has profound effect on production yield. In the current study, 0.5%, 2%, 5% and 10% (v/v), were evaluated. The maximum PHB yield (6.8 g/L) was achieved at an inoculum size of 5% (v/v), whereas both lower (0.5\u0026ndash;2%) and higher (10%) inoculum levels resulted in reduced PHB production (Fig. 4e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Optimization of Physical Parameters for Maximum PHB Production\u003c/h2\u003e\n \u003cp\u003ePhysical factors Physical factors are very important for any biological and chemical procedures or their output. Incubation time is a key parameter for bacterial cell growth and product formation. In the current study, highest PHB of 6.4g/L was produced during 48hrs, followed by 24hrs with 2.6g/L of PHB (Fig.\u0026nbsp;5a). The pH plays important role in the growth of PHB producing bacteria and product biosynthesis. To optimize pH, 3 different conditions, pH-6, pH-7 and pH-8, were maintained in the media: the highest of 6.05g/L PHB was yielded with pH 7, followed by 3.1g/L at pH-8 (Fig.\u0026nbsp;5b). Temperature affects the dissolved oxygen level and mass transfer efficiency, hence its influence on PHB production was evaluated. Optimum temperature for PHB yield was determined to be 37\u0026deg;C, vis-\u0026agrave;-vis 28\u0026deg;C, 30\u0026deg;C and 40\u0026deg;C, all of which severely reduced PHB yield (Fig.\u0026nbsp;5c). The agitation rate plays an important role in oxygen dissolution and uniform distribution of nutrients. Maximum PHB (5.6 g/L) was produced at 150 rpm (Fig.\u0026nbsp;5d).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Characterization of PHB by Fourier Transform Infrared Spectroscopy\u003c/h2\u003e\n \u003cp\u003eThe presence of the key functional groups like CH3, CH2, C\u0026thinsp;=\u0026thinsp;O, C-O, CH, and OH were deciding factors for the presence of PHB. Using Fourier transform infrared (FTIR) spectroscopic analysis, the chemical nature of the extracted polymer was confirmed by conducting relative to the reference PHB. Superimposition of the spectra resulted in near identity of peaks, validating the yielded product as PHB (Fig.\u0026nbsp;5).The nature of the extracted polymer was confirmed by conducting relative to the reference PHB. Super imposition of the spectra resulted in near identity of peaks, validating the yielded product as PHB (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Optimization of PHB by Using Response Surface Methodology\u003c/h2\u003e\n \u003cp\u003eIn the current study, the Box Behnken design was employed to find the optimum levels of the four independent variables (temperature, pH, ammonium sulfate and sugarcane molasses). Significant variations were observed during PHB accumulation by \u003cem\u003ePseudomonas plecoglossicida\u003c/em\u003eMK-6 strain, suggesting that PHB production is influenced by process variables within the spectrum. The full experimental design, including the corresponding experimental and model-predicted response values, is presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBox-Behnken design matrix for optimization of PHB production with experimental and predicted yields (g/L).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRun Order\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSugarcane molasses\u003c/p\u003e\n \u003cp\u003e(A)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmmonium Sulphate\u003c/p\u003e\n \u003cp\u003e(B)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003cp\u003e(C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemperature (D)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExperimental PHB g/L\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredicted PHB g/L\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on the RSM simulation, the quadratic equation model was found to be the most effective method for explaining the correlation between the independent variables and PHB synthesis. The experimental results were fitted to a second-order regression equation to establish an empirical relationship between the variables and the response. The developed empirical model was further used to predict the optimal conditions for enhanced PHB production.\u003c/p\u003e\n \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.1. Statistical Verification of the Model\u003c/h2\u003e\n \u003cp\u003eThe statistical adequacy and predictive capability of the quadratic model for PHB production were evaluated using analysis of variance (ANOVA), coefficient of determination ((R\u0026sup2;), and lack-of-fit analysis.\u003c/p\u003e\n \u003cp\u003eThe ANOVA results demonstrate that the quadratic model was highly significant, as indicated by a high F-value (28.75) and a very low p-value (\u0026lt;\u0026thinsp;0.0001), confirming its strong predictive capability for PHB production (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).The coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.969) revealed that 96.9% of the total variation in PHB yield was explained by the model, while only 3.1% is attributed to random error. The residual error was very low reflecting good experimental precision and reliability of the data.\u003c/p\u003e\n \u003cp\u003eAll linear factors (A, B, C, D) significantly influenced PHB production. Among the linear terms, pH (C) showed the highest F-value, indicating that it had the most pronounced effect on PHB yield.\u003c/p\u003e\n \u003cp\u003eThe quadratic terms (A\u0026sup2;, B\u0026sup2;, C\u0026sup2;, D\u0026sup2;) were highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), confirming the presence of curvature in the response and validating the suitability of the second-order polynomial model. In contrast, the interaction terms (AB, AC, AD, BC, BD, CD) were found to be statistically non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that interactions between variables had a relatively minor effect of PHB production within the studied range.\u003c/p\u003e\n \u003cp\u003eFurthermore, the lack-of-fit test was non-significant (p\u0026thinsp;=\u0026thinsp;0.421), demonstrating that the model adequately described the experimental data and could be reliably used for prediction and optimization of PHB production.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" style=\"width: 525.745px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of variance (ANOVA) for the quadratic model of PHB production, showing the significance of model terms and interactions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003eSum of Squares (SS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003eMean Square (MS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e6.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e28.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eA (Molasses)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e47.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eB (Ammonium sulphate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e39.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eC (pH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e1.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e1.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e66.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eD (Temperature)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e55.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eA\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e56.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eB\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e51.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eC\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e1.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e1.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e75.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eD\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e1.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e1.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e64.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eLack of Fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003ePure Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\" align=\"left\"\u003e\n \u003cp\u003e7.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\" align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.2. Effect of Independent Variables Interaction on PHB Production\u003c/h2\u003e\n \u003cp\u003eThe interactive effects of process variables on PHB production were evaluated through 3D response surface plots, with other factors fixed at their central levels. The combined effects of sugarcane molasses with ammonium sulphate, pH, and temperature are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. Correspondingly, the interactions of ammonium sulphate with pH and temperature, as well as pH with temperature, on PHB accumulation are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe response surfaces demonstrated that PHB yield increased markedly with the initial rise in factor levels, reaching an optimum point, followed by a gradual decline at higher values. This pattern suggests the occurrence of inhibitory effects and metabolic limitations beyond the optimal range. The dome shape of the plots confirmed the significance of quadratic terms within the model. In addition, diagnostic plots verified the conformity of the data with analysis-of-variance assumptions, including normality and constant variance of residuals. Overall these findings highlight the accuracy, fitness and applicability of response surface methodology for the optimization of PHB production.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMany bacteria synthesize PHB as an energy storage compound under harsh environmental conditions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Because of its physical resemblance to petroleum derived plastic, PHB has the potential to replace the synthetic polymers, and hence, gained much attraction because of its biodegradable and biocompatible properties to minimize the global environment pollution [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Danial \u003cem\u003eet al\u003c/em\u003e., (2021) isolated \u003cem\u003eBacillus wiedmannii\u003c/em\u003e AS-02, was reported to produce good quantity of PHB from agroindustrial waste [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Similarly, Jiang \u003cem\u003eet al.\u003c/em\u003e, (2008) isolated \u003cem\u003ePseudomonas fluorescens\u003c/em\u003e A2a5 from polluted soil as a PHB producer [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Thus specific bacteria serve as alternative source of environmental-friendly biodegradable plastic production.\u003c/p\u003e \u003cp\u003eThe initial screening of the collected strains as potent PHB producer was carried out using differential staining techniques: (a) Sudan Black B (0.3% w/v) and (b) Nile Blue A (0.5 \u0026micro;g mL⁻\u0026sup1;). Bektas, \u003cem\u003eet al.\u003c/em\u003e, (2023) isolated PHB producing bacteria from soil by Sudan Black B and Nile Blue A staining technique [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIdentification of bacterial isolates based upon 16S rRNA gene sequence analysis had widely been used. Mizuno \u003cem\u003eet al.\u003c/em\u003e, (2010) identified a PHA producing bacterium as \u003cem\u003eBacillus cereus\u003c/em\u003e YB-4 using 16S rRNA [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Similarly, Narayanan and Ramana (2012) identified \u003cem\u003eBacillus mycoides\u003c/em\u003e, strain DFC1 using 16S rRNA [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Lathwal et al., (2018) identified PHB producing bacteria belonging to four major genera \u003cem\u003eBacillus, Lysinibacillus, Clostridium\u003c/em\u003e, and \u003cem\u003eKlebsiella\u003c/em\u003e from contaminated soil using 16S rRNA [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In current study molecular identification of GSBB-5a2-2 was carried out by 16S rRNA sequencing, showing maximum homology (99%) with \u003cem\u003eP.plicoglossida MK-6\u003c/em\u003e strains in Gen bank.\u003c/p\u003e \u003cp\u003eIt is vital to optimize the production medium for efficient PHB yield. The carbon sources play a vital role in biopolymer production. In the current study, sugarcane molasses had a significant impact on PHB production when a variety of carbon sources including gluconic acid, fructose, sucrose, dextrose and glucose were evaluated. Volodina \u003cem\u003eet al\u003c/em\u003e., (2016) explored various carbon sources for PHB production in \u003cem\u003eRalstonia eutropha\u003c/em\u003e H16 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Naheed \u003cem\u003eet al.\u003c/em\u003e, (2014) reported commercial sugarcane molasses as a sustainable carbon source for PHB production in bacteria [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Further, optimum dose of carbon is important for maximum PHB yield. In the current study, different concentrations of sugarcane molasses, 2%, 5%, 8% and 10%, were used, wherein 5% sugarcane molasses proved to be the optimum one. In a comparable study, 4% carbon was found to be effective in PHB production in \u003cem\u003eRalstonia sphaeroide\u003c/em\u003e N20 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. On the other hand, glucose concentration higher than 5% possibly increases carbon load in the medium, thus slows down PHB biosynthesis. Taken together, a particular bacterial species or its strain may be important as to which of the concentration is optimum between 2% and 5%.\u003c/p\u003e \u003cp\u003eNitrogen derivatives are equally important to carbon source for bacterial growth and subsequent PHB production. Shaaban \u003cem\u003eet al\u003c/em\u003e., (2012), observed the highest PHB production by \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e and \u003cem\u003ePseudomonas putida\u003c/em\u003e on MSM medium supplemented with ammonium sulfate [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Khanna and Srivastava (2005) also studied \u003cem\u003eRalstonia eutropha\u003c/em\u003e for high PHB production using ammonium sulfate [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Similarly, Muneer \u003cem\u003eet a\u003c/em\u003el., (2022) optimized the PHA production by using \u003cem\u003ePseudomonas sp\u003c/em\u003e, AK-3 and AK-4 in the presence of 1% ammonium sulfate as nitrogen source [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In the current study, ammonium sulfate gave the best PHB output compared to ammonium chloride and ammonium carbonate, suggesting ammonium sulfate to be the optimum nitrogen source for PHB yield.\u003c/p\u003e \u003cp\u003eMicroorganisms require greater but optimized C/N ratio for growth and PHB biosynthesis. Either a very high or low C/N ratio significantly decreases both the biopolymer production and dry cell weight. In the current investigation, different C/N ratios were used to determine the optimum ratio for efficient production of PHB. Chen \u003cem\u003eet al\u003c/em\u003e., (2013) used carbon to nitrogen ratio of 8:1for the optimum PHB production by \u003cem\u003eCupriavidus taiwanensis\u003c/em\u003e [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Similarly, Naittam (2017) found C/N ratio of 6:1 as optimum for PHB production by \u003cem\u003eR. sphaeroides\u003c/em\u003e N20 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In the current study, similar results were also obtained with optimum C/N ratio 8:1.Thus, a rationalized properties of C and N increase the efficiency of bacteria to produce PHB.\u003c/p\u003e \u003cp\u003eThe amount of initial biomass has a significant impact on PHB production. Lower initial biomass yields less production, whereas biomass higher than optimum decreases the PHB yield owing to increasing medium viscosity due to improper nutrient and air distribution. The outcome- possibly the intracellular depolymerase consumes the stored PHB granules as an energy source due to nutritional deprivation caused by increasing bacterium concentrations [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Khanna and Srivastava (2005) reported that 5% inoculum size was ideal for maximum PHB synthesis during optimization [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e49\u003c/span\u003e] though \u003cem\u003eAlcaligens spp\u003c/em\u003e and \u003cem\u003ePseudomonas oleovorans\u003c/em\u003e produced PHA just under 10% inoculum concentration [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In the current study the optimal starter for PHB generation was 5% suggesting an inoculum size of 5%-10% as an optimized amount of starter culture for PHB production.\u003c/p\u003e \u003cp\u003eSince PHB yield is also time-dependent, a temporal experiment of 24h, 48h and 72h was designed for optimum PHB production, resulting 48 hours as the optimum time-scale interval for the purpose. Alshehrei (2019) also reported that under the shaking conditions \u003cem\u003eBacillus\u003c/em\u003e species gave maximum PHB yield after 48 hours at 30\u0026deg;C and pH 7 [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e51\u003c/span\u003e], while Pervaiz (2022) reported the optimum yield of PHB after 48 hours at 37\u0026deg;C by \u003cem\u003eSerratia nematodiphila\u003c/em\u003e strain MB307 [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Similar results were also reported by Mittal \u003cem\u003eet al\u003c/em\u003e., (2024) that \u003cem\u003eCupriavidus necator\u003c/em\u003e yielded maximum PHB using banana as a substrate at 48hrs post-incubation, following a decline as time extends beyond 48 hours [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003epH of the medium affects the growth of PHB-producing bacteria as well as product biosynthesis. Alshehrei, F. (2019) found that maximum PHB at pH 7 was produced (F15) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e51\u003c/span\u003e], as also corroborated by [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTemperature is another physical factor affecting the PHB production. Getachew \u003cem\u003eet al\u003c/em\u003e., (2016) observed maximum PHB at 37oC by \u003cem\u003eBacillus species\u003c/em\u003e [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e54\u003c/span\u003e], though Desouky \u003cem\u003eet al\u003c/em\u003e., (2014) observed 35\u0026deg;C as optimum temperature for PHB yield by \u003cem\u003eBacillus thuringiensi\u003c/em\u003es [55]. In the current study also, 37 \u003csup\u003eo\u003c/sup\u003eC was an optimum fermentation temperature for PHB yield. Regardless, there seems to exist a threshold upper limit for temperature above which PHB energy reservoir would be spent to cope with high temperature stress instead of mass accumulation, as happened in case of \u003cem\u003ePseudomonas putida\u003c/em\u003e SS9 where PHB quantitatively decreased [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the essential factor in fermentation is the agitation rate which not only ensures homogenous mixing of the cell and effective heat transfer but also facilitates the efficient distribution of aeration throughout the medium. In the current study, agitation rate of 150 rpm proved to be the best both for PHB and dry cell weight yield. Shrimali \u003cem\u003eet al.\u003c/em\u003e, (2024) while working on \u003cem\u003eNeobacillus niacini\u003c/em\u003e GS1 found that agitation rate of 120 rpm was most efficient in increasing cell biomass and PHA production [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. These findings were consistent with those of Dalsasso, \u003cem\u003eet al.\u003c/em\u003e, 2019 where similar agitation rate of 150 rpm was reported for airflow effect on PHB [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe presence of the key functional group like CH3, CH2, C\u0026thinsp;=\u0026thinsp;O, C-O, CH and OH, is a deciding factor for the presence of PHB. Sola \u003cem\u003eet al\u003c/em\u003e., (2025) extracted PHB from \u003cem\u003eCynobacteria\u003c/em\u003e and characterized the extracted biopolymers as PHB by using FTIR [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Similarly, Thapa \u003cem\u003eet al.\u003c/em\u003e, (2018) characterized the extracted compounds on FTIR-analysis giving C\u0026thinsp;=\u0026thinsp;O peak, thus, confirming the isolated strains to be PHB producers [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In the current study, spectra of both test and reference samples were nearly identical (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgricultural waste residues, including sugarcane molasses, sugarcane bagasse, wheat bran, rice husk and whey are potential sources of carbohydrates which can be explored for efficient production of PHB [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Hosseini \u003cem\u003eet al\u003c/em\u003e., (2025) reported that biopolymer production from sugar beet molasses by \u003cem\u003eStaphylococcus aureus\u003c/em\u003e [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Melo \u003cem\u003eet al\u003c/em\u003e. (2025) characterized the structure and microbial PHB synthesis using commercial sugarcane molasses [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, Jung \u003cem\u003eet al\u003c/em\u003e. (2023) reported the successful bioconversion of untreated sugarcane molasses into PHB using tap water and a newly identified strain, \u003cem\u003ePriestia sp\u003c/em\u003e. YH4 [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRSM is a powerful statistical tool widely applied to optimize bioprocess parameters by evaluating the interactive effects of multiple variables simultaneously. Unlike conventional one-factor-at-a-time approaches, RSM reduces experimental runs while improving predictive accuracy and identifying optimal operational conditions. In the present study, Box\u0026ndash;Behnken design effectively determined the combined influence of key physiological parameters on PHB production, leading to enhanced polymer yield under optimized conditions. Tantoco \u003cem\u003eet al\u003c/em\u003e. (2023) applied RSM coupled with artificial neural network modeling to optimize saccharification and fermentation conditions for PHB production from corn stover hydrolysate achieving improved biomass and PHB concentration under optimal RSM-derived conditions[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. PHB production by \u003cem\u003eBacillus megaterium\u003c/em\u003e LSRB 0103 was optimized via RSM, where central composite design accurately predicted optimal conditions and the experimental results closely matched model predictions, validating the predictive power of RSM models [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Collectively, these studies validate the effectiveness of RSM as a reliable approach for improving PHB yield through systematic evaluation of process variables.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, the present study successfully demonstrated the capability of agro-industrial waste valorization for cost-effective biopolymer production. The PHB-producing bacterial isolate GSBB-52a-2, identified as \u003cem\u003eP. plecoglossicida\u003c/em\u003e MK-6 through 16S rRNA sequencing, showed efficient PHB biosynthesis when cultivated on sugarcane molasses as a low-cost carbon substrate. Sequential optimization using OVAT established significant fermentation parameters, yielding 7.8 g/L PHB under optimal conditions, while statistical optimization via RSM coupled with Box\u0026ndash;Behnken design further enhanced production to 7.9 g/L. Structural confirmation of the extracted polymer through FT-IR validated the biopolymer as PHB.\u003c/p\u003e \u003cp\u003eAlthough PHB production \u003cem\u003eP. plecoglossicida\u003c/em\u003e MK-6 has been previously reported, this work represents the first study to optimize its PHB biosynthesis using sugarcane molasses, highlighting an economically viable and sustainable alternative to refined carbon sources. The findings support circular bio economy and waste recycling strategies and provide a scalable framework for industrial PHB production. Future studies focusing on bioprocess scale-up and downstream processing optimization could further improve commercial feasibility.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\u003cp\u003ePHB: Polyhydroxybutyrate\u003c/p\u003e\u003cp\u003eFTIR: Fourier transform infrared spectroscopy\u003c/p\u003e\u003cp\u003eBLAST: Basic Local Alignment Search Tool\u003c/p\u003e\u003cp\u003eNCBI: National Center for Biotechnology Information\u003c/p\u003e\u003cp\u003eMSA: Minimal salt agar\u003c/p\u003e\u003cp\u003eMSM: Minimal salt medium\u003c/p\u003e\u003cp\u003ePHA: Polyhydroxyalkanoates\u003c/p\u003e\u003cp\u003ePCR: Polymerase chain reaction\u003c/p\u003e\u003cp\u003egDNA: Genomic Deoxyribose nucleic acid\u003c/p\u003e\u003cp\u003erRNA: ribosomal ribonucleic acid\u003c/p\u003e\u003cp\u003eANOVA: Analysis of Variance\u003c/p\u003e\u003cp\u003eOVAT: Variable at a Time\u003c/p\u003e\u003cp\u003eRSM: Response surface methodology\u003c/p\u003e\u003cp\u003eBBD: Box–Behnken design\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eOpen access funding provided by Ongoing Research Funding program, (ORF-2026-561), King Saud University, Riyadh, Saudi Arabia.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis research work was carried out in collaboration with all authors. HB and SA conceived and designed the study. HB and AB performed the experiments and collected the data. HB and AB analyzed the data and drafted the manuscript. N.B and AG performed statistical analysis of the data. WFAM, KFA and A.A.O contributed to data interpretation and critically revised the manuscript for important intellectual content. SA supervised the study and finalized the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors extend their appreciation to Ongoing Research Funding program, (ORF-2026-561), King Saud University, Riyadh, Saudi Arabia.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe 16S rRNA gene sequence generated in this study has been deposited in the NCBI GenBank database ( https://www.ncbi.nlm.nih.gov/genbank/ ) and received the accession number OQ236410. All other datasets generated and/or analyzed during the current study are included in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRajendran S et al. \u003cem\u003eReplacement of Petroleum Based Products With Plant-Based Materials, Green and Sustainable Energy\u0026mdash;A Review.\u003c/em\u003e Engineering Reports, 2025. 7(4): p. e70108.\u003c/li\u003e\n\u003cli\u003eMohanan N, et al. Microbial and enzymatic degradation of synthetic plastics. Front Microbiol. 2020;11:580709.\u003c/li\u003e\n\u003cli\u003eChen Y, Cheng X, Zeng Y. \u003cem\u003eThe occurrence of microplastic in aquatic environment and toxic effects for organisms.\u003c/em\u003e International Journal of Environmental Science and Technology, 2023. 20(9): p. 10477-.\u003c/li\u003e\n\u003cli\u003eAkinsemolu, A. and H. 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Woldesenbet, \u003cem\u003eProduction of biodegradable plastic by polyhydroxybutyrate (PHB) accumulating bacteria using low cost agricultural waste material.\u003c/em\u003e BMC research notes, 2016. 9(1): p. 509.\u003c/li\u003e\n\u003cli\u003eDesouky, S., et al., \u003cem\u003eScreening, optimization and extraction of polyhydroxyalkanoates (PHAs) from Bacillus thuringienesis.\u003c/em\u003e Journal of Advances in Biology \u0026amp; Biotechnology, 2014. 1(1): p. 40\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eBose, S.A., et al., \u003cem\u003eProcess intensification of biopolymer polyhydroxybutyrate production by pseudomonas putida SS9: a statistical approach.\u003c/em\u003e Chemosphere, 2023. 313: p. 137350.\u003c/li\u003e\n\u003cli\u003eShrimali, G., et al., \u003cem\u003eOptimized Polyhydroxybutyrate Production by Neobacillus Niacini GS1 Utilizing Corn Flour, Wheat Bran, and Peptone: A Sustainable Approach.\u003c/em\u003e Biomass, 2024. 4(4): p. 1164\u0026ndash;1177.\u003c/li\u003e\n\u003cli\u003eDalsasso, R.R., et al., \u003cem\u003ePolyhydroxybutyrate (PHB) production by Cupriavidus necator from sugarcane vinasse and molasses as mixed substrate.\u003c/em\u003e Process Biochemistry, 2019. 85: p. 12\u0026ndash;18.\u003c/li\u003e\n\u003cli\u003eSola, P.V., et al., \u003cem\u003ePHB in cyanobacteria: analyzing production through images processing and FT-IR techniques.\u003c/em\u003e New biotechnology, 2025.\u003c/li\u003e\n\u003cli\u003eThapa, C., et al., \u003cem\u003eIsolation of polyhydroxybutyrate (PHB) producing bacteria, optimization of culture conditions for PHB production, extraction and characterization of PHB.\u003c/em\u003e Nepal Journal of biotechnology, 6(1): p. 62\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eJung, H.J., et al., \u003cem\u003ePolyhydroxybutyrate (PHB) production from sugar cane molasses and tap water without sterilization using novel strain, Priestia sp. YH4.\u003c/em\u003e International Journal of Biological Macromolecules, 250: p. 126152.\u003c/li\u003e\n\u003cli\u003eTantoco, C.J.A., et al., \u003cem\u003eResponse Surface Methodology and Artificial Neural Network Optimization and Modeling of the Saccharification and Fermentation Conditions of the Polyhydroxybutyrate from Corn Stover.\u003c/em\u003e Philippine Journal of Science, 2023. 152(1).\u003c/li\u003e\n\u003cli\u003eBasak, S., et al., \u003cem\u003ePHB Production by Bacillus megaterium LSRB 0103 Using Cornstarch and Urea: S. Basak\u0026nbsp;\u003c/em\u003eet al. Current Microbiology, 2024. 81(6): p. 139.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bbit","sideBox":"Learn more about [BMC Biotechnology](http://bmcbiotechnol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bbit/default.aspx","title":"BMC Biotechnology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PHB, Pseudomonas plecoglossicida MK-6, Sugarcane Molasses, 16S rRNA, FT-IR, Box-Behnken Design, Response Surface Methodology","lastPublishedDoi":"10.21203/rs.3.rs-9174495/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9174495/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePolyhydroxybutyrate (PHB), is a sustainable, and green alternative to petrochemical plastic, however, its production cost remains high due to reliance on glucose. This study was designed to screen PHB producing bacteria from dumping soil to evaluate PHB production using agro-industry waste of sugarcane molasses as carbon source in reference to an efficient waste recycling practice. The PHB positive strain GSBB-5a2-2 was screened by using Sudan Black B and Nile Blue A and was identified as \u003cem\u003ePseudomonas plecoglossicida\u003c/em\u003e MK-6 based on 16S rRNA sequencing (GenBank accession number OQ236410), was selected for further analysis. The ideal fermentation variables were determined using single factor optimization, the maximum PHB (7.8 g/L) was obtained with 5% sugarcane molasses, ammonium sulfate as a nitrogen source, 8:1 C/N ratio, pH 7, 37\u0026deg;C and 150 rpm for 48hrs were found as optimum factors. Further optimization using Response Surface Methodology with Box-Behnken Design enhance PHB synthesis. The extracted PHB was identified via Fourier transform infrared spectroscopy (FT-IR). PHB production by \u003cem\u003ePseudomonas plecoglossicida\u003c/em\u003e MK-6 has been previously documented; however, this study reports for the first time to optimize PHB synthesis by this strain using sugarcane molasses as a low-cost carbon substrate, contributing to circular bio economy goals.\u003c/p\u003e","manuscriptTitle":"Sustainable Valorization of Sugarcane Molasses into Polyhydroxybutyrate by Pseudomonas plecoglossicida MK-6 and Optimization Using Response Surface Methodology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 09:44:38","doi":"10.21203/rs.3.rs-9174495/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T09:10:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T12:26:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T08:11:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174591697017602034910950009008859734888","date":"2026-04-07T05:40:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339204303140921798503442788457147785161","date":"2026-04-06T11:32:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272133568439069712455628324530314384828","date":"2026-04-06T09:19:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T09:11:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T09:05:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-06T08:15:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-03T20:04:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Biotechnology","date":"2026-04-03T19:59:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bbit","sideBox":"Learn more about [BMC Biotechnology](http://bmcbiotechnol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bbit/default.aspx","title":"BMC Biotechnology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90c8776b-5a98-4167-ac46-cd61134d76e9","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T09:10:06+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T09:25:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 09:44:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9174495","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9174495","identity":"rs-9174495","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}