Improvement of bacterial α-amylase production and application using two steps statistical factorial design

Author’s Accepted Manuscript Improvement of bacterial α-amylase production and application using two steps statistical factorial design Samia A. Ahmed, Faten A. Mostafa, Wafaa A. Helmy, Mohamed A. Abdel-Naby www.elsevier.com/locate/bab

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S1878-8181(16)30461-3 http://dx.doi.org/10.1016/j.bcab.2017.03.004 BCAB522

To appear in: Biocatalysis and Agricultural Biotechnology Received date: 29 November 2016 Accepted date: 5 March 2017 Cite this article as: Samia A. Ahmed, Faten A. Mostafa, Wafaa A. Helmy and Mohamed A. Abdel-Naby, Improvement of bacterial α-amylase production and application using two steps statistical factorial design, Biocatalysis and Agricultural Biotechnology, http://dx.doi.org/10.1016/j.bcab.2017.03.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Improvement of bacterial α-amylase production and application using two steps statistical factorial design

Samia A. Ahmed*, Faten A. Mostafa, Wafaa A. Helmy, Mohamed A. Abdel-Naby. Department of Chemistry of Natural and Microbial Products, National Research Centre, Dokki, Cairo, Egypt. Samia Abdel-Aziz Ahmed [email protected] *Corresponding Author: Samia A. Ahmed, Telephone: 002 02 6347972, Fax : 002 02 3370931

Abstract Five agro-industrial wastes (AIW) were selected to verify their potential as low cost substrate to produce α-amylase enzyme by bacterial strains using solid state fermentation (SSF) and submerged state fermentation (SMF). Among the AIW used strawberry leaves and watermelon rind produced the maximum α-amylase production under SMF by B.subtilis and B. licheniformis, respectively. Co-culture of B.subtilis and B. licheniformis (1:1) on watermelon rind (Citrullus lanatus) improved α-amylase production by 176.3 and 329.2%, respectively compared to mono culture of B.subtilis and B. licheniformis. Decreasing agitation speed to 100 rpm increased the production by 58.1%. In addition, the pretreatment of WMR with different methods before fermentation inhibited the enzyme production. Using two steps of statistical factorial design (Placket-Burman and Central Composite) to optimize the production medium enhanced α-amylase production by 10.3-fold. The produced α-amylase could effectively hydrolyze AIW, however the highest saccharification yield with the highest reducing sugar (75% and 15mg/ml) were obtained using cantaloupe peel (Cucumis melo). It is superior to use cantaloupe peel as a novel AIW for saccharification produced glucose (78.3%) and uronic acid (21.3%). Moreover, using Central Composite design to optimize saccharification condition of cantaloupe peel enhanced the yield of reducing sugar by 3.73fold. Key words: α-amylase, co-culture, factorial design, agro-industrial wastes, saccharification.

1. Introduction Enzymes are biological catalysis used in various sectors of industry. α-amylase (E.C. 3.2.1.1) has been in increasing demand due to its vital role in starch hydrolysis into low molecular weight sugars and its applications [Sundarram & Murthy, 2014]. α-amylase has been used in other processes, removing environmental pollutant, bakery, detergent, paper, alcohol and desizing of textiles industries [Sundarram & Murthy, 2014; Singh et al., 2015; Saini et al., 2016]. Many strategies have been adopted to increase enzyme production and decrease its production costs including use of microbial co-cultures, optimization of growth conditions, application of crude enzyme extracts and using AIW [Ali et al., 2016]. AIW have become the subject of intensive research as it rich in moisture, carbohydrate, protein, in some cases, antioxidants and other bioactive compounds [Panda et al., 2016]. Besides, it contains around 60-75% (w/w) starch, hydrolysable to glucose offers a good resource in fermentation processes [Soni et al., 2003]. In South Africa it has been estimated that agriculture wastes contribute around 4.2 million tons per annum [Oelofse and Nahman, 2013]. Microbial bio-

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processing of AIW has been proven to be a potential tool for cleaning up the environment beside production of value added products [Panda et al., 2016]. Bacillus sp. is popular strain for producing enzymes such as α-amylase [Massatto et al., 2012]. Cheaper substrate selection influences reduction of the enzyme production cost with removing environmental pollutant. Selection of substrate is based on its suitability for microorganism, availability, operational cost, conversion efficiency and non toxic nature [Panda et al., 2016]. Production of α-amylase may be classified SSF and SMF. SMF technique is suitable for microorganisms such as bacteria which require high moisture content for their growth [Sundarram & Murthy, 2014]. Moreover, the use of statistical designs such as Plackett-Burman and Central Composite has been carried out for α-amylase production [Duque et al., 2016; Panda et al., 2016]. These advance statistical approaches offer a design that allows the study of several factors that requires minimum number of runs, thus, saving time and resources. These also allow the screening of critical factors, provide information regarding optimized levels of each factor and knowledge of the interactions between factors and its effect on amylase production. Coculture is aerobic or an aerobic incubation of different microbial strains under aseptic conditions. Degradation of substrates occurs by the combined metabolic activity of these strains. To have a stable co-culture, the strains must be compatible and able to grow together [Panda et al., 2016]. Co-culture fermentation involve two or more organisms which lead to better utilization of substrate, increased resistance to contamination, and productivity as compared to mono cultures [Saini et al., 2016]. This study evaluated the production of α-amylase by mono and co-culture of bacterial strains utilizing cheap AIW. Besides, using two steps statistical factorial designed to optimize the production and the application process of biomass hydrolysis by crude α-amylase for biofuel purpose.

2. Material and Methods 2.1. Preparation of substrate Agro- industrial wastes such as orange peel (OP), watermelon rind (WMR), strawberry leave (SL), pomegranate peel (PoP), egg shill (ES), lemon peel (LP), wheat bran (WB), pea peel (PP) and cantaloupe peel (CP) were collected from local market of Giza, Egypt. They were washed with tap water to remove dirt and impurities. The washed substrates were then dried in an oven at 50°C for 24h, grinded in a laboratory grinder to ~0.5 cm particle size. 2.2. Microbial cultures and inoculum development B. licheniformis, B. circulans, B. macerans 314, B. megaterium, B. amyloliquefaciens, B. subtilis, and B. sterothermophilus were obtained from the Culture Collection of the National Research Centre, Dokki, Cairo, Egypt. B. licheniformis ATCC 21415 was obtained from American Type Culture Collection, USA. All bacterial strains were maintained on nutrient agar slants (NAS) at 35C and transferred weekly. To develop a homogenous inoculum suspension, a growth of each strain (2 slants of 24h old O.D600~0.11) was transferred into 50 ml of sterilized distilled H2O and was incubated in a shaking incubator at 150 rpm and 35C for 2h. For the develop a homogenous inoculum suspension for coculture, 2.5 ml from B. subtilis inoculum was mixed with 2.5 ml from B. licheniformis inoculum and was added to each production flask. 2.3. Screening the ability to produce α-amylase enzyme 2.3.1. Qualitative screening The bacterial strains were screened for their abilities to produced α-amylase by starch agar plates test. The sterilized medium containing starch (10%) and agar (12%) was used on sterilized Petri plates. Pure single colony of each bacterial strain was streaked on the culture media and was allowed to grow for 24 and 48h at 35C. Starch agar plates were flooded with iodine solution (1g iodine dissolved in an aqueous solution of potassium iodide 2%) for 10 min. Positive reaction due to starch hydrolysis is indicated by a clear zone around the bacterial growth. Blue black color on agar plate indicates negative test (Sajjad and Choudhry, 2012). 2.3.2. Quantitative screening

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This screening was done by determination of α-amylase activity according to Sajjad and Choudhry (2012) by adding 0.5 ml of clear culture filtrate (crude enzyme) to 0.5 ml of 1% starch solution prepared in acetate buffer (0.05M, pH 5.0). The mixture was incubated for 20 min at 40C and the released reducing sugars were determined by Somogyi method (1952). All the cultures were triplicates and the results are the mean. 2.4. Chemical analysis The moisture content was determined by drying the sample to a constant weight at 105C. The ash content was estimated according to Arumugan and Manikandan (2011) by heating the residue of moisture determination at 550C till constant weight. Total carbohydrate was estimated by phenol sulfuric acid using glucose as standard and the color density was measured at 490 nm (Dubois et al., 1956). 2.5. Hydrolysis of some AIW using crude α-amylase 2.5.1. Qualitative examination of the hydrolysis products These were performed by paper chromatography of the hydrolysates on Whatman No.1 filter paper (Jayme and Knolle, 1956) using the solvent system: n-butanol-acetone-water (4:5:1, v/v/v). For a comparison, authentic samples of galactose, glucose, manose, arabinose and xylose were co-chromatographed as reference substances. After chromatographic separation, the chromatogram was air dried and sprayed with 40-50 ml of the anilinephthalate reagent (Partridge, 1949),, air dried and then heated at 105°C for 10 min in an oven for developing the spots. 2.5.2. Quantitative determination of the hydrolysis products The detected spots in the chromatogram were cut off, divided into small strips and dropped into test tubes. Eluting agent (4.0 ml) was added to each tube and shaked for complete elution. The absorbance of the resulting colored solutions were determined in a BAUSCH, LOMB spectronic 2000 spectrophotometer at 390 and 360 nm for hexoses and pentoses, respectively. The quantities of sugars were determined by comparison to appropriate standard curves constructed under the same conditions. 2.6. Substrate pretreatment WMR the best substrate for maximum α-amylase production was pretreated with different methods and the carbohydrate content was determined for each pretreated samples before using for enzyme production. 2.6.1. Liquid hot water pretreatment (LHW) The fresh WMR were slurried with distilled water in a ratio 1:10 (solid: liquid) and autoclaved at 121°C for 1h (Arumugam and Manikandan, 2011). The pretreated sample was cooled to room temperature followed by centrifugation at 5000xg for 20 min and dried at 50°C in an oven for 24h. 2.6.2. Wet oxidation pretreatment In wet oxidation, the WMR was treated with distilled water in a ratio 1:2 (solid: liquid) in flask fully opening and was heated for 15 min at 121°C (Sarkar et al., 2012). After that the pretreated WMR was cooled to ~25°C and dried in an oven for 24h at 50°C. 2.6.3. Diluted alkaline pretreatment Watermelon rind was assessed using 0.5 M NaOH with solid:liquid ratio 1:20 (w/v) at 30°C for 1h (Begum and Alimon, 2011). The pretreated WMR was washed with acetate buffer (0.02M, pH 4.3) and washed with distilled H2O until pH of the sample reached neutral. Sample was dried over night in an oven at 50°C. 2.6.4. Diluted acid pretreatment This method was performed by adding diluted H2SO4 (0.05N) to WMR in a ratio solidliquid 1:20 (w/v) and incubating at 30°C and 150 rpm for 1 h (Begum and Alimon, 2011). The acid pretreated sample was cooled and the pH was adjusted to 6.0 with NaOH and dried at 50°C over night. 2.6.5. Microwave oven pretreatment Pretreatment of WMR in a microwave oven is also a feasible method and easy to operate (Sarkar et al., 2012). In this method, 1.0 g of each pretreated WMR with previous methods

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(LHW, wet oxidation, dilution H2SO4 and dilution NaOH) was subjected to microwave radiation power for 5 min. Samples were screened for enzyme production process. 2.6.6. Solar pretreatment One gram of the pretreated WMR with previous methods (LHW, wet oxidation, dilution H2SO4 and dilution NaOH) was exposes to the sunlight in an open Petri dish at ~35°C for 6h. Pretreated WMR were screened for enzyme production. 2.7. Fermentation technique for α-amylase production 2.7.1. Mono culture 2.7.1.1. Solid state fermentation (SSF) Each of the AIW (2.0 g) was used in 250 ml-Erlenmeyer flask, moistened with 5.0 ml of distilled H2O. After sterilization, media were inoculated with 5.0 ml of inoculum suspension (O.D600~0.11). The inoculated cultures were incubated at 35°C for 48h under static condition. The enzyme was extracted from the fermented biomass with distilled H2O (50 ml/ flask) at 35°C for 1h with shaking at 150 rpm. The eluted enzyme was separated from the biomass by centrifugation (5000xg for 20 min at 4°C) and the clear supernatant was used as the crude enzyme. 2.7.1.2. Submerged state fermentation (SMF) Submerged fermentation for α-amylase production was carried in 250 ml-Erlenmeyer flask containing 2.0 g of AIW moistened with 45.0 ml of distilled H2O. After sterilization, media were inoculated with 5.0 ml of inoculum suspension (O.D600~0.11) and incubated at 150 rpm and 35°C for 48h. Sample was done by centrifugation for the fermented biomass at 5000xg for 20 min under cooling condition (4°C). The clear supernatant was used as the crude α-amylase enzyme. 2.7.2. Co-culture for SMF process The selected bacterial strains were grown as mono culture (control) and as co-culture from B. licheniformis and B. subtilis in a ratio of 1:1 using the most suitable substrates (WMR, OP and SL) for SMF process. The inoculated flasks were incubated at 35°C and 150 rpm for 48h. The clear supernatant which obtained by centrifugation at 5000xg, 4°C for 20 min was used for enzyme assay. 2.8. Effect of agitation on the production of α-amylase The effect of agitation (rpm) on the production of α-amylase was determined at vary agitation speeds of 0, 50,100, 150 and 200 rpm at 35°C for 48h. 2.9. Effect of moistening agent on α-amylase production The effect of moistening agent on α-amylase production was studied by adding 45.0 ml of different moistening agents (tap H2O, distilled H2O and acetate buffer 0.05M, pH 5.0) to the 2.0 g of WMR. After sterilization, each flask was inoculated with 5.0 ml of co-culture homogenous inoculum suspension and was incubating at 35°C and 100 rpm for 48h. 2.10. Effect of duration on α-amylase production The effect of incubation period was determined by incubating the inoculated flasks for variable incubation period (24, 48, and 72h) at 35°C and 100 rpm. 2.11. Effect of WMR pretreatment on α-amylase production The pretreated WMR (2.0 g) with different methods (LHW, wet oxidation, diluted alkaline, diluted acid, microwave and solar) as mentioned previously was used as substrate for SMF in the production media. After 48h incubation at 35°C and 100 rpm, the culture was centrifuged at 5000xg for 20 min and 4°C. The clear supernatant was used as the crude enzyme. 2.12 Optimization of α-amylase production by statistical factorial design 2.12.1. Placket-Burman design In this design we investigated the effect of eleven factor on α-amylase production including A: WMR, B: SL, C: OP, D: lactose, E: glucose, F: CaCl, G: NaCl, H: KCl, J: Tween 80, K: peptone, L: NH4Cl. Each of these factors was studied with low level (-1) and high level (+1). Total number of experiments was 12 runs based on the rule n+1, where n: represents the number of factors under investigation. In the experimental design, each row represented an experiment, and each column represented an independent variable (Table 1). The statistical significance was determined by F-value, and the proportion of variance

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explained by the model obtained was given by the multiple coefficient of determination, R 2. Experimental responses were analyzed by first order model by the following equation: R1 (αamylase activity U/ml) = βº+ ΣβiXi βº is the model intercept and βi is the linear coefficient, and Xi is the level of the independent variable. 2.12.2. Central Composite design (CCD) In this design we study the quantitative effect of the two most effective factors determined from the Placket-Burman design including A:WMR concentration and B: KCl. Variables with 5 centre points (low -2, -1, moderate 0, and high +1, +2) were used in CCD which gives the total of 13 run as shown in Table 2. Statistical analysis of the model was performed using the analysis of variance (ANOVA). 2.13. Enzyme hydrolysis (saccharification) of some AIW Different AIW as OP, LP, WB, PP, PoP, SL and CP (with the same amount of total carbohydrate 0.18 g) was mixed with 5.0 ml of crude enzyme (contain 128.63U) in acetate buffer (0.05M, pH 5.0) and the total volume 10 ml. The reaction mixture was incubated at 50°C and the control used preheated enzyme. At indicated intervals time (24h), samples were withdrawn and heated in a boiling water bath for 15 min to inactivate the enzyme, centrifuged at 5000xg and the extension of saccharification on supernatant was measured by the reducing sugar Somogyi method (Somogyi, 1952). The produced mono saccharides were identified by paper chromatography, and were determined quantitatively. Saccharification yield (%) = (Reducing sugar x0.9/ Total carbohydrate in substrate)x100. 2.14. Optimization of CP saccharification by CCD In this step we tried to obtain the highest reducing sugars yield resulting from hydrolysis of CP carbohydrate by crude α-amylase. This achieved by CCD optimizing four independent variables α-amylase units (A), CP weight (B), time h (C),polyethylene glycol (PEG) concentration% (D). Each variable was tested with five levels low -2, -1, center point 0, high level +1, +2 as shown in Table 3 giving 29 run.

3. Results and discussion 3.1. Screening the ability to produce α-amylase enzyme 3.1.1. Qualitative screening Bacterial strains were screened for their abilities to produce α-amylase by applying them to starch agar plates. As shown in Fig. 1, of all the positive tested strains, B. subtilis and B. licheniformis were positive with the biggest clear zones around the bacterial growth. Blue black color indicates a negative test due to the trapping of the iodine inside the helical structure of starch. On the other side, B. macerance and B. amyloliquefaciens were negative for α-amylase production. 3.1.2. Quantitative screening 3.1.2.1. Mono culture for α-amylase production A comparative evaluation of α-amylase production by the positive bacterial strains using SSF and SMF was done. Nutritional parameters play a very major role in maximizing the yield of the enzyme production. The role of different AIW impact on α-amylase production was evaluated. From Fig.2 it can be seen that SMF was found to support maximum yield of αamylase than in SSF. SMF is suitable for microorganism such as bacteria which require high moisture content for their growth (Sundarram & Murthy, 2014). B. subtilis and B. licheniformis were the most producers for α-amylase enzyme. The highest level of α-amylase from B. subtilis was obtained by utilizing SL (as a new substrate) followed by OP and WMR. However, WMR is the best preferred substrate for maximum α-amylase production by B. licheniformis. Hence the following experiment was carried out using SMF by mono and coculture of B. subtilis and B. licheniformis on the most suitable substrates (WMR, OP and SL). Mouna imen and Mahmoud (2015) produced 8.26 U/ml α-amylase by Streptomyces sp. using SMF of orange waste which was lower than our results. There are very few researches using

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WMR for enzyme production especially α-amylase, moreover, no one found on utilizing SL as solid substrate for any enzyme production. 3.1.2.2. Co-culture for α-amylase production Mono culture of B. subtilis and B. licheniformis was compared with co-culture of them for α-amylase production. As shown in Fig. 3 B. subtilis has been successfully co-cultivated with B. licheniformis with highest level of α-amylase (32.9 U/ml) utilizing WMR. WMR contains 25.9% total carbohydrate, 4% moisture and 8% ash content. WMR was found to be the best substrate for α-amylase production by co-culture enhanced the production yield by 329.2 and 176.3% compared with mono culture of B. licheniformis and B. subtilis, respectively. Saini et al. (2016) reported that, co-culture fermentations may lead to better substrate utilization, increased productivity and increased resistance to contamination. Our result is higher than that obtained by Abdullah et al. (2016) who produced 15.0 U/ ml of αamylase by co-culture consists of B.cereus and B. thuringiensis. Watermelon (Citrullus lanatus) is an important crop consisting of about 35% rind goes to waste. Besides, WMR contains vitamins (A, B, C and E), mineral salts (K, Mg, Ca and Fe), and specific amino acids (El-Badry et al., 2014). WMR is waste, available, cheap, non toxic and it contains sufficient nutrients that support good microbial growth and synthesize high yield of α-amylase enzyme beside reduce pollution problems. Other AIW like banana peel and wheat bran were used for α-amylase production by B. subtilis (Sajjad and Choudhry, 2012; Sundarram and Murthy, 2014). Co-culture and WMR were selected for further studies. 3.2. Effect of agitation on the production of α-amylase Agitation intensity influences the mixing and O2 transfer rates in many microbial fermentation which influences microbial growth and products formation. Maximum αamylase production was observed at 100 rpm (56.5 U/ml) with 72.1% increased in the yield. At higher or lower agitation speed (150 and 50 rpm) bacterial growth decreased (data not shown) and thus enzyme production decreased by 41.9 and 8.0 %, respectively (Fig. 4). Gupta (2003) pointed to that 300 rpm have normally been employed for various microorganisms to produce α-amylase. 3.3. Effect of moistening agents on α-amylase production The results in Fig. 5 showed that distilled H2O is the best moistening agent for αamylase production which is similar to that obtained by Sodhi et al. (2005). A decrease of 24.3% with tap H2O and 36.9% with acetate buffer for α-amylase production. Using only distilled H2O for maximum enzyme yield decreased the production cost. This might be due to that WMR is rich substrate with necessary nutrients for microorganisms. Singh et al. (2014) reported that maximum amylase production was observed with nutrient salt solution followed by distilled H2O and tap H2O. The effect of moistening agent on microbial growth and enzyme production might be attributed to the impact on the physical properties of solid substrate (Singh et al., 2014). 3.4. Effect of duration on α-amylase production The effect of incubation periods from 24 to 72h on α-amylase production were carried out by co-culture of B. subtilis and B. licheniformis utilizing untreated WMR under SMF. Incubation time considered one of the most important factors that affect the enzyme production by microorganisms. Maximum α-amylase production was obtained at 48h followed by a reduction at 72h (data not shown). This could be due to that bacterial strains were in their exponential phase. Decreasing the production with increasing incubation time might be due to the depletion of nutrients and increased the inhibitory metabolites (Ali et al., 2016). Our result is agreed with that reported by Sodhi et al. (2005). However, Sajjad and Choudhry (2012) found that the cell cultures of B. subtilis given the highest α-amylase production at 24h. 3.5. Effect of WMR pretreatment on the production of α-amylase Untreated WMR appear to provide sufficient necessary nutrients for co-culture to grow and synthesize high level of α-amylase. Pretreatment methods refer to solubilization and separation of one or more of the components of AIW. Goals of an effective pretreatment are: formation of sugars directly or subsequently by hydrolysis, yield maximum content of

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fermentable sugar, limiting the loss of carbohydrate, minimizing formation of inhibitors due to degradation, reduce energy impute, minimize costs (Sarkar et al., 2012; Anwar et al., 2014). All of the above mentioned criteria should be comprehensively considered as a basis to obtain highest level of product. However, the results (Fig. 6) showed that more α-amylase was obtained from untreated than treated WMR. This result might be due to the involved of some unknown compounds in the induction of α-amylase expression may be removed during pretreatment or that chemical generated during pretreatments may inhibit bacterial growth or enzyme induction. For example, furfural and hydroxyl methyl furfural are the most important inhibitors during fermentation of dilute-acid pretreated solid substrate, therefore the amount of microbial growth was decreased (El-Tayeb, 2012). Similar results were obtained by Kim et al. (2014) and Yoon et al. (2014). 3.6. Optimization of α-amylase production by statistical factorial design 3.6.1. Placket-Burman design This design was used to screen important independent variables (Placket and Burman, 1946) affecting α-amylase production. Eleven factors were tested as shown in Table 1, there was great variation from 0.0 to 303.76 U/ml reflecting the importance of medium optimization to attain higher productivity. The significance of the model is indicated by analysis of variance as shown in Table 4. Nine factors WMR (A), SL (B), OP (C), lactose (D), glucose (E), CaCl (F), KCl (H) and peptone (K) were found to be significant on α-amylase production. The following model equation was proposed for α-amylase activity: R1 (α-amylase activity U/ml) = +337.95 +11.804*WMR conc. -16.364*SL -24.25*OP 9.629*Lactose -6.50*Glucose -39.683*CaCl -33.093*NaCl +33.18*KCl -45.157*peptone. The magnitude and direction of the factor coefficient explained the effect of each medium component on α-amylase production. The (-) and (+) symbol in the coefficient refers to negative and positive effect as shown in Fig. 7, respectively of the tested factor on α-amylase production. The effectiveness of the model was tested by analysis of variance as shown in Table 4, determination of squared regression correlation coefficient R2 (0.9994), adj R2 value (0.9969) was found to be very close to R2. Usually, Low CV (coefficient of variation) value 4.49 was preferred indicating accuracy and success of the design conducted (Sen and Swaminathan, 2004). The maximum α-amylase production was achieved in run 5. This design caused 5.37- fold increase in α-amylase production which is higher than that reported by some authors (Abdel-Fattah et al., 2013; Abel-Nabey and Farag, 2016). On the other hand, Tween 80 (J) and NH4Cl (L) were insignificant on α-amylase production. Of the nine significant factors only substrate (WMR) and KCl showed positive effect on α-amylase production. In contrast, Duque et al (2016) found that Tween 80 had a significant effect and substrate concentration had no effect on α-amylase production by Enterococcus faecium DMF78. The other seven factors showed negative effect on α-amylase production including peptone in contrast to that reported by Duque et al (2016). Also, CaCl showed significant effect on αamylase production agreeing with Prajapati et al. (2015) while Duque et al. (2016) showed that it had no effect on enzyme production. Glucose and lactose showed negative effect on enzyme production contract to that reported by some authors (Ramachandran et al., 2004; Hassan and Karim, 2015; Prajapati et al., 2015). 3.6.2. Central Composite Design (CCD) As shown in Table 2, the α-amylase activity varied from 0 in run 3 to maximal production 584.24 U/ml in run 6 causing 10.33 -fold increase in enzyme production if compared with un-optimized medium. Based on the results, second order polynomial equation obtained as follows: R1 α-amylase activity (U/ml) = +199.80 +73.36*WMR + 160.26*KCl +4.55 *WMR*KCl 42.29*WMR2 +73.51*KCl2. Table 5 shows the analysis of variance. The Model F-value of 932.45 implies the model is significant. Since F-value is greater than tabulated F-value the model is a good prediction of the experimental results according to Shi et al. (2009). The R2 is 0.9985 indicating that 99.85% of the variability in the response could be explained by the model (Montgomery, 1997). As the values of predicted R2 of 0.9892 and adjusted R2 0.9974 are closer to each other this mean the success of the model. As result of optimization process

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via two steps statistical factorial design to obtain high α-amylase productivity 584.24U/ml (increased by 933.34%) the fermentation medium most contain (g/l): KCl 11.04, peptone 2.0, NH4Cl 2.0 and WMR 17.50g/flask. 3.7. Enzyme hydrolysis (saccharification) of some AIW Saccharification is the critical step for biotechnology where complex carbohydrates are converted to simple monomers. Compared to acid hydrolysis, enzymatic hydrolysis is advantageous due to it requires less energy, mild environment conditions, low utility cost, low corrosion, low toxicity, and no inhibitory by-product is formed (Sarkar et al., 2016). As shown in Fig. 8, from all tested AIW (OP, CP, PP, LP, WB, PoP and SL) the highest saccharification yield was obtained from CP, PP and OP, respectively. Degradation of different AIW by crude α-amylase enzyme confirms that other hydrolyzing enzymes were present in the filtered (pectinase, cellulase and xylanase). The reducing sugar recovery after highest enzymatic hydrolysis of CP, PP and OP are presented in Table 6.The highest saccharification yield 75% was obtained from a novel substrate CP (Cucumis melo) which is an excellent source of vitamin (A,C, beta-carotene, B1,2,3,5,6,9 and K), minerals (Calcium, iron, Magnesium, Manganese, Phosphorus, Potassium, Sodium, Zinc) which may activated the other enzymes in crude amylase preparation. Glucose appeared as the highest level 78.3% from CP hydrolysate pointed to the presence of other hydrolytic enzymes like cellulase. Uronic acid from CP, OP and PP were 21.3, 9.7 and 9.6%, respectively pointed to the presence of pectinase in the crude α-amylase preparation. The results were compared with acid hydrolysis and confirmed by paper chromatography (Fig.9). The presence monosaccharide from CP, PP and OP were determined quantitatively. The results showed that enzymatic hydrolysis of CP, OP and PP produced glucose (78.3, 39.3 and 35.8%). However, acid hydrolysis of CP, OP and PP produced 77.6, 60.7 and 63.8% glucose, respectively. The monosaccharide converted from CP could be used further in the fermentation of other products. 3.8. Optimization of cantaloupe peel saccharification by CCD The application of response surface methodology (Box et al., 1978) yielded the following regression equation: R1 reducing sugar (mg/ml) = +22.04 -2.5 * α-amylase uints -0.78 * CP+ 7.20 * time +7.61* PEG conc. -0.31 * α-amylase uints * CP -4.66 * α-amylase uints * time -5.72 * α-amylase uints * PEG conc. +1.32 * CP * time +2.72* CP * PEG conc. +8.88 *time*PEGconc.+2.19*α-amylase uints2 -5.37* CP2 -2.97* time2 +0.55 * PEG conc.2 Where R1 is the reducing sugars, analysis of variance (ANOVA) are given in Table 7. The effectiveness of the model was checked by the determination coefficient R 2 (0.9814). Indicating that only 1.86% of the total variations are not explained by the model. The selected variables are the significant factors as indicated in Fig. 10a normal probability plot where data points lie close to the straight line. Also, the success of the model was indicated by the closeness of the actual and predicted results as shown in Fig. 10b. The maximal reducing

sugars production was obtained in run 20 (55.99 mg/ml) compared to 15 mg/ml which obtained from un-optimized saccharification conditions. So, the CCD for optimize CP saccharification enhanced the reducing sugar yield by 3.73-fold. This can be attributed to the presence of high level of PEG which adsorb to lignin surfaces resulting in reduction of unproductive enzyme binding making the contact between the enzyme and substrate more easily resulting in hydrolysis yield (Börjesson et al., 2007; Lou et al., 2013).

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4. Conclusion In conclusion, the present approach should be a superior for proper bio-utilization of AIW and their conversion into useful products. Co-culture of B. licheniformis and B.subtilus considerable potential to utilize WMR as a cost effective growth supported medium with increasing the production of α-amylase enzyme between 2.0 to 4.3-fold in comparison with monoculture. To the best of our knowledge, very few research on utilizing WMR and no one on utilizing SL as new and sole nutrient source for microorganism. Statistical optimization employing factorial design was proved to be valuable tool for the optimization of α-amylase production enhanced the yield by 10.33-fold. Moreover, CCD for optimize CP saccharification enhanced the reducing sugar yield by 3.73-fold. The mono saccharides converted from CP (78.3% glucose and 21.3% uronic acid) could be used further in the fermentation of ethanol, organic acid, SCP, etc. This research work may be meaningful both in the utilization and conversion of AIW, and in the reduction of environmental pollution.

Conflict of interest There is no conflict of interests regarding the publication of this paper.

Acknowledgements Authors are highly thankful National Research Centre (Egypt) for providing financial support for the conduct the project numbered P100224.

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11

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Fig. 1: Primary screening of bacterial strains for α-amylase production. Fig.2: Alpha-amylase production by bacterial strains at 48h using solid state fermentation (SSF) and submerged state fermentation (SMF). Fig. 3: Production of α-amylase enzyme by mono and co-culture. Fig. 4: Effect of agitation on α-amylase enzyme production. Fig. 5: Effect of moistening agents on α-amylase enzyme production Fig. 6: Effect of pretreatment of watermelon rind on α-amylase production. Fig. 7: Pareto chart showing the effect of each factor on α-amylase production. Fig. 8: Saccharification process of some AIW by crude α-amylase enzyme. Fig.9: Paper chromatography for enzyme and acid hydrolysis of agroindustrial waste, orange peel (OP), Pea peel (PP), and Cantaloupe peel (CP), standard monosaccharide (xylose, galactose, glucose, manose and arabinose).

Fig. 10: Normal % probability versus studentized residuals (a) and scatter diagram of predicted versus actual response (b) in reduced quadratic model for α-amylase production system.

12

Table 1: Placket-Burman design for α-amylase production Run

Factor1 A:WMR g/flask

Factor2 B:SL g/flask

Factor3 C:OP g/flask

Factor4 D:Lactose g/l

Factor5 E:Glucose g/l

Factor6 F:CaCl g/l

Factor7 G:NaCl g/l

Factor8 H:KCl g/l

Factor9 J:Tween80 ml/l

Factor10 K:peptone g/l

Factor11 L:NH4Cl g/l

1 2 3 4 5 6 7 8 9 10 11

5.00 5.00 10.00 10.00 5.00 10.00 10.00 10.00 5.00 5.00 5.00

0.00 5.00 5.00 5.00 0.00 0.00 0.00 0.00 5.00 5.00 0.00

0.00 0.00 5.00 0.00 0.00 0.00 5.00 5.00 5.00 5.00 5.00

5.00 5.00 0.00 0.00 0.00 0.00 5.00 5.00 5.00 0.00 0.00

0.00 5.00 0.00 0.00 0.00 5.00 5.00 0.00 0.00 5.00 5.00

1.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0.00 1.00 1.00

1.00 1.00 1.00 0.00 0.00 1.00 0.00 1.00 0.00 1.00 0.00

0.00 1.00 0.00 1.00 0.00 1.00 0.00 1.00 1.00 0.00 1.00

1.00 1.00 1.00 1.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00

4.00 2.00 4.00 2.00 2.00 4.00 2.00 2.00 4.00 2.00 4.00

4.00 2.00 2.00 4.00 2.00 4.00 4.00 2.00 4.00 4.00 2.00

Response 1 R1 Amylase activity(U/ml) 98.32 142.59 37.51 280.25 303.76 244.64 166.68 153.79 0 0 54.4

12

10.00

5.00

0.00

5.00

5.00

1.00

0.00

0.00

0.00

4.00

2.00

70.32

Table 2: Central Composite Design for α-amylase production Run Factor A: WMR (g/flask)

Factor B: KCl (g/l)

R1: α-amylase activity (U/ml)

code

actual

code

actual

actual

predicated

1

1

20.00

-1

5.00

146.27

139.57

2

-1

15.00

1

10.00

300.52

313.37

3

-1

15.00

-1

5.00

0.00

1.95

4

0

17.50

0

7.50

199.80

199.80

5

1

20.00

1

10.00

465.00

469.20

6

0

17.50

1.414

11.04

584.24

573.46

7

-1.414

13.96

0

7.50

20.67

11.48

8

1.414

21.04

0

7.50

215.93

218.97

9

0

17.50

-1.414

3.96

115.55

120.18

10

0

17.50

0

7.50

199.80

199.80

11

0

17.50

0

7.50

199.80

199.80

12

0

17.50

0

7.50

199.80

199.80

13

0

17.50

0

7.50

199.80

199.80

13

Table 3: Central Composite Design for saccharification of cantaloupe peel Run

Factor 1

Factor 2

Factor 3

Factor 4

R1

A:amylase uints

B: CP con,

C: time

D: PEG conc.

Reducing

(U)

(g)

(%)

sugars

(h)

(mg/ml) 1

+1 (200)

+1 (2)

-1 (18)

+1 (0.1)

3.89

2

0 (128.63)

0 (1.014)

0 (24)

0 (0.05)

22.04

3

+1 (200)

+1 (2)

+1 (48)

-1 (0.025)

3.55

4

0 (128.63)

0 (1.014)

-2 (6)

0 (0.05)

0.00

5

+1 (200)

+1 (2)

-1 (18)

-1 (0.025)

9.43

6

+1 (200)

-1 (0.5)

-1 (18)

-1 (0.025)

2.56

7

+1 (200)

-1 (0.5)

+1 (48)

+1 (0.1)

20.87

8

-1 (100)

-1 (0.5)

-1 (18)

+1 (0.1)

8.75

9

0 (128.63)

0 (1.014)

0 (24)

0 (0.05)

22.04

10

-1 (100)

-1 (0.5)

-1 (18)

-1 (0.025)

4.00

11

-2(50)

0 (1.014)

0 (24)

0 (0.05)

37.50

12

+1 (200)

+1 (2)

+1 (48)

+1 (0.1)

30.00

13

0 (128.63)

0 (1.014)

+2 (72)

0 (0.05)

25.25

14

-1 (100)

-1 (0.5)

+1 (48)

-1 (0.025)

9.75

15

-1 (100)

+1 (2)

-1 (18)

+1 (0.1)

10.00

16

0 (128.63)

0 (1.014)

0 (24)

0 (0.05)

22.04

17

-1 (100)

+1 (2)

+1 (48)

-1 (0.025)

5.50

18

-1 (100)

+1 (2)

-1 (18)

-1 (0.025)

0.00

19

0 (128.63)

0 (1.014)

0 (24)

0 (0.05)

22.04

20

-1 (100)

+1 (2)

+1 (48)

+1 (0.1)

55.99

21

+2 (250)

0 (1.014)

0 (24)

0 (0.05)

29.00

22

0 (128.63)

-2 (0.25)

0 (24)

0 (0.05)

6.03

23

0 (128.63)

0 (1.014)

0 (24)

+2 (0.5)

42.57

24

-1 (100)

-1 (0.5)

-1 (18)

+1 (0.1)

1.98

25

0 (128.63)

0 (1.014)

0 (24)

0 (0.05)

22.04

26

-1 (100)

-1 (0.5)

+1 (48)

+1 (0.1)

49.90

27

0 (128.63)

+2 (4)

0 (24)

0 (0.05)

0.00

28

0 (128.63)

0 (1.014)

0 (24)

-2 (0.01)

10.86

29

+1 (200)

-1 (0.5)

+1 (48)

-1 (0.025)

7.29

14

Table 4: Analysis of variance of Placket-Burman design Source

Sum of

df

Mean square

F-value

squares

p-value

Remark

Prob>f

Model

1.205E+005

9

13393.60

396.96

0.0025

A-WMR conc.

10450.08

1

10450.08

309.72

0.0032

B-SL

20083.54

1

20083.54

595.24

0.0017

C-OP

44104.69

1

44104.69

1307.18

0.0008

D-Lactose

6953.34

1

6953.34

206.08

0.0048

E-Glucose

3168.75

1

3168.75

93.92

0.0105

F-CaCl

4724.30

1

4724.30

140.02

0.0071

G-NaCl

3285.51

1

3285.51

97.38

0.0101

H-KCl

3302.74

1

3302.74

725.23

0.0014

Residual

462.76

7

66.11

Lack of fit

462.76

3

154.25

Pure error

0.00

4

0.00

Cor total

3.057E+005

significant

R2: 0.9994, adj. R2: 0.9969, pred. R2: 0.9799, C.V.%: 4.49

Table 5: Analysis of variance of Central Composite Design for optimization of αamylase production Source

Sum of

df

squares

Mean

F-value

square

p-value Prob>f

Model

3.052E+005

5

61047.79

923.45

<0.0001

A-WMR

43054.89

1

43054.89

651.28

<0.0001

B-KCl

2.055E+005

1

2.055

3108.00

<0.0001

AB

82.90

1

82.90

1.25

0.2997

A2

12439.88

1

12439.88

188.17

<0.0001

B2

37591.10

1

37591.10

568.63

<0.0001

Residual

462.76

7

66.11

Lack of Fit

462.76

3

154.25

Pure error

0.00

4

0.00

Cor total

3.057E+005

conc.

15

Remark

significant

R2: 0.9985, adj. R2: 0.9974, pred. R2: 0.9892, C.V.%: 3.71

Table 6: Analysis of some agro-industrail waste and its hydrolysate by crude α-amylase preparation

Galactose

Glucose

Arabinose

4.0

66.3

13.3

9.7

9.4

39.3

41.5

-

Pea peel

29.0 5.6

8.2

70.0

14.0

9.6

-

35.8

20.8

33.7

Cantaloupe Peel

17.7 6.5

10.1

75.0

15.0

21.3

-

78.3

-

Table 7: Analysis of variance of Central Composite Design for saccharification of cantaloupe peel Source Model A-amylase units B-sub.wt. C-time D-PEG conc. AB AC AD BC BD CD

Sum of squares 6332.26 156.69 14.74 1243.41 1391.23 1.51 347.3 523.13 28.08 118.28 1261.03

df 14 1 1 1 1 1 1 1 1 1 1

Mean square 452.30 156.69 14.74 1243.41 1391.23 1.51 347.3 523.13 28.08 118.28 1261.03

16

F- value 52.86 18.31 1.72 145.31 162.59 0.18 40.59 61.14 3.28 13.82 147.37

P-value prob˃F ˂0.0001 0.0008 0.2105 ˂0.0001 ˂0.0001 0.6804 ˂0.0001 ˂0.0001 0.0915 0.0023 ˂0.0001

Significant

Xylose

Uronic acid

27.1 4.0

Total carbohydrate

Orange peel

Agro-industrial waste

Reducing sugar (mg/ml)

composition (%)

Saccharification yield (%)

composition (%)

Ash content

Monosaccharide

Moisture content

Chemical

-

A2 B2 C2 D2 Residual Lack of fit Pure Error

124.26 748.22 228.53 7.98 119.79 119.79 0.000

1 1 1 1 14 10 4

Cor Total

6452.05

28

124.26 748.22 228.53 7.97 8.56 11.98 0.000

17

14.52 87.44 26.71 0.93

0.0019 ˂0.0001 0.0001 0.3507

Control

B.circulancs

B.licheniformis ATCC21415

B.sterothermophilus

B.megaterium

B.licheniformis

B.subtilis

16

SMF Alpha amylase activity (U/ml)

Watermelon rind

10 8 6 4 2

ic B.c

ns ula

5 m is ns i lus rm 141 eriu cie oph gat ni fo C2 efa m e e r u C h e q T c B.m l oli oth B.l i is A ter my form B.s i B. a n ch e Bacterial srains B.li

10

Alpha-amylase activity (U/ml)

Pomgranate peel Strawberry leaves

12

0

SSF

Orange peel Egg shill

14

Orange peel Egg shill

8

Pomgranate peel Stawberry leave

i li s ubt B.s

Watermelon rind

6 4 2 0

s lan ic u B. c

t er ega B. m

ium B.

am

ef liqu ylo

ens aci

ic B. l

CC AT

15 214

m is ifor Bacterial hen

f or eni ich B. l

strains

mis B

e rm roth . st e

ilus oph

ilis ubt B. s

40

Orange peel

Alpha-amylase activity (U/ml)

35

Watermelon rind

Straw berry leave

30 25 20 15 10 5 0

B.licheniformis

B.subtilis

Bacterial culture

B.licheniformis +B.subtilis

Alph-amylase activity (U/ml)

75 60 45 30 15 0

0

50

rpm

100

150

200

ea

Pretreatment methods

+I rri da

tio n

O4

un t re at ed (c on t ro l)

So la r

H+ So lar

l ar

la r

50

40

30

20

10

Total carbohydrate (%)

Distiled w ater

SO 4+

aO

ea m+ So

Moistening agent

H2

N

St

Activ ity

W +S o

Tap w ater

LH

t io n Na OH +I rri da tio H2 n SO 4+ I rr ida tio n

m

W +I rri da

60

H2 S

70

St

m

aO H

ea

W

0

LH

N

St

LH

Alpha-amylase activity (U/ml)

Alph-amylase activity (U/ml) 70

60

50

40

30

20

10

Acetat buf f er

total carbohy drate

30

25

20

15

10

5

0 0

ra ng

Ca nt a

pe el

pe el

lo up e pe el

le av es

at e

be rry

gr an

Pe a

12

10

8

6

4

Saccharification yield (%)

Reducing sugar (mg/ml)

St ra w

Po m

Agro-industrail waste

W he

pe el

pe el

at br an

Le m on

O

Reducing sugar (mg/ml) 16 Saccharif ication y ield (%)

90

14

75

60

45

30

2 15

0 0

a

b

.