In this present studyα-amylase producing bacteria were isolated from the soil near North Bengal University canteen on an amylase agar medium.Primary screening of the isolates was done by iodine test,based on the clea...In this present studyα-amylase producing bacteria were isolated from the soil near North Bengal University canteen on an amylase agar medium.Primary screening of the isolates was done by iodine test,based on the clear zone around the bacterial colonies in the media plates.Seven bacterial isolates were selected and quantitatively screened for amylase production.Among them,strain sps2 showing the highest amylase activity(215±2 IU/ml)was selected for further studies.Isolated strain sps2 was identified both morphologically and biochemically and further confirmed by 16s rRNA sequencing as Streptomyces pratensis sps2(Gene bank accession No.OP 236721).Optimization ofα-amylase produced by the isolate was conducted under submerged fermentation of low-cost carbon source banana peel,using a one factor at a time(OFAT)approach.Maximum amylase activity of 821.33±0.57 IU/ml was recorded after 2 days of fermentation.The artificial neural network(ANN)model was employed using the OFAT data to generate a predictive model for amylase production and the R-value of 0.94496 and the RMSE of 64.82 suggesting that the neural network has the precise capability to generate an optimization environment.Ammonium sulfate precipitation and dialysis techniques were used to partially purify theα-amylase and then characterized.The enzyme was found to have a molecular mass of 28 kDa(SDS-PAGE and zymogram)with a K_(m)and V_(max)of 2 mg/ml and 1000μmol/min,respectively.The enzyme was found to be thermostable in the temperature range of 4-90℃ and more stable at alkaline pH with optimum pH of 7.展开更多
Response surface methodology(RSM)and artificial neural networks(ANN)are considered the most efficient way for opti-mization and modeling studies to design and develop various biosimilars.The primary objective of this ...Response surface methodology(RSM)and artificial neural networks(ANN)are considered the most efficient way for opti-mization and modeling studies to design and develop various biosimilars.The primary objective of this study was to create empirical modeling and optimization of media parameters for producing B.halotolerans VSH 09 lipase using RSM and ANN.One-factor-at-a-time(OFAT)analysis revealed that triacylglycerols hydrolyzed by lipase manifest substantial activity.The subsequent screening for best carbon,nitrogen,and inducer was performed using the Placket-Burman design(PBD).The statistically significant variables were further examined for their optimum level using Box-Behnken design(BBD).The lipase production was optimized(26.04 IU/ml)under the ideal molasses(2.5%),peptone(2%),and salt(0.1%CaCO_(3),0.1%(NH_(4))2SO_(4),and 0.1%MgSO_(4).7H_(2)O).Both models revealed impeccable predictions;however,more interestingly,it was evaluated that ANN outperforms the RSM regarding data fitting and estimation capabilities.展开更多
文摘In this present studyα-amylase producing bacteria were isolated from the soil near North Bengal University canteen on an amylase agar medium.Primary screening of the isolates was done by iodine test,based on the clear zone around the bacterial colonies in the media plates.Seven bacterial isolates were selected and quantitatively screened for amylase production.Among them,strain sps2 showing the highest amylase activity(215±2 IU/ml)was selected for further studies.Isolated strain sps2 was identified both morphologically and biochemically and further confirmed by 16s rRNA sequencing as Streptomyces pratensis sps2(Gene bank accession No.OP 236721).Optimization ofα-amylase produced by the isolate was conducted under submerged fermentation of low-cost carbon source banana peel,using a one factor at a time(OFAT)approach.Maximum amylase activity of 821.33±0.57 IU/ml was recorded after 2 days of fermentation.The artificial neural network(ANN)model was employed using the OFAT data to generate a predictive model for amylase production and the R-value of 0.94496 and the RMSE of 64.82 suggesting that the neural network has the precise capability to generate an optimization environment.Ammonium sulfate precipitation and dialysis techniques were used to partially purify theα-amylase and then characterized.The enzyme was found to have a molecular mass of 28 kDa(SDS-PAGE and zymogram)with a K_(m)and V_(max)of 2 mg/ml and 1000μmol/min,respectively.The enzyme was found to be thermostable in the temperature range of 4-90℃ and more stable at alkaline pH with optimum pH of 7.
文摘Response surface methodology(RSM)and artificial neural networks(ANN)are considered the most efficient way for opti-mization and modeling studies to design and develop various biosimilars.The primary objective of this study was to create empirical modeling and optimization of media parameters for producing B.halotolerans VSH 09 lipase using RSM and ANN.One-factor-at-a-time(OFAT)analysis revealed that triacylglycerols hydrolyzed by lipase manifest substantial activity.The subsequent screening for best carbon,nitrogen,and inducer was performed using the Placket-Burman design(PBD).The statistically significant variables were further examined for their optimum level using Box-Behnken design(BBD).The lipase production was optimized(26.04 IU/ml)under the ideal molasses(2.5%),peptone(2%),and salt(0.1%CaCO_(3),0.1%(NH_(4))2SO_(4),and 0.1%MgSO_(4).7H_(2)O).Both models revealed impeccable predictions;however,more interestingly,it was evaluated that ANN outperforms the RSM regarding data fitting and estimation capabilities.
