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Purification, Characterization, and Biochemical Properties of $\alpha$-Amylase from Potato
Sarker, Goutam Kumar,Hasan, Sohel,Nikkon, Farjana,Mosaddik, Ashik,Sana, Niranjan Kumar,Rahman, Habibur,Park, Sang-Gyu,Lee, Dong-Sun,Cho, So-Mi Kim The Korean Society for Applied Biological Chemistr 2010 Applied Biological Chemistry (Appl Biol Chem) Vol.53 No.1
Purification, characterization and biochemical properties of $\alpha$-amylase from post harvest Bangladeshi Potato (Solanum tuberosum L.) were investigated. The $\alpha$-amylase was purified by successive chromatography on DEAE and CM-cellulose columns with a yield of 24.24%. SDSPAGE showed a molecular weight of 44 kDa for the enzyme that contain 2.8% sugar. The enzyme lost total activity in the presence of the chelating agent EDTA, confirming it was an $\alpha$-type amylase. The enzyme displayed optimum activity at pH 7.2 and $37^{\circ}C$, with an apparent Km value of 0.26% using starch as its substrate. The enzyme was strongly inhibited by $Cu^{2+},\;Fe^{2+}$ and $Zn^{2+}$; moderately by $Li^+,\;Hg^+$ and $Cd^{2+}$; and slightly by $Ag^+,\;K^+,\;Mn^{2+}$ and $Mg^{2+}$. Conversely, $Fe%{3+}$ and $Na^+$ appreciably enhanced activity, while adding calcium ion nearly doubled enzyme activity. In addition, the activity of $\alpha$-amylase gradually decreased with increasing concentrations of urea. Thus, potato $\alpha$-amylase is an attractive target for study to better understand the structure-function relationships of $\alpha$-amylases.
Purification, Characterization, and Biochemical Properties of α-Amylase from Potato
( Goutam Kumar Sarker ),( Sohel Hasan ),( Farjana Nikkon ),( Ashik Mosaddik ),( Niranjan Kumar Sana ),( Habibur Rahman ),( Sang Gyu Park ),( Dong Sun Lee ),( Somi Kim Cho ) 한국응용생명화학회 2010 Applied Biological Chemistry (Appl Biol Chem) Vol.53 No.1
Purification, characterization and biochemical properties of α-amylase from post harvest Bangladeshi Potato (Solanum tuberosum L.) were investigated. The α-amylase was purified by successive chromatography on DEAE and CM-cellulose columns with a yield of 24.24%. SDS-PAGE showed a molecular weight of 44 kDa for the enzyme that contain 2.8% sugar. The enzyme lost total activity in the presence of the chelating agent EDTA, confirming it was an α-type amylase. The enzyme displayed optimum activity at pH 7.2 and 37˚C, with an apparent Km value of 0.26% using starch as its substrate. The enzyme was strongly inhibited by Cu2+, Fe2+ and Zn2+; moderately by Li+, Hg+ and Cd2+; and slightly by Ag+, K+, Mn2+ and Mg2+. Conversely, Fe3+ and Na+ appreciably enhanced activity, while adding calcium ion nearly doubled enzyme activity. In addition, the activity of α-amylase gradually decreased with increasing concentrations of urea. Thus, potato α-amylase is an attractive target for study to better understand the structure-function relationships of α-amylases.
Biochemistry : Purification, Characterization, and Biochemical Properties of α-Amylase from Potato
Goutam Kumar Sarker,Sohel Hasan,Farjana Nikkon,Ashik Mosaddik,Niranjan Kumar Sana,Habibur Rahman,Sang Gyu Park,Dong Sun Lee,So Mi Kim Cho 한국응용생명화학회 2010 Journal of Applied Biological Chemistry (J. Appl. Vol.53 No.1
( Sumana Kundu ),( Goutam Sarker ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.5
A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual superclassifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.
Kundu, Sumana,Sarker, Goutam Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.5
A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual super-classifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.