Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection

被引:28
作者
De Benedetto, Giuseppe Egidio [1 ]
Di Masi, Sabrina [2 ]
Pennetta, Antonio [1 ]
Malitesta, Cosimino [2 ]
机构
[1] Univ Salento, Dipartimento Beni Culturali, Via D Birago 64, I-73100 Lecce, Italy
[2] Dipartimento Sci & Tecnol Biol & Ambientali, Via Monteroni 1, I-73100 Lecce, Italy
来源
BIOSENSORS-BASEL | 2019年 / 9卷 / 01期
关键词
biosensors; enzyme inhibition; metal ions; central composite design; response surface methodology; INHIBITION-BASED BIOSENSORS; FACTORIAL DESIGN; ENZYME BIOSENSORS; GLUCOSE; IONS; TECHNOLOGIES; WATER;
D O I
10.3390/bios9010026
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Herein, we report the application of a chemometric tool for the optimisation of electrochemical biosensor performances. The experimental design was performed based on the responses of an amperometric biosensor developed for metal ions detection using the flow injection analysis. The electrode preparation and the working conditions were selected as experimental parameters, and thus, were modelled by a response surface methodology (RSM). In particular, enzyme concentration, flow rates, and number of cycles were reported as continuous factors, while the sensitivities of the biosensor (S, mu A center dot mM(-1)) towards metals, such as Bi3+ and Al3+ were collected as responses and optimised by a central composite design (CCD). Bi3+ and Al3+ inhibition on the Pt/PPD/GOx biosensor response is for the first time reported. The optimal enzyme concentration, scan cycles and flow rate were found to be 50 U center dot mL(-1), 30 and, 0.3 mL center dot min(-1), respectively. Descriptive/predictive performances are discussed: the sensitivities of the optimised biosensor agreed with the experimental design prediction. The responses under the optimised conditions were also tested towards Ni2+ and Ag+ ions. The multivariate approach used in this work allowed us to obtain a wide working range for the biosensor, coupled with a high reproducibility of the response (RSD = 0.72%).
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页数:11
相关论文
共 34 条
[11]   Inhibitive determination of metal ions by an amperometric glucose oxidase biosensor - Study of the effect of hydrogen peroxide decomposition [J].
Guascito, Maria Rachele ;
Malitesta, Cosimino ;
Mazzotta, Elisabetta ;
Turco, Antonio .
SENSORS AND ACTUATORS B-CHEMICAL, 2008, 131 (02) :394-402
[12]   Screen-Printed Glucose Oxidase-Based Biosensor for Inhibitive Detection of Heavy Metal Ions in a Flow Injection System [J].
Guascito, Maria Rachele ;
Malitesta, Cosimino ;
Mazzotta, Elisabetta ;
Turco, Antonio .
SENSOR LETTERS, 2009, 7 (02) :153-159
[13]   Optimization of a bacterial bioluminescent biosensor through experimental design [J].
Horry, Habib ;
Maul, Armand ;
Thouand, Gerald .
SENSORS AND ACTUATORS B-CHEMICAL, 2007, 127 (02) :649-657
[14]   Molecules and elements for quantitative bioanalysis: The allure of using electrospray, MALDI, and ICP mass spectrometry side-by-side [J].
Linscheid, Michael W. .
MASS SPECTROMETRY REVIEWS, 2019, 38 (02) :169-186
[15]   GLUCOSE FAST-RESPONSE AMPEROMETRIC SENSOR BASED ON GLUCOSE-OXIDASE IMMOBILIZED IN AN ELECTROPOLYMERIZED POLY(ORTHO-PHENYLENEDIAMINE) FILM [J].
MALITESTA, C ;
PALMISANO, F ;
TORSI, L ;
ZAMBONIN, PG .
ANALYTICAL CHEMISTRY, 1990, 62 (24) :2735-2740
[16]   Heavy metal determination by biosensors based on enzyme immobilised by electropolymerisation [J].
Malitesta, C ;
Guascito, MR .
BIOSENSORS & BIOELECTRONICS, 2005, 20 (08) :1643-1647
[17]   Progress in the biosensing techniques for trace-level heavy metals [J].
Mehta, Jyotsana ;
Bhardwaj, Sanjeev K. ;
Bhardwaj, Neha ;
Paul, A. K. ;
Kumar, Pawan ;
Kim, Ki-Hyun ;
Deep, Akash .
BIOTECHNOLOGY ADVANCES, 2016, 34 (01) :47-60
[18]   Optimization of a glucose biosensor setup based on a Ni/Al HT matrix [J].
Mignani, A. ;
Luciano, G. ;
Lanteri, S. ;
Leardi, R. ;
Scavetta, E. ;
Tonelli, D. .
ANALYTICA CHIMICA ACTA, 2007, 599 (01) :36-40
[19]   Fast detection of cyclopiazonic acid in cheese using Fourier transform mid-infrared ATR spectroscopy [J].
Monaci, Linda ;
Vatinno, Rosa ;
De Benedetto, Giuseppe E. .
EUROPEAN FOOD RESEARCH AND TECHNOLOGY, 2007, 225 (3-4) :585-588
[20]  
Mugheri AQ., 2016, SENSOR LETT, V14, P1178, DOI [10.1166/sl.2016.3752, DOI 10.1166/SL.2016.3752]