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Batch injection analysis towards auxiliary diagnosis of periodontal diseases based on indirect amperometric detection of salivary α-amylase on a cupric oxide electrode.

Authors :
Garcia PT
Dias AA
Souza JAC
Coltro WKT
Source :
Analytica chimica acta [Anal Chim Acta] 2018 Dec 24; Vol. 1041, pp. 50-57. Date of Electronic Publication: 2018 Aug 29.
Publication Year :
2018

Abstract

This study describes, for the first time, the use of a batch injection analysis system with amperometric detection (BIA-AD) to indirectly determine salivary α-amylase (sAA) levels in saliva samples for chronic periodontitis diagnosis. A chemical/thermal treatment was explored to generate a CuO film on a Cu electrode surface. This procedure offered good stability (RSD = 0.3%), good repeatability (RSD < 1.3%) and excellent reproducibility (RSD < 1.5%). The sAA concentration levels were determined based on the detection of maltose produced by enzymatic hydrolysis of starch. The analytical performance was investigated, and a linear correlation was observed for a maltose concentration range between 0.5 and 6.0 mmol L <superscript>-1</superscript> with a correlation coefficient equal to 0.999. The analytical sensitivity and the limit of detection were 48.8 μA/(mmol L <superscript>-1</superscript> ) and 0.05 mmol L <superscript>-1</superscript> , respectively. In addition, the proposed system provided an excellent analytical frequency (120 analysis h <superscript>-1</superscript> ). The clinical feasibility of the proposed method was investigated by the determination of sAA levels in four saliva samples (two from healthy control persons (C1 and C2) and two from patients with chronic periodontitis (P1 and P2)). The accuracy provided by the BIA-AD system ranged from 93 to 98%. The sAA concentration levels achieved for each sample were compared to the values found by spectrophotometry and there was no statistically significant difference between them at a confidence level of 95%. Finally, the method reported herein emerges as a simple, low cost and promising tool for assisting periodontal diseases diagnosis.<br /> (Copyright © 2018 Elsevier B.V. All rights reserved.)

Details

Language :
English
ISSN :
1873-4324
Volume :
1041
Database :
MEDLINE
Journal :
Analytica chimica acta
Publication Type :
Academic Journal
Accession number :
30340690
Full Text :
https://doi.org/10.1016/j.aca.2018.08.039