title: Does chemometrics enhance the performance of electroanalysis?
abstract: This review explores the question whether chemometrics methods enhance the performance of electroanalytical methods. Electroanalysis has long benefited from the well-established techniques such as potentiometric titrations, polarography and voltammetry, and the more novel ones such as electronic tongues and noses, which have enlarged the scope of applications. The electroanalytical methods have been improved with the application of chemometrics for simultaneous quantitative prediction of analytes or qualitative resolution of complex overlapping responses. Typical methods include partial least squares (PLS), artificial neural networks (ANNs), and multiple curve resolution methods (MCR-ALS, N-PLS and PARAFAC). This review aims to provide the practising analyst with a broad guide to electroanalytical applications supported by chemometrics. In this context, after a general consideration of the use of a number of electroanalytical techniques with the aid of chemometrics methods, several overviews follow with each one focusing on an important field of application such as food, pharmaceuticals, pesticides and the environment. The growth of chemometrics in conjunction with electronic tongue and nose sensors is highlighted, and this is followed by an overview of the use of chemometrics for the resolution of complicated profiles for qualitative identification of analytes, especially with the use of the MCR-ALS methodology. Finally, the performance of electroanalytical methods is compared with that of some spectrophotometric procedures on the basis of figures-of-merit. This showed that electroanalytical methods can perform as well as the spectrophotometric ones. PLS-1 appears to be the method of practical choice if the %relative prediction error of approximately +/-10% is acceptable.
authors.affiliation: State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, Jiangxi 330047, China. ynni@ncu.edu.cn
authors.forename: Yongnian
authors.initials: Y
authors.lastname: Ni
authors.forename: Serge
authors.initials: S
authors.lastname: Kokot
mesh_terms: D000465:Algorithms; D003217:Conductometry; D004350:Drug Residues / Q000032:analysis*; D004563:Electrochemistry; D004785:Environmental Pollutants / Q000032:analysis*; D005504:Food Analysis; D010573:Pesticide Residues / Q000032:analysis*; D011048:Polarography; D011199:Potentiometry
chemical_list: D004785:Environmental Pollutants; D010573:Pesticide Residues