title: A new and consistent parameter for measuring the quality of multivariate analytical methods: Generalized analytical sensitivity.
abstract: Generalized analytical sensitivity (γ) is proposed as a new figure of merit, which can be estimated from a multivariate calibration data set. It can be confidently applied to compare different calibration methodologies, and helps to solve literature inconsistencies on the relationship between classical sensitivity and prediction error. In contrast to the classical plain sensitivity, γ incorporates the noise properties in its definition, and its inverse is well correlated with root mean square errors of prediction in the presence of general noise structures. The proposal is supported by studying simulated and experimental first-order multivariate calibration systems with various models, namely multiple linear regression, principal component regression (PCR) and maximum likelihood PCR (MLPCR). The simulations included instrumental noise of different types: independently and identically distributed (iid), correlated (pink) and proportional noise, while the experimental data carried noise which is clearly non-iid.
authors: Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Instituto de Química de Rosario (IQUIR-CONICET), Suipacha 531, Rosario S2002LRK, Argentina; Universidade Federal da Paraíba (UFPB), Centro de Ciências Exatas e da Natureza, Departamento de Química, Castelo Branco, João Pessoa, PB, Brazil.
authors: Wallace
authors: W
authors: Fragoso
authors: Universidade Federal da Paraíba (UFPB), Centro de Ciências Exatas e da Natureza, Departamento de Química, Castelo Branco, João Pessoa, PB, Brazil.
authors: Franco
authors: F
authors: Allegrini
authors: Universidade Federal da Paraíba (UFPB), Centro de Ciências Exatas e da Natureza, Departamento de Química, Castelo Branco, João Pessoa, PB, Brazil. Electronic address: olivieri@iquir-conicet.gov.ar.
authors: Alejandro C
authors: AC
authors: Olivieri
keywords: Analytical figures of merit; Analytical sensitivity; Method comparison; Multivariate calibration; Sensitivity