Detection limit estimator for multivariate calibration by an extension of the IUPAC recommendations for univariate methods

Abstract

A methodology is proposed to estimate the limit of detection (LOD) of analytical methods when multivariate calibration is applied. It tries to follow the same premises as the IUPAC methodology for univariate calibration. The mathematical support is given and algorithms such as partial least squares (PLS) regression, PLS2 and principal component regression (PCR) are used. Only multivariate raw data are used; that is, no surrogate univariate signal is deduced. Non-linearities are allowed. Near infrared (NIR) data of 5 component pseudo-gasoline samples together with simulated fluorescence synchronous spectra of binary mixtures (first order data) are used for evaluation. Experimental verification is performed using different kinds of data, namely: binary mixtures of bentazone and fenamiphos (very overlapped spectra, second order data) obtained by sequential injection (SI), and kinetic data of the reaction between the Fenton's reagent (FR) and pesticides such as atrazine, bentazone and alachlor (individual or binary mixtures, second order data). Results are always compared with independent methods previously proposed in the literature, based in the use of surrogate univariate signals. In general, similar results are found and no statistically significant differences seem to be present, except in a few cases when complex chemical systems are involved.

The multivariate detection limit for Mycoplasma pneumoniae as determined by nanorod array-surface enhanced Raman spectroscopy and comparison with limit of detection by qPCR

“…48 However, several groups have published protocols for estimating a detection limit for multivariate data based on an extension of International Union of Pure and Applied Chemistry (IUPAC) recommendations for univariate calibration. 49, 50 We have adapted one of these methods 50 to calculate a multivariate detection limit (MDL) for serial dilutions of M. pneumoniae as analyzed by NA-SERS. This approach relies on the spectral residuals and a calculated regression vector from a multivariate regression model while taking account of type I (false positive) and type II (false negative) errors in the following manner.…”

“…The values of the t α , v and t β , v coefficients were chosen to correspond to IUPAC recommended criteria in which the probabilities of either a type I or type II error are approximately 7%. 50 …”

Évaluation des effets d’une lampe à cathode creuse pulsée à courant variable sur les interférences spectrales de l’arsenic dans le dosage du cadmium par spectrophotométrie d’absorption atomiqueArticle envoyé à la Revue du génie et de la science de l'environnement.

“…l'écart type déterminé à partir de 10 mesures d'une solution d'eau ultra-pure caractérisée par une conductivité supérieure à 18 MUÁcm -1 et k, la pente de la droite Abs = f(C) (Mocak et al 1997;Tokalioglu et al 2000;Janiga et al 2008;Ostra et al 2008). Les résultats montrent, d'une part, que quel que soit le mode d'atomisation utilisé, les LD sont indépen-dantes du mode de correction sélectionné et, d'autre part, que le four électrothermique est environ 70 fois plus sensible que la flamme pour le dosage de Cd en solution aqueuse (tableau 2).…”

Estimation of multivariate detection limits of four quality parameters in licorice using MEMS–NIR spectrometry coupled with two sampling accessories

“…The International Standards Organization (ISO) has recommended a LOD methodology to deal with analytical signal, which is a speci¯c univariate. 9,10 However, the ISO did not provide standard multivariate detection limits (MDL) method. NIR signals were complex and required multivariate calibration models.…”