Precision of prediction in second‐order calibration, with focus on bilinear regression methods

Abstract

We consider calibration of hyphenated instruments with particular focus on determination of the unknown concentrations of new specimens. A hyphenated instrument generates for each specimen a two-way array of data. These are assumed to depend on the concentrations through a bilinear regression model, where each constituent is characterized by a pair of profiles to be determined in the calibration. We discuss the problem of predicting the unknown concentrations in a new specimen, after calibration. We formulate three different predictor construction methods, a`naive' method, a least squares method, and a refined version of the latter that takes account of the calibration uncertainty. We give formulae for the uncertainty of the predictors under white noise, when calibration can be seen as precise. We refine these formulae to allow for calibration uncertainty, in particular when calibration is carried out by the bilinear least squares (BLLS) method or the singular value decomposition (SVD) method proposed by Linder and Sundberg (Chemometrics Intell. Lab. Syst. 1998; 42: 159±178). By error propagation formulae and previous results on the precision of bA and b B we can obtain approximate standard errors for the predicted concentrations, according to each of the two estimation methods. The performance of the predictors and the precision formulae is illustrated on both real (fluorescence) and simulated data.

Second- and higher-order data generation and calibration: A tutorial

“…Other less employed algorithms in this context 232 are generalized rank annihilation (GRAM) [22], direct trilinear decomposition (DTLD) 233 [23] and bilinear least-squares (BLLS) [24], either because the use single calibration 234…”

Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report)

“…This terminology reflects whether, in the absence of noise, a pure-component data matrix has mathematical rank unity (complexity-one) or higher (mixed complexity). However, we will use the term "bilinear" here, because, in our opinion, it does not lead to confusion and also because it has been adopted in other authoritative texts [43][44][45]. Second-order bilinear calibration methods are of considerable interest since many instruments produce data that, ideally, follow the bilinear model [46][47][48].…”

Determination of Daunomycin in Human Plasma and Urine by Using an Interference-free Analysis of Excitation-Emission Matrix Fluorescence Data with Second-Order Calibration

“…Bilinear least squares (BLLS) is a recently introduced technique, based on a direct least-squares procedure. 21,22 The third main group is an iterative one. 12,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] Iterative algorithms have been widely employed.…”