We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smo...

Two multivariate calibration methods are compared for the simultaneous chromatographic determination and separation of Sulfamethoxazole (SMX) and Phthalazine (PHZ) by High Performance Liquid Chromatography (HPLC). Multivariate calibration techniques such as Classical Least Squares (CLS) and Inverse Least Squares (ILS) were introduced into HPLC to determine the quantification by using UV d...

in this paper, multivariate calibration methods of partial least squares (pls) and principle component regression (pcr) were investigated in simultaneously determination of hydrazine (hz) and acetylhydrazine (ahz). the aforementioned methods are based on formation of the silver nanoparticles (agnps) from the reaction of hz and ahz with ag+ ions as oxidizing agent in the presence of citrate ions...

Resolution of binary mixtures of theophylline (THEO), montelukast (MKST) and loratadine (LORA) with minimum sample pretreatment and without analyte separation has been successfully achieved by multivariate spectrophotometric calibration, together with partial least-squares (PLS-1), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV–Vis ...

a new method was developed for the spectral resolution by further determination of three- and  four-component  mixtures  of  drugs in urine samples through  the complementary application of multivariate curve resolution-alternating least squares with correlation  constraint. in the current study, a simple method was proposed to construct a calibration set for the mixture of drugs in the presenc...

In this paper we propose a robust probabilistic multivariate calibration (RPMC) model in an attempt to identify linear relationships between two sets of observed variables contaminated with outliers. Instead of the Gaussian assumptions that predominate in classical statistical models, RPMC is closely related with the multivariate Student's t-distribution over noises and latent variables. As a r...