the multivariate calibration method, partial least square regression (pls) was applied for thesimultaneous spectrophotometric determination of lidocaine (lid) and hydrocortisone acetate(hca) in their mixtures. the parameters of chemometric technique were optimized and theproposed method was validated with synthetic samples and applied to analyze these drugs inpharmaceutical products with good a...

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 ...

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 detecto...

Multivariate calibration is a classic problem in the analytical chemistry field and frequently solved by partial least squares method in the previous work. Unfortunately there are so many redundant features in that problem, that feature selection are often performed before modeling by partial least squares method and the features not selected are usually discarded. In this paper, the redundant ...

Two new approaches to multivariate calibration are described that, for the first time, allow information on measurement uncertainties to be included in the calibration process in a statistically meaningful way. The new methods, referred to as maximum likelihood principal components regression (MLPCR) and maximum likelihood latent root regression (MLLRR), are based on principles of maximum likel...

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...