In calibration experiments, a number of samples of known concentration are used to establish the relationship between a measured response and sample concentration; this relationship is then used to estimate the unknown concentration of further samples from their measured responses. In addition to the estimates themselves, it is useful to have available some measure of their precision, usually g...

One of the essential factors influencing the prediction accuracy of multivariate calibration models is the quality of the calibration data. A local regression strategy, together with a wavelength selection approach, is proposed to build the multivariate calibration models based on partial least squares regression. The local algorithm is applied to create a calibration set of spectra similar to ...

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...

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...

The efficacy of smoothing first-order data as a preprocessing method for multivariate calibration is discussed. In particular, the use of symmetric smoothing filters (such as Savitzky–Golay filters) is examined from the perspective of calibration performance, in contrast with past studies based on univariate signal-to-noise improvement. It is shown mathematically that in the limit of a perfect ...

The potential of Near Infrared Reflectance Spectroscopy (NIRS) as a fast method to predict the Crude Protein (CP) and Moisture (M) content in fishmeal by scanning spectra between 1000 and 2500 nm using multivariate regression technique based on Partial Least Squares (PLS) was evaluated. The coefficient of determination in calibration (R2C) and Standard Error of Calibra...