On problematic practice of using normalization in Self-modeling/Multivariate Curve Resolution (S/MCR)

The paper is briefly dealing with greater or lesser misused normalization in self-modeling/multivariate curve resolution (S/MCR) practice. The importance of the correct use of the ode solvers and apt kinetic illustrations are elucidated. The new terms, external and internal normalizations are defined and interpreted. The problem of reducibility of a matrix is touched. Improper generalization/development of normalization-based methods are cited as examples. The position of the extreme values of the signal contribution function is clarified. An Executable Notebook with Matlab Live Editor was created for algorithmic explanations and depictions

Multilineáris (többutas) kalibrációs eljárások alkalmazása és fejlesztése élelmiszer- és környezettudományi mérések kiértékeléséhez = Using and developing multilinear (multi-way) calibration procedures to evaluate measurements of food and environmental sciences

A projekt során HPLC?FT-IR mérések kvalitatív és kvantitatív kiértékeléséhez OSSS-IU-PARAFAC algoritmus kifejlesztését; a görbeillesztés nélküli komponensprofil-kinyerés módszer implementálását és továbbfejlesztését; meloxikám-mannit binér rendszer kioldódási tulajdonságai kapcsán mulitilineáris módszerek alkalmazhatóságának vizsgálatát; meloxikám kioldódódási tulajdonságának javításához különböző gyógyszer-technológiák vizsgálatához XRD mérési eredmények kiértékelését; kalibrációs módszerek általános vizsgálatát (ezen belül PLS regressziós módszerhez objektív háttérváltozó meghatározó módszer fejlesztését); McReynolds polaritási skála vizsgálatához PCR és PLS felhasználásával változószelekciót és modellezést; városi ózonkoncentráció változás vizsgálata kapcsán PCA, PCR és PLS alkalmazásával osztályozást és modellezést; híg vizes oldatok Raman spektrumának háttérkorrekcióját; EEM-TSFS spektrumok korrigálást és vizsgálatát; valamint élelmiszerekben történő hőterjedés kapcsán modellépítést és paraméter-meghatározást végeztem el, ill. valósítottam meg. | In this project OSSS-IU-PARAFAC algorithm was developed for evaluating HPLC-FT-IR measurements. Self modeling curve resolution (SMCR) method was implemented as Matlab script and improved. Applicability of multilinear chemometric methods used for exploitation of dissolution properties of meloxicam-mannitol binary systems was investigated. Chemometric evaluation of XRD measurements to study different pharmaceutical technologies for improving the dissolution of meloxicam was done. General review of calibration methods including the objective determination of latent variables for PLS modeling was accomplished. McReynolds polarity scale was studied by variable selection and model building methods using PCR and PLS. Prediction of ozone concentration in ambient air using multivariate methods (PCA, PCR and PLS) was achieved. Background correction/elimination of Raman spectra of thin water solutions was developed. Rayleigh scattering correction and transforming of EEM and TSFS spectra were completed. As well as modelling heat penetration curves in thermal processes and determination of thermal parameters were realized

Investigation of Multivariate Statistical Process Control in R Enviroment

At the first stage of our work, the theoretical knowledge needed to use the multivariate statistical process control (MSPC) was explored. Last year, we clarified the sometimes confused concepts, equations, and formulas [1]. At the se­cond stage, R project simulation studies and some food industrial practical model investigations are carried out for con­firming the MSPC advantages compared with the univariate ones. Furthermore, we analyse, using principal component analysis (PCA), what could cause the outlying values. Moreover, we will demonstrate how to use the MYT-decomposition

Classification of Hungarian medieval silver coins using x-ray fluorescent spectroscopy and multivariate data analysis

A set of silver coins from the collection of Déri Museum Debrecen (Hungary) was examined by X-ray fluorescent elemental analysis with the aim to assign the coins to different groups with the best possible precision based on the acquired chemical information and to build models, which arrange the coins according to their historical periods. Results: Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, classification and regression trees and multivariate curve resolution with alternating least squares were applied to reveal dominant pattern in the data and classify the coins into several groups. We also identified those chemical components, which are present in small percentages, but are useful for the classification of the coins. With the coins divided into two groups according to adequate historical periods, we have obtained a correct classification (76-78%) based on the chemical compositions. Conclusions: X-ray fluorescent elemental analysis together with multivariate data analysis methods is suitable to group medieval coins according to historical periods. Keywords: X-ray fluorescence spectroscopy, Multivariate techniques, Coin, Silver, Middle age