RECENT TRENDS IN QUALITY ASSURANCE TECHNIQUES CHEMOMETRICS MS
![INTRODUCTION ØIntroduced by Svante Wold [4] and Bruce R. Kowalski in the early 1970](https://slidetodoc.com/presentation_image_h2/93bc4038136a34881bda151180684927/image-3.jpg "INTRODUCTION ØIntroduced by Svante Wold [4] and Bruce R. Kowalski in the early 1970")
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RECENT TRENDS IN QUALITY ASSURANCE TECHNIQUES CHEMOMETRICS MS. SHEVANTE T. B M. PHARM(QAT) V. I. P. E. R GUIDED BY-DR. MR. GADHAVE M. V V. I. P. E. R 1
CONTENTS 1. INTRODUCTION 2. VARIOUS TECHNIQUES 3. AREA OF APPLICATION 4. SPECTROSCOPISTS’ REQUIREMENTS FOR CHEMOMETRICS 5. PROCESS ANALYSIS 6. MULTIVARIATE DATA ANALYSIS 7. ADVANTAGES OF MULTIVARIANT DATA ANALYSIS 8. PROCESS ANALYTICAL TECHNOLOGY (PAT) AND QUALITY BY DESIGN (QBD) 9. USES OF MULTIVARIATE ANALYSIS METHODS 10. CONCLUSION 11. REFERENCES V. I. P. E. R 2
INTRODUCTION ØIntroduced by Svante Wold [4] and Bruce R. Kowalski in the early 1970 s Ø‘‘A reasonable definition of chemometrics remains as how do we get chemical relevant information out of measured chemical data, how do we represent and display this information, and how do we get such information into data? ’’ as mentioned by Wold. ØChemometricians have applied the well-known approaches of multivariate calibration, chemical resolution, and pattern recognition for analytical studies. ØChemometrics is the use of mathematical and statistical methods to improve the understanding of chemical information and to correlate quality parameters or physical properties to analytical instrument data. V. I. P. E. R 3
WHITE, BLACK, AND GRAY SYSTEMS Ø Mixture samples commonly encountered in analytical chemistry fall into three categories, which are known collectively as the white--gray—black multicomponent system. SYSTEM DESCRIPTION White All spectra of the component chemical species in the sample, as well as the impurities are available. Black no a priori information regarding the chemical composition Grey No complete knowledge is available for the chemical composition or spectral information V. I. P. E. R 4
Various techniques ØPartial Least Squares (PLS) , ØSoft Independent Modeling of Class Analogy (SIMCA) Ø Methods Based on Factor Analysis, ØPrincipal-component Regression (PCR) Ø Target Factor Analysis (TFA) Ø Evolving Factor Analysis (EFA) ØRank Annihilation Factor Analysis (RAFA) Ø Window Factor Analysis (WFA) ØHeuristic Evolving Latent Projection (HELP) ØArtificial Neural Network. ØMultiplicative scatter Correction. V. I. P. E. R 5
PARTIAL LEAST SQUARES REGRESSION (PLS REGRESSION) ØIt is a statistical method that bears some relation to principal component regression instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. SOFT INDEPENDENT MODELLING BY CLASS ANALOGY (SIMCA) ØIt is a statistical method for supervised classification of data. PRINCIPAL COMPONENT REGRESSION (PCR) ØIt is a regression analysis technique that is based on Principal component analysis(PCA). ØPrincipal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linerly uncorrelated variables called principal components. FACTOR ANALYSIS ØIt is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. V. I. P. E. R 6
RANK ANNIHILATION FACTOR ANALYSIS (RAFA) ØIt is used to analyze difference spectra of kinetic-spectrophotometric data. Annihilation of the contribution of one chemical component from the original data matrix is a general method in RAFA WINDOW FACTOR ANALYSIS (WFA) ØIt is a self-modeling method for extracting the concentration profiles of individual components from evolutionary processes such as flow injection, chromatography, titrations and reaction kinetics. HEURISTIC EVOLVING LATENT PROJECTION(HELP) ØIt is new method to resolve two way bilinear multi component data into spectra & chromatograms of pure constituents. EVOLVING FACTOR ANALYSIS (EFA) Ø It is a recently developed method for a completely model-free resolution of overlapping peaks into concentration profiles and absorption spectra V. I. P. E. R 7
M ath em ati cs Sta nic stry a g i Or em h C tis tic s Computing CHEMOMETRICS g n eri En e gin Analytical Chemistry T an heor Ch d Ph etica em ysi l ist cal ry y log o i B Industrial Applications among others p. Ph a rm ac eu tic als
AREA OF APPLICATION Spectroscopy & analyzing spectroscopic data ØDemonstrates the basic principles underlying the use of common experimental, chemometric, and statistical tools. Ø Emphasis has been given to problem-solving applications and the proper use and interpretation of data used for scientific research. ØUseful for analysts in their daily problem solving, as well as detailed insights into subjects often considered difficult to thoroughly grasp by non-specialists. Ø Provides mathematical proofs and derivations for the student or rigorouslyminded specialist. ØMultivariate analysis. V. I. P. E. R 9
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