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RECENT TRENDS IN QUALITY ASSURANCE TECHNIQUES CHEMOMETRICS

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. CONTENTS INTRODUCTION VARIOUS TECHNIQUES AREA OF APPLICATION SPECTROSCOPISTS’ REQUIREMENTS FOR CHEMOMETRICS PROCESS ANALYSIS MULTIVARIATE DATA ANALYSIS

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RECENT TRENDS IN QUALITY ASSURANCE TECHNIQUES CHEMOMETRICS

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  1. RECENT TRENDS IN QUALITY ASSURANCE TECHNIQUESCHEMOMETRICS MS.SHEVANTE T.B M.PHARM(QAT) V.I.P.E.R GUIDED BY-DR.MR.GADHAVE M.V V.I.P.E.R

  2. CONTENTS INTRODUCTION VARIOUS TECHNIQUES AREA OF APPLICATION SPECTROSCOPISTS’ REQUIREMENTS FOR CHEMOMETRICS PROCESS ANALYSIS MULTIVARIATE DATA ANALYSIS ADVANTAGES OF MULTIVARIANT DATA ANALYSIS PROCESS ANALYTICAL TECHNOLOGY (PAT) AND QUALITY BY DESIGN (QBD) USES OF MULTIVARIATE ANALYSIS METHODS CONCLUSION REFERENCES V.I.P.E.R

  3. INTRODUCTION • Introduced by SvanteWold [4] and Bruce R. Kowalski in the early 1970s • ‘‘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 . • Chemometricians have applied the well-known approaches of multivariate calibration, chemical resolution, and pattern recognition for analytical studies. • Chemometrics 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

  4. WHITE, BLACK, AND GRAY SYSTEMS • Mixture samples commonly encountered in analytical chemistry fall into three categories, which are known collectively as the white--gray—black multi-component system. V.I.P.E.R

  5. Various techniques • Partial Least Squares (PLS) , • Soft Independent Modeling of Class Analogy (SIMCA) • Methods Based on Factor Analysis, • Principal-component Regression (PCR) • Target Factor Analysis (TFA) • Evolving Factor Analysis (EFA) • Rank Annihilation Factor Analysis (RAFA) • Window Factor Analysis (WFA) • Heuristic Evolving Latent Projection (HELP) • Artificial Neural Network. • Multiplicative scatter Correction. V.I.P.E.R

  6. PARTIAL LEAST SQUARES REGRESSION (PLS REGRESSION) • It is a statistical method that bears some relation to principalcomponent 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) • It is a statistical method for supervised classification of data. • PRINCIPAL COMPONENT REGRESSION (PCR) • It is a regression analysis technique that is based on Principal component analysis(PCA). • Principal 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 • It 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

  7. RANK ANNIHILATION FACTOR ANALYSIS (RAFA) • It 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) • It 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) • It is new method to resolve two way bilinear multi component data into spectra & chromatograms of pure constituents. • EVOLVING FACTOR ANALYSIS (EFA) • 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

  8. Mathematics Mathematics Organic Organic Chemistry Chemistry Statistics Statistics Biology Biology Analytical Analytical Computing Computing Applications Applications Industrial Industrial CHEMOMETRICS CHEMOMETRICS Chemistry Chemistry among others among others p Theoretical Theoretical Pharmaceuticals Engineering Engineering and Physical and Physical Chemistry Chemistry

  9. AREA OF APPLICATION • Spectroscopy & analyzing spectroscopic data • Demonstrates the basic principles underlying the use of common experimental, chemometric, and statistical tools. • Emphasis has been given to problem-solving applications and the proper use and interpretation of data used for scientific research. • Useful for analysts in their daily problem solving, as well as detailed insights into subjects often considered difficult to thoroughly grasp by non-specialists. • Provides mathematical proofs and derivations for the student or rigorously-minded specialist. • Multivariate analysis. V.I.P.E.R

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