Introduction to

Multivariate Analysis

Prof. Dr. Anselmo E de Oliveira

anselmo.quimica.ufg.br

anselmo.disciplinas@gmail.com

Apresentação

• Análise Multivariada (Quimiometria I)

• Aulas Teóricas e Práticas: Sala 203 – Práticas: PC e notebook

• Horário: 3as e 5as, 14:00 às 15:40 h

• Minha sala: 209, IQ-1

• Softwares – Matlab, Octave, Planilhas

eletrônicas, R,...

• E-mail – Assunto: [Q1] assunto – anselmo.disciplinas@gmail.com

• Página do curso anselmo.quimica.ufg.br Quimiometria 1

– Material didático

– Plano de Ensino • Ementa

• Dias sem aula

• Datas das avaliações

• Bibliografia

Apresentação

Prova

• 02/07

• Sala de aula

• 14:00 às 16:00 h

• Consulta

• Computador

• Enviar o resultado por e-mail

Trabalho

• Escrita de um texto acadêmico e científico sobre a aplicação do conteúdo do curso

• Contido no trabalho de pós-graduação ou em artigo científico publicado a partir de 2010 (QUALIS A1, A2, B1, B2 e B3)

• Entrega no último dia de aula (30/07)

Apresentação

• Quem são vocês?

– Nome

– Orientador

– Projeto em desenvolvimento

– Formação

• O que vocês esperam do curso?

Chemometrics

Chemometrics is not a single tool but a range of methods including – Basic Statistics, Signal Processing, Factorial Design, Calibration, Curve Fitting, Factor Analysis, Detection, Pattern Recognition and Neural Networks.

Fonte: http://www.decisioncraft.com/dmdirect/chemometrics.htm

Chemometrics

Fonte: http://www.decisioncraft.com/dmdirect/chemometrics.htm

Chemometrics

Exploratory data analysis can reveal hidden patterns in complex data by reducing the information to a more comprehensible form. Such a chemometric analysis can expose possible outliers and indicate whether there are patterns or trends in the data. Exploratory algorithms such as principal component analysis (PCA) are designed to reduce large complex data sets into a series of optimized and interpretable size.

Fonte: http://www.decisioncraft.com/dmdirect/chemometrics.htm

Chemometrics

In many applications, it is expensive, time consuming or difficult to directly measure a variable of interest. Such cases require the analyst to predict something of interest based on related properties that are easier to measure. The goal of chemometric regression analysis is to develop a model which correlates the information in the set of known measurements to the desired property. Chemometric algorithms for performing regression include partial least squares (PLS) and principal component regression (PCR). Chemometric regression is extensively used in making decisions relating to product quality in the on-line monitoring and process control industry where fast and expensive systems are needed to test.

Fonte: http://www.decisioncraft.com/dmdirect/chemometrics.htm

Chemometrics

Many applications require that samples be assigned to predefined categories. This may involve determining whether a sample is good or bad, or predicting an unknown sample as belonging to one of several distinct groups. A classification model is used to predict a sample's class by comparing the sample to a previously analyzed experience set, in which categories are already known. k-nearest neighbour (KNN) is primary used in Chemometrics. This can be thought as separating chromatorgraphic data set from spectroscopic data set and doing analysis. When these techniques are used to create a classification model, the answers provided are more reliable and include the ability to reveal unusual samples in the data. Therefore, Chemometrics helps in standardizing data.

Fonte: http://www.decisioncraft.com/dmdirect/chemometrics.htm

The Analytical Process

Tools: Exploratory data analysis

Data mining

Calibration/resolution

Information/control theory

optimization

Experimental design

Sampling theory

Luck

Information: chemical concentrations...

Measurements: voltages, currents, volumes...

Samples

System

Knowledge of properties of system

Fonte: M.A. Sharaf; D.L. Illman; B.R. Kowalski, Chemical Analysis: Chemometrics

Dados Multivariados

• Automação nas análises grande quantidade de dados

– Métodos cromatográficos e espectroscópicos

• Um analito vários analitos

• Muitas variáveis são medidas

Dados Multivariados

Univariado x Multivariado

Univariado

• Teores de umidade de um produto ao longo do mês

• Horário de chegada de um funcionário

• Curva de calibração: sinal x concentração de um analito

Multivariado

• Todos os dados do controle de qualidade referente a um produto ao longo do mês

• Todas as informações relativas à produtividade de um funcionário

• Calibração Multivariada: espectro x concentração de um analito

Univariado x Multivariado

V1

V2

V1

V2

V1

V3 covariância

Pattern Recognition

• Inteligência Artificial

• Reconhecimento de padrões em 2D e 3D: nós x computador?

• Como avaliar nossa habilidade de reconhecer padrões em grandes tabelas de números com muitas amostras e muitas medidas?

Pattern Recognition

• Handwritten: http://www.wired.com/wiredscience/2009/08/emghandwriting/

Pattern Recognition

• Printed alphanumeric characters

Pattern Recognition

• Speech recognition – http://www.oddcast.com/home/demos/tts/tts_example.php?sitepal

Pattern Recognition

• Speker recognition – http://cs.joensuu.fi/pages/tkinnu/research/index.html

Pattern Recognition

• Fingerprint identification

Pattern Recognition

• Radars: http://www.emagsys.com/patternRecognition.html

An airborne image of an A-3 flight prior to automated motion compensation, image centering, and overlay fitting (a) and the image after automated processing (b)

Pattern Recognition

• Electrocardiogram: http://www.ivline.info/2010/05/quick-guide-to-ecg.html

Pattern Recognition

• Weather Forecasting: http://www.cptec.inpe.br/cidades/tempo/230

Pattern Recognition

• Stock Market Analysis: http://www.marketoracle.co.uk/Article29762.html

Pattern Recognition

• Preprocessing techniques are designed to transform the data into the most informative representation in the context of the goal study

Pattern Recognition

• Unsupervised learning refers to methods that make no a priori assumptions about cathegory-membership of the samples, but rather assist the analyst in unconvering intrinsic clusters or other patterns in the data

• In supervised learning the computer “learns” to optimally classify the samples based on advance knowledge about their category membership.

Pattern Recognition

clustering