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Multivariate Analysis
Prof. Dr. Anselmo E de Oliveira
anselmo.quimica.ufg.br
anselmo.disciplinas@gmail.com
Introduction
Apresentação
• Análise Multivariada (Quimiometria 1)
• Aulas Teóricas e Práticas – Salas 304 e 104
• Horário – 5as, 14:00 às 17:50 h
• Minha sala: 209, IQ-1
• Softwares – Matlab, Octave, Planilhas
eletrônicas, R,...
– Subject: [Q1] ...
– anselmo.disciplinas@gmail.com
• Página do curso
– anselmo.quimica.ufg.br Quimiometria 1
– Material didático
– Plano de Ensino
• Ementa
• Datas das avaliações
• Bibliografia
Apresentação
Avaliação: 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 2011 (QUALIS A1... B4)
• Entrega no último dia de aula (26/11)
Apresentação
• Quem são vocês?
– Nome
– 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
Statistics
Food Feed
Chemistry
Pharmacy
Engineering
Computing
Exploratory Data Analysis
• Exploratory data analysis can reveal hidden patterns in complex data by reducing the information to a more comprehensible form
Exploratory Data Analysis
• It can expose possible outliers and indicate whether there are patterns or trends in the data.
Exploratory Data Analysis
• Exploratory algorithms such as principal component analysis (PCA) are designed to reduce large complex data sets into a series of optimized and interpretable size.
Regression Analysis
• 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
Godinho et al. Talanta 2014, 129, 143
Regression Analysis
• 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.
Classification Model • It is used to predict a sample's class by comparing the sample
to a previously analyzed experience set, in which categories are already known
Hyperspectral Imaging and Chemometric Modeling of Echinacea — A Novel Approach in the Quality Control of Herbal Medicines Sandassi et al. Molecules 2014, 19(9), 13104-13121
Classification Model • k-nearest neighbour (k-NN) is primary used in Chemometrics.
– This can be thought as separating chromatographic data set from spectroscopic data set and doing analysis.
Classification Model • 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.
The Analytical Process
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
Challenges with Data Analytics
Aggregating data from multiple sources
Cleaning data
Choosing a model
Moving to production
Multivariate Data
automated analysis large amount of data
chromatography and spectroscopy methods
one sample/analyte at a time many samples/analytes
many variables are measured on each run
Multivariate Data
Univariate x Multivariate Data
Univariate data
• Control of product humidity during a month
• Lunch time of an employee
• Calibration curve:
peak intensity x analyte concentration
Multivariate data
• Datasheet including all quality control data
• Historical information regarding employees’ productivity
• Multivariate calibration: spectrum x analyte concentration
Univariate x Multivariate Data
V1
V2
V1
V2
V1
V3 covariance
Pattern Recognition
• Artificial Inteligence
• 2D and 3D pattern recognition:
– human being x computer?
– Pattern recongnition on measurement data tables/sheets containing lots of numbers and a huge amount of sample information.
Pattern Recognition
• Handwritten
Pattern Recognition
• Printed alphanumeric characters
Pattern Recognition
• Speech recognition
Pattern Recognition
• Speker recognition
Pattern Recognition
• Fingerprint identification
Pattern Recognition
• Radars
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
Pattern Recognition
• Weather Forecasting
Pattern Recognition
• Stock Market
Pattern Recognition
• 217 males • diagnosed with bipolar disorder, major
depressive disorder, schizophrenia and other psychiatric issues.
• about 20 percent went from no suicidal thoughts to a high level of suicidal thoughts while they were being seen at a clinic at the university.
• blood samples • RNA biomarkers that appeared to predict
suicidal thinking. • it's unclear how well the biomarkers would
work in the larger population due to the fact that the study was limited to high-risk males with psychiatric diagnoses, but that the app is ready to be deployed and tested on a wider group in real-world settings such as emergency rooms.
Pattern Recognition
• Preprocessing techniques are designed to transform the data into the most informative representation in the context of the goal study
Espectros FT-NIR de 268 amostras de óleos minerais: (A) sem pré-tratamento; (B) após correção de linha base; (C) após correção de espalhamento
múltiplo; (D) após alisamento Savitzky–Golay .
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
Pattern Recognition
• In supervised/machine learning the computer “learns” to optimally classify the samples based on advance knowledge about their category membership.
Pattern Recognition
clustering
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