BASICS OF CHEMOMETRICS
|
|
- Janis Atkinson
- 6 years ago
- Views:
Transcription
1 INTRODUCTION - Chemometrics Introduction What is this and why we need it BASICS OF CHEMOMETRICS Juan Antonio Fernández Pierna Vincent Baeten Pierre Daenne - Some definitions - Overview of methods - Eamples Walloon Agricultural Research Centre (CRA-W) Valorisation of Agricultural Products Department Gemblou, Belgium 2 X - METRICS CHEMOMETRICS INTRODUCTION The use of multivariate analysis in the discipline X: Statistical, mathematical or graphical technique, considers multiple variables simultaneously Chemometrics is the chemical discipline that uses mathematics and statistics to design or select optimal eperimental procedures, to provide maimum relevant chemical information by analyzing chemical data, and to obtain knowledge about chemical systems - Biometrics (used in biology) - Technometrics (used in engineering) - Psychrometrics (used in phychology) - Chemometrics (used in chemistry) 3 D. L. Massart 4
2 CHEMOMETRICS INTRODUCTION CHEMOMETRICS INTRODUCTION Mathematics Computing Engineering Statistics Theoretical and physical chemistry Biology Analytical chemistry Organic chemistry Industrial Food Feed The scientific world today The data flood generated by modern analytical instrumentation produces large quantity of numbers to understand and quantify phenomenons around us. The evolution of personal computers allows faster acquisition, processing and interpretation of chemical data. Every scientist uses software related to mathematical methods or to processing of knowledge. Meeting point of various disciplines Chemometrics 5 A deeper understanding of those methods and tools for viewing all data simultaneously are needed. 6 CHEMOMETRICS INTRODUCTION CHEMOMETRICS INTRODUCTION Use of mathematical and statistical methods for selecting optimal eperiments Statistical eperimental design Design of Eperiments (DoE) CHEMOMETRICS S Analytical request Analytical method Analytical answer Etracting maimum amount of information when analysing multivariate (chemical) data Classification Process monitoring, Multivariate calibration Useful at any point in an analysis, from the first conception of an eperiment until the data is discaed. 7 8
3 Wavelength Wavelength Wavelength CHEMOMETRICS APPLICATIONS CHEMOMETRICS DEFINITIONS Huge growth area in past 5 years Process Control and analysis Food and feed analysis Biology metabolomics etc Environmental monitoring Analytical Chemistry Linear algebra is the language of Chemometrics. One cannot epect to truly understand most chemometric techniques without a basic understanding of linear algebra (Wise and Gallagher, 998) Matri and vector operations 9 CHEMOMETRICS DEFINITIONS CHEMOMETRICS DEFINITIONS - Samples are referred to as OBJECTS - Measurement results (e.g. concentration, absorbances, ) are referred to as VARIABLES -A data table of K variables and M objects is referred to as a DATA MATRIX OF SIZE MK M objects - observations X K variables CHEMOMETRICS: Etract meaningful information about the objects and the variables from data matrices é ê ê 2 X = ê... ê êë n n2 Variables... pù... ú 2 pú ú ú... npú û Samples If X has 3 rows and 5 columns 3 5 data matri Absorbance Absorbance Absorbance 2
4 CHEMOMETRICS INTRODUCTION CHEMOMETRICS IMPORTANT DISCIPLINES In summary M objects X K variables = é ê ê 2 ê... ê êë n n pù ú 2 pú... ú ú npúû Sampling, selection of objects and variables Clustering Multivariate regressions, calibrations and predictions Neural Networ Validation Graphical display and outlier detection 3 4 Source: Chemometrics Introduction - Jens C. Frisvad Calibration data set (X) Sample Selection SAMPLE SELECTION Data Eploration Pattern Recognition Visualization Unsupervised analysis Principal Component Analysis (PCA) Data matri pre-treatment Supervised analysis Regression models Model building Model construction Calibration INCLUDE IN THE CALIBRATION SET ALL THE FACTORS OF VARIATION: Measurement factors: - room temperature - operator - instrument setup clustering tendency outlier detection Outliers Validation New data Prediction Selection optimal compleity Uncertainty estimation 5 Population sources of variation: - origins of samples - processes - varieties - storage conditions - sample preparations (t, particle size) - residual moisture 6
5 PATTERN RECOGNITION PRINCIPAL COMPONENT ANALYSIS Groupingofobjectse.g.how similaristhebehaviourofcompounds, how similar are products PCA PrincipalComponentAnalysis PCA isachemometrictechniqueforvisualizing high dimensionaldata.pca reducesthedimensionality(the numberofvariables)ofadatasetbymaintainingasmuch varianceaspossible PC Lookingatrelationships: Betweensamples:food samples / patients / people / spectra / PC 2 (3.42%) PC2 Betweenvariables:elementalcompositions/compound concentrations / spectral pea / PC2.6 PC PC (96.9%) PRINCIPAL COMPONENT ANALYSIS CALIBRATION.2 Samples/Scores Plot of Xoffset Quantitativeorqualitativeestimation.Especialymitures. Scores on PC 3 (.34%) Clusters Outliers Univariatecalibration:onemeasuremente.g.apeakhight Multivariatecalibration:severalmeasurementse.g.spectra Statistical,mathematicalorgraphicaltechnique,considers multiplevariablessimultaneously Scores on PC (96.9%) Object space 9 2
6 MULTIVARIATE CALIBRATION MULTIVARIATE CALIBRATION Relateinstrumentalresponsetochemicalconcentration. Impropercalibrationyieldsimproperresults. Linear,non-linearandmultivariatecalibrationalgorithms areavailable,dependingontheinstrumentandtheanalysis involved. Linking two sets of data together : peak hight to concentration /Spectra to concentrations /biological activitytostructure/ 2 Calibration X Data spectra Y Data + Referenceues Calibration model -Moisture(regression) -Concentrationofprotein(regression) -countryoforigin(discrimination) -PDO (discrimination) - -Infraredspectroscopy(IR-Raman) -Fourier-Transform Infraredspectroscopy(FTIR) -NuclearMagneticResonance(NMR) -X-RayFluorescence(XRF) -X-RayDiffraction(XRD)spectroscopy - 22 MULTIVARIATE CALIBRATION - VALIDATION MULTIVARIATE CALIBRATION Calibration X Data spectra + Y Data Refue Calibration model Y=f(X)=XB+E Validation X Data spectra + Calibration model Y Data /refue. Y is a matrinm containingthe reference ues X is a matrinp containingthe spectra (NIR,MIR, Raman, ) B is a matripm containingthe regression coeficients Calibration uses empiricaldata and prior knowledge for determininghow to predict unknown quantitative information yfrom available measurements,via some mathematicaltransfer functions 23 E is a matrinm eplainingfor the modelerror. 24
7 Unsupervised Principal Component Analysis (PCA) Cluster Analysis (CA) Multiple Linear Regression (MLR) Unsupervised - Pattern recognition Multivariate analysis Regression Principal Component Regression (PCR) Partial Least Squares (PLS) ArtificialNeural Networ (ANN) Unsupervised Principal Component Analysis (PCA) Cluster Analysis (CA) Multiple Linear Regression (MLR) Principal Component Regression (PCR) Supervised LS Support Vector Machines (LS-SVM) Local techniques Multivariateanalysis Regression Partial Least Squares (PLS) Artificial Neural Networ (ANN) LS Support Vector Machines (LS-SVM) Discrimination PLS-Discriminant Analysis (PLS-DA) SIMCA Supervised Local techniques à See similarities and regularities present in the data. PLS-Discriminant Analysis (PLS-DA) Discrimination SIMCA K-Nearest Neighbours (k-nn) K-Nearest Neighbours (k-nn) 25 Support Vector Machines (SVM) 26 Algorithms Support VectorMachines (SVM) Supervised - Regression Supervised - Discrimination Unsupervised Principal Component Analysis (PCA) Cluster Analysis (CA) Multiple Linear Regression (MLR) Principal Component Regression (PCR) Principal Component Analysis (PCA) à Regression Unsupervised based statisticaltechnique used in determining which Cluster Analysis particular classification or Multiple Linear Regression (MLR) (CA) group an item of data or an object belongs to on the basis of its characteristics or essentialfeatures. Principal Component Regression (PCR) Partial Least Squares (PLS) Partial Least Squares (PLS) Multivariateanalysis Regression Artificial Neural Networ (ANN) Multivariateanalysis Regression Artificial Neural Networ (ANN) LS Support Vector Machines (LS-SVM) LS Support Vector Machines (LS-SVM) Supervised Local techniques Supervised Local techniques PLS-Discriminant Analysis (PLS-DA) PLS-Discriminant Analysis (PLS-DA) à It includes any Discrimination techniques for modeling SIMCA and analyzing several variables,when the focus is on the K-Nearest relationship Neighbours (k-nn) between a dependent variable (property of interest) and one or more independent Support Vector Machines (SVM) variables (spectra). 27 Discrimination SIMCA K-Nearest Neighbours (k-nn) Support Vector Machines (SVM) 28
8 VARIABLE SELECTION VARIABLE SELECTION Find a setofpredictorvariableswhich givesagoodfit, predicts the dependent ue wel and is as smal as possible. Backwaelimination:Startwithalthepredictorsand potentialydroppredictorsinsubsequentsteps. Forwaselection:Startwithnopredictorsandpotentialy addpredictorsinsubsequentsteps. Stepwiseregression:Combinationofbackwa eliminationandforwaselection VARIABLE SELECTION CHEMOMETRICS SOFTWARE Reducingnumberofvariables Possibilityofconstructingfastspectrometersbasedonareduced number of variables aiming to a reduction of ing and utilizationtimestobeused,forinstance,inaconveyerbelt. Moreover,thefactofgroupingthedifferentselectedvariablesin regionsalowsaneasychemicalinterpretationofthespectra. References - Backwavariableselectionin PLS (BVSPLS) ;J.A.Fernández Pierna,V.Baeten,P.Daenne.AnalyticaChimicaActa642(29) pp Prediction errorimprovementsusing variableselection on smal calibration sets - a comparison of some recentmethods.s.i. Overgaa,J.A.Fernández Pierna,V.Baeten,P.Daenne & T. Isason.J.NearInfraredSpectrosc.2, (22). 3 32
9 EXAMPLE: FEED EXAMPLE: FEED Other FARIMAL Vegetal Other Poultry Bovine + Pig Terrestrialanimal Fish Belonging to. CAL LOOCV % classified as. % classified as. Fish Rest Fish Rest Fish Rest %correct classification EXAMPLE: CEREALS - IMPURITIES EXAMPLE: NUTS AND DRIED FRUITS Walnut Hazelnut Brazil nut Almond Cashew Raisin SVM discrimination models NIR hyperspectral imaging spectroscopy and chemometrics for the detection of undesirable substances in food and feed J.A. Fernández Pierna, Ph. Vermeulen, O. Amand, A. Tossens, P. Daenne and V. Baeten. Special issue Chemometrics and Intelligent Laboratory Systems 7 (22)
10 EXAMPLE: OLIVES - IMPURITIES EXAMPLE: OLIVE OIL QUALITY PARAMETERS KNN - PCA models Usinga visible vision system for on-line determination of qualityparameters of olive fruits. E. Guzmán, V.Baeten,J.A.FernándezPierna,J.A.García Mesa.Foofand Nutrition Sciences,4,9-98(23) Application of low-resolution Raman spectroscopyfor the analysis of oidized olive oil E. Guzmán, V.Baeten,J.A.FernándezPierna,J.A.García Mesa.(2) Food control 22, EXAMPLE: OLIVE OIL ADULTERATION EXAMPLE: SUGAR BEET PLANTS CYST optical microscopy NIR hyperspectral imaging SVM discrimination models Detection ofthe Presence ofhazelnut Oil in Olive Oil by FT-Raman and FT-MIR Spectroscopy'V.Baeten, J.A.FernándezPierna,P.Daenne,M.Meurens,D.L.García González,R.Aparicio.Journal of Agricultural and Food Chemistry,53 (6) (25), NIRhyperspectral imagingspectroscopyand chemometrics for the detection of undesirable substances in food and feed J.A. Fernández Pierna, Ph. Vermeulen, O. Amand, A. Tossens, P. Daenne and V. Baeten. Special issue Chemometrics and Intelligent Laboratory Systems 7 (22)
11 EXAMPLE: SUGAR BEET PLANTS CERCOSPORA EXAMPLE: FEED PRODUCTS Multivariate regression method comparison: PLS, ANN and LS-SVM 4 42 EXAMPLE: FEED PRODUCTS EXAMPLE: FEED PRODUCTS Feed (286767) Ash, Fat, Fibre, Starch, Protein Feed Ingredients (266527) Ash, Fat, Fibre, Protein Fresh Silages (357) Dry Matter, Fibre, Protein Soils (6257) CEC, COT_SK, N_Kj Pre-processing: SNV + detrend + First derivative 43 Eample of data distribution (Soils N_Kj) 44
12 EXAMPLE: FEED PRODUCTS EXAMPLE: FEED PRODUCTS Feed - Fat Feed - fibre Feed - ash Feed ingredients - ash Feed ingredients - fat 2.5 Feed - starch Feed - protein Feed ingredients - fibre Feed ingredients - protein EXAMPLE: FEED PRODUCTS EXAMPLE: TRANSFERT CALIBRATION TRANSFER FROM DISPERSIVE INSTRUMENTS TO HANDHELD SPECTROMETERS (MEMS) Fresh silages - fibre Fresh silages - protein Fresh silages - dry matter Calibration Transfer from Dispersive Instruments to Handheld Spectrometers, J.A. Fernández Pierna, P. Vermeulen, B. Lecler, V. Baeten, P. Daenne. Applied Spectroscopy 64 (6) (2) 48
13 EXAMPLE: TRANSFERT QUESTIONS ABOUT CHEMOMETRICS? j.f cra.walonie.be http: /
NIR applications for emerging vegetal contaminants
NIR applications for emerging vegetal contaminants Dr Juan Antonio Fernández Pierna Dr Pascal Veys Philippe Vermeulen Dr Vincent Baeten Dr Pierre Dardenne NIR platform Gembloux - 27 March 2013 Walloon
More informationFood authenticity and adulteration testing using trace elemental and inorganic mass spectrometric techniques
Food authenticity and adulteration testing using trace elemental and inorganic mass spectrometric techniques Michal Godula, Ph.D. Thermo Fisher Scientific The world leader in serving science Overview Food
More informationOptimizing Model Development and Validation Procedures of Partial Least Squares for Spectral Based Prediction of Soil Properties
Optimizing Model Development and Validation Procedures of Partial Least Squares for Spectral Based Prediction of Soil Properties Soil Spectroscopy Extracting chemical and physical attributes from spectral
More informationAnalysis of heterogeneity distribution in powder blends using hyperspectral imaging
Analysis of heterogeneity distribution in powder blends using hyperspectral imaging Dr Juan Antonio Fernández Dr Vincent Baeten Guanghui Shen Dr Anna de Juan Rodrigo R. de Oliveira Walloon Agricultural
More informationElimination of variability sources in NIR spectra for the determination of fat content in olive fruits
Ref: C59 Elimination of variability sources in NIR spectra for the determination of fat content in olive fruits Natalia Hernández-Sánchez, Physical Properties Laboratory-Advanced Technologies In Agri-
More informationNIR Spectroscopy Identification of Persimmon Varieties Based on PCA-SVM
NIR Spectroscopy Identification of Persimmon Varieties Based on PCA-SVM Shujuan Zhang, Dengfei Jie, and Haihong Zhang zsujuan@263.net Abstract. In order to achieve non-destructive measurement of varieties
More informationMULTIVARIATE PATTERN RECOGNITION FOR CHEMOMETRICS. Richard Brereton
MULTIVARIATE PATTERN RECOGNITION FOR CHEMOMETRICS Richard Brereton r.g.brereton@bris.ac.uk Pattern Recognition Book Chemometrics for Pattern Recognition, Wiley, 2009 Pattern Recognition Pattern Recognition
More informationBasics of Multivariate Modelling and Data Analysis
Basics of Multivariate Modelling and Data Analysis Kurt-Erik Häggblom 2. Overview of multivariate techniques 2.1 Different approaches to multivariate data analysis 2.2 Classification of multivariate techniques
More informationFTIR characterization of Mexican honey and its adulteration with sugar syrups by using chemometric methods
Journal of Physics: Conference Series FTIR characterization of Mexican honey and its with sugar syrups by using chemometric methods To cite this article: M A Rios-Corripio et al 11 J. Phys.: Conf. Ser.
More informationApplication of Raman Spectroscopy for Detection of Aflatoxins and Fumonisins in Ground Maize Samples
Application of Raman Spectroscopy for Detection of Aflatoxins and Fumonisins in Ground Maize Samples Kyung-Min Lee and Timothy J. Herrman Office of the Texas State Chemist, Texas A&M AgriLife Research
More informationTechniques and Applications of Hyperspectral Image Analysis
Techniques and Applications of Hyperspectral Image Analysis Hans F. Grahn and Paul Geladi. B I CENTENNIAL 1 8 0 7 t; *WI LEY. 2007 _. r B I CENTENNIAL John Wiley & Sons, Ltd Contents Preface List of Contributors
More informationAdvancing from unsupervised, single variable-based to supervised, multivariate-based methods: A challenge for qualitative analysis
Advancing from unsupervised, single variable-based to supervised, multivariate-based methods: A challenge for qualitative analysis Bernhard Lendl, Bo Karlberg This article reviews and describes the open
More informationAnalysis of Cocoa Butter Using the SpectraStar 2400 NIR Spectrometer
Application Note: F04 Analysis of Cocoa Butter Using the SpectraStar 2400 NIR Spectrometer Introduction Near-infrared (NIR) technology has been used in the food, feed, and agriculture industries for over
More informationCourse in Data Science
Course in Data Science About the Course: In this course you will get an introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst. The course gives an
More informationChemometrics: Classification of spectra
Chemometrics: Classification of spectra Vladimir Bochko Jarmo Alander University of Vaasa November 1, 2010 Vladimir Bochko Chemometrics: Classification 1/36 Contents Terminology Introduction Big picture
More informationNIRS Getting started. US Dairy Forage Research Center 2004 Consortium Annual Meeting
NIRS Getting started US Dairy Forage Research Center 2004 Consortium Annual Meeting Terminology NIR = Near Infrared or NIR = Near Infrared Reflectance NIT = Near Infrared Transmittance History 1800 The
More informationMultivariate calibration
Multivariate calibration What is calibration? Problems with traditional calibration - selectivity - precision 35 - diagnosis Multivariate calibration - many signals - multivariate space How to do it? observed
More informationReducedPCR/PLSRmodelsbysubspaceprojections
ReducedPCR/PLSRmodelsbysubspaceprojections Rolf Ergon Telemark University College P.O.Box 2, N-9 Porsgrunn, Norway e-mail: rolf.ergon@hit.no Published in Chemometrics and Intelligent Laboratory Systems
More informationBASICS OF INFRARED AND RAMAN SPECTROSCOPY
BASICS OF INFRARED AND RAMAN SPECTROSCOPY 2 nd RAFA workshop on : Infrared spectroscopy, Raman spectroscopy and chemometrics for monitoring of food and feed products, lab-tothe-sample Vincent Baeten, Juan
More informationMethod Transfer across Multiple MicroNIR Spectrometers for Raw Material Identification
Method Transfer across Multiple MicroNIR Spectrometers for Raw Material Identification Raw material identification or verification (of the packaging label) is a common quality-control practice. In the
More informationABASTRACT. Determination of nitrate content and ascorbic acid in intact pineapple by Vis-NIR spectroscopy Student. Thesis
Thesis Determination of nitrate content and ascorbic acid in intact pineapple by Vis-NIR spectroscopy Student Mrs. Sasathorn Srivichien Student ID 52680101 Degree Doctor of Philosophy Program Food Science
More informationRapid Compositional Analysis of Feedstocks Using Near-Infrared Spectroscopy and Chemometrics
Rapid Compositional Analysis of Feedstocks Using Near-Infrared Spectroscopy and Chemometrics Ed Wolfrum December 8 th, 2010 National Renewable Energy Laboratory A U.S. Department of Energy National Laboratory
More informationUsing radial basis elements for classification of food products by spectrophotometric data
Using radial basis elements for classification of food products by spectrophotometric data Stanislav Penchev A Training Model of a Microprogramming Unit for Operation Control: The paper describes an approach
More informationDetection and Quantification of Adulteration in Honey through Near Infrared Spectroscopy
Proceedings of the 2014 International Conference of Food Properties (ICFP 2014) Kuala Lumpur, Malaysia, January 24-26, 2014 Detection and Quantification of Adulteration in Honey through Near Infrared Spectroscopy
More informationExploring Raman spectral data using chemometrics
Laboratoire de Spectrochimie Infrarouge et Raman Exploring Raman spectral data using chemometrics Ludovic Duponchel Laboratoire de Spectrochimie Infrarouge et Raman UMR CNRS 8516 Université Lille I Villeneuve
More informationPrincipal component analysis, PCA
CHEM-E3205 Bioprocess Optimization and Simulation Principal component analysis, PCA Tero Eerikäinen Room D416d tero.eerikainen@aalto.fi Data Process or system measurements New information from the gathered
More informationQUALITY AND DRYING BEHAVIOUR OF ORGANIC FRUIT PRODUCTS
Prof. Riccardo Massantini Department for Innovation in Biological, Agro food and Forest systems (DIBAF), University of Tuscia, Viterbo (Italy) massanti@unitus.it OUR RESEARCH GROUP AND COMPETENCES RICCARDO
More informationApplication of Raman Spectroscopy for Noninvasive Detection of Target Compounds. Kyung-Min Lee
Application of Raman Spectroscopy for Noninvasive Detection of Target Compounds Kyung-Min Lee Office of the Texas State Chemist, Texas AgriLife Research January 24, 2012 OTSC Seminar OFFICE OF THE TEXAS
More informationMilk Analysis with the TIDAS P Milk Inspector
Milk Analysis with the TIDAS P Milk Inspector In Cooperation with the German Schwarzwald Milch GmbH a milk analysis system was developed, which is able to measure the percentage of fat, protein and dry
More informationCross model validation and multiple testing in latent variable models
Cross model validation and multiple testing in latent variable models Frank Westad GE Healthcare Oslo, Norway 2nd European User Meeting on Multivariate Analysis Como, June 22, 2006 Outline Introduction
More informationChemometrics. 1. Find an important subset of the original variables.
Chemistry 311 2003-01-13 1 Chemometrics Chemometrics: Mathematical, statistical, graphical or symbolic methods to improve the understanding of chemical information. or The science of relating measurements
More informationPutting Near-Infrared Spectroscopy (NIR) in the spotlight. 13. May 2006
Putting Near-Infrared Spectroscopy (NIR) in the spotlight 13. May 2006 0 Outline What is NIR good for? A bit of history and basic theory Applications in Pharmaceutical industry Development Quantitative
More informationNear Infrared reflectance spectroscopy (NIRS) Dr A T Adesogan Department of Animal Sciences University of Florida
Near Infrared reflectance spectroscopy (NIRS) Dr A T Adesogan Department of Animal Sciences University of Florida Benefits of NIRS Accurate Rapid Automatic Non-destructive No reagents required Suitable
More informationExplaining Correlations by Plotting Orthogonal Contrasts
Explaining Correlations by Plotting Orthogonal Contrasts Øyvind Langsrud MATFORSK, Norwegian Food Research Institute. www.matforsk.no/ola/ To appear in The American Statistician www.amstat.org/publications/tas/
More informationChemometrics The secrets behind multivariate methods in a nutshell!
Chemometrics The secrets behind multivariate methods in a nutshell! "Statistics means never having to say you're certain." We will use spectroscopy as the analytical method to explain the most commonly
More informationWater in Food 3 rd International Workshop. Water Analysis of Food Products with at-line and on-line FT-NIR Spectroscopy. Dr.
Water in Food 3 rd International Workshop Water Analysis of Food Products with at-line and on-line FT-NIR Spectroscopy Dr. Andreas Niemöller Lausanne 29th 30th March 2004 Bruker Group Company Technology
More informationprofileanalysis Innovation with Integrity Quickly pinpointing and identifying potential biomarkers in Proteomics and Metabolomics research
profileanalysis Quickly pinpointing and identifying potential biomarkers in Proteomics and Metabolomics research Innovation with Integrity Omics Research Biomarker Discovery Made Easy by ProfileAnalysis
More informationSpectroscopy in Transmission
Spectroscopy in Transmission + Reflectance UV/VIS - NIR Absorption spectra of solids and liquids can be measured with the desktop spectrograph Lambda 9. Extinctions up to in a wavelength range from UV
More informationDISCRIMINATION AND CLASSIFICATION IN NIR SPECTROSCOPY. 1 Dept. Chemistry, University of Rome La Sapienza, Rome, Italy
DISCRIMINATION AND CLASSIFICATION IN NIR SPECTROSCOPY Federico Marini Dept. Chemistry, University of Rome La Sapienza, Rome, Italy Classification To find a criterion to assign an object (sample) to one
More informationPRECISION AGRICULTURE APPLICATIONS OF AN ON-THE-GO SOIL REFLECTANCE SENSOR ABSTRACT
PRECISION AGRICULTURE APPLICATIONS OF AN ON-THE-GO SOIL REFLECTANCE SENSOR C.D. Christy, P. Drummond, E. Lund Veris Technologies Salina, Kansas ABSTRACT This work demonstrates the utility of an on-the-go
More informationThe Role of a Center of Excellence in Developing PAT Applications
The Role of a Center of Excellence in Developing PAT Applications! Pittcon 2005 Orlando FL, March 2 nd 2005! Nils-Erik Andersson AstraZeneca R&D, Mölndal Sweden 1 Outline! Business needs and New ways of
More informationMultivariate Analysis
Prof. Dr. J. Franke All of Statistics 3.1 Multivariate Analysis High dimensional data X 1,..., X N, i.i.d. random vectors in R p. As a data matrix X: objects values of p features 1 X 11 X 12... X 1p 2.
More informationSmart Sensing Embedded Spectroscopy Platform Botlek studiegroep 06-april-2017
Smart Sensing Embedded Spectroscopy Platform Botlek studiegroep 06-april-2017 W. Karremans Personal introduction Background: Process Analysis DSM AKZO Nobel Chemicals Aspenpharma 2016: Sales Engineer Elscolab
More informationNEAR INFRARED SPECTROSCOPY IN DRUG DISCOVERY AND DEVELOPMENT PROCESSES
r Chapter NEAR INFRARED SPECTROSCOPY IN DRUG DISCOVERY AND DEVELOPMENT PROCESSES Yves Roggo, Klara Dégardin and Michel Ulmschneider Contents 9.1. INTRODUCTION... 433 9.2. NIRS AND IMAGING: THEORY AND INSTRUMENTATION...
More informationDiscrimination of Dyed Cotton Fibers Based on UVvisible Microspectrophotometry and Multivariate Statistical Analysis
Discrimination of Dyed Cotton Fibers Based on UVvisible Microspectrophotometry and Multivariate Statistical Analysis Elisa Liszewski, Cheryl Szkudlarek and John Goodpaster Department of Chemistry and Chemical
More informationMULTIVARIATE STATISTICAL ANALYSIS OF SPECTROSCOPIC DATA. Haisheng Lin, Ognjen Marjanovic, Barry Lennox
MULTIVARIATE STATISTICAL ANALYSIS OF SPECTROSCOPIC DATA Haisheng Lin, Ognjen Marjanovic, Barry Lennox Control Systems Centre, School of Electrical and Electronic Engineering, University of Manchester Abstract:
More informationIssues and Techniques in Pattern Classification
Issues and Techniques in Pattern Classification Carlotta Domeniconi www.ise.gmu.edu/~carlotta Machine Learning Given a collection of data, a machine learner eplains the underlying process that generated
More informationSTATISTICS 407 METHODS OF MULTIVARIATE ANALYSIS TOPICS
STATISTICS 407 METHODS OF MULTIVARIATE ANALYSIS TOPICS Principal Component Analysis (PCA): Reduce the, summarize the sources of variation in the data, transform the data into a new data set where the variables
More informationApplication of mathematical, statistical, graphical or symbolic methods to maximize chemical information.
Application of mathematical, statistical, graphical or symbolic methods to maximize chemical information. -However, this definition can be expanded to include: biology (biometrics), environmental science
More informationMixed Hierarchical Models for the Process Environment
Mixed Hierarchical Models for the Process Environment Barry M. Wise, Robert T. Roginski, Neal B. Gallagher and Jeremy M. Shaver Eigenvector Research Inc., Wenatchee, WA, USA Abstract EffecFve monitoring
More informationChemometrics in method validation why?
Chemometrics in method validation why? 10th Jone Omar May 2016, Gent Eurachem 2016 Joint Research Centre the European Commission's in-house science service Early career scientist Who am I? PhD in analytical
More informationQuantum machine learning for quantum anomaly detection CQT AND SUTD, SINGAPORE ARXIV:
Quantum machine learning for quantum anomaly detection NANA LIU CQT AND SUTD, SINGAPORE ARXIV:1710.07405 TUESDAY 7 TH NOVEMBER 2017 QTML 2017, VERONA Types of anomaly detection algorithms Classification-based
More informationYat Yun Wei Food Safety Division Health Sciences Authority. All Rights Reserved Health Sciences Authority
Analytical Techniques in Food Authentication Yat Yun Wei Food Safety Division Health Sciences Authority 1 Outline of presentation What is Authentic Food? Why is there a need for reliable techniques for
More informationDiscrimination of Edible Oil Products and Quantitative Determination of Their Iodine Value by Fourier Transform Near-Infrared Spectroscopy
Discrimination of Edible Oil Products and Quantitative Determination of Their Iodine Value by Fourier Transform Near-Infrared Spectroscopy Hui Li a, F.R. van de Voort a, *, A.A. Ismail a, J. Sedman a,
More informationTechniques and Applications of Multivariate Analysis
Techniques and Applications of Multivariate Analysis Department of Statistics Professor Yong-Seok Choi E-mail: yschoi@pusan.ac.kr Home : yschoi.pusan.ac.kr Contents Multivariate Statistics (I) in Spring
More informationInferential Analysis with NIR and Chemometrics
Inferential Analysis with NIR and Chemometrics Santanu Talukdar Manager, Engineering Services Part 2 NIR Spectroscopic Data with Chemometrics A Tutorial Presentation Part 2 Page.2 References This tutorial
More informationFAST CROSS-VALIDATION IN ROBUST PCA
COMPSTAT 2004 Symposium c Physica-Verlag/Springer 2004 FAST CROSS-VALIDATION IN ROBUST PCA Sanne Engelen, Mia Hubert Key words: Cross-Validation, Robustness, fast algorithm COMPSTAT 2004 section: Partial
More informationPresentation of a new system to monitor and stabilize mid infrared spectral data
Presentation of a new system to monitor and stabilize mid infrared spectral data Clément Grelet, Vincent Baeten, Pierre Dardenne, Juan Antonio Fernandez Garcia, Ouissam Abbas, & Frédéric Dehareng Walloon
More informationPlum-tasting using near infra-red (NIR) technology**
Int. Agrophysics, 2002, 16, 83-89 INTERNATIONAL Agrophysics www.ipan.lublin.pl/int-agrophysics Plum-tasting using near infra-red (NIR) technology** N. Abu-Khalaf* and B.S. Bennedsen AgroTechnology, Department
More information2 D wavelet analysis , 487
Index 2 2 D wavelet analysis... 263, 487 A Absolute distance to the model... 452 Aligned Vectors... 446 All data are needed... 19, 32 Alternating conditional expectations (ACE)... 375 Alternative to block
More informationADAPTIVE LINEAR NEURON IN VISIBLE AND NEAR INFRARED SPECTROSCOPIC ANALYSIS: PREDICTIVE MODEL AND VARIABLE SELECTION
ADAPTIVE LINEAR NEURON IN VISIBLE AND NEAR INFRARED SPECTROSCOPIC ANALYSIS: PREDICTIVE MODEL AND VARIABLE SELECTION Kim Seng Chia Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein
More informationDairyGuard: Augmenting Nutritional Testing of Milk Powder with Adulterant Screening
APPLICATION NOTE Near Infrared Spectroscopy Authors: Ben Perston Rob Packer PerkinElmer Inc. Shelton, CT DairyGuard: Augmenting Nutritional Testing of Milk Powder with Adulterant Screening Introduction
More informationVisible-near infrared spectroscopy to assess soil contaminated with cobalt
Available online at www.sciencedirect.com Procedia Engineering 35 (2012 ) 245 253 International Meeting of Electrical Engineering Research ENIINVIE 2012 Visible-near infrared spectroscopy to assess soil
More informationResearch Article Honey Discrimination Using Visible and Near-Infrared Spectroscopy
International Scholarly Research Network ISRN Spectroscopy Volume 2012, Article ID 487040, 4 pages doi:10.5402/2012/487040 Research Article Honey Discrimination Using Visible and Near-Infrared Spectroscopy
More informationNatural Products. Innovation with Integrity. High Performance NMR Solutions for Analysis NMR
Natural Products High Performance NMR Solutions for Analysis Innovation with Integrity NMR NMR Spectroscopy Continuous advancement in Bruker s NMR technology allows researchers to push the boundaries for
More informationChemometrics. Classification of Mycobacteria by HPLC and Pattern Recognition. Application Note. Abstract
12-1214 Chemometrics Application Note Classification of Mycobacteria by HPLC and Pattern Recognition Abstract Mycobacteria include a number of respiratory and non-respiratory pathogens for humans, such
More informationDetection of Chlorophyll Content of Rice Leaves by Chlorophyll Fluorescence Spectrum Based on PCA-ANN Zhou Lina1,a Cheng Shuchao1,b Yu Haiye2,c 1
7th International Conference on Mechatronics, Control and Materials (ICMCM 2016) Detection of Chlorophyll Content of Rice Leaves by Chlorophyll Fluorescence Spectrum Based on PCA-ANN Zhou Lina1,a Cheng
More informationLecture 2: Linear Algebra Review
CS 4980/6980: Introduction to Data Science c Spring 2018 Lecture 2: Linear Algebra Review Instructor: Daniel L. Pimentel-Alarcón Scribed by: Anh Nguyen and Kira Jordan This is preliminary work and has
More informationQTOF-based proteomics and metabolomics for the agro-food chain.
QTOF-based proteomics and metabolomics for the agro-food chain luigi.lucini@unicatt.it Metabolomics Two scenarios identification of known unknowns and unknown unknowns For known unknowns use spectral or
More informationUSE OF NIR SPECTROSCOPY AND LS-SVM MODEL FOR THE DISCRIMINATION OF VARIETIES OF SOIL
USE OF NIR SPECTROSCOPY AND LS-SVM MODEL FOR THE DISCRIMINATION OF VARIETIES OF SOIL Zengfang Li 1, Jiajia Yu 2, Yong He 2,* 1 Zhejiang Water Conservancy and Hydropower College,Hangzhou 310018,China 2
More informationSIMULATIONS-GUIDED DESIGN OF PROCESS ANALYTICAL SENSOR USING MOLECULAR FACTOR COMPUTING
University of Kentucky UKnowledge University of Kentucky Doctoral Dissertations Graduate School 2007 SIMULATIONS-GUIDED DESIGN OF PROCESS ANALYTICAL SENSOR USING MOLECULAR FACTOR COMPUTING Bin Dai University
More informationMachine learning comes from Bayesian decision theory in statistics. There we want to minimize the expected value of the loss function.
Bayesian learning: Machine learning comes from Bayesian decision theory in statistics. There we want to minimize the expected value of the loss function. Let y be the true label and y be the predicted
More informationMachine learning for pervasive systems Classification in high-dimensional spaces
Machine learning for pervasive systems Classification in high-dimensional spaces Department of Communications and Networking Aalto University, School of Electrical Engineering stephan.sigg@aalto.fi Version
More informationCS 6375 Machine Learning
CS 6375 Machine Learning Nicholas Ruozzi University of Texas at Dallas Slides adapted from David Sontag and Vibhav Gogate Course Info. Instructor: Nicholas Ruozzi Office: ECSS 3.409 Office hours: Tues.
More informationChemometrics. Matti Hotokka Physical chemistry Åbo Akademi University
Chemometrics Matti Hotokka Physical chemistry Åbo Akademi University Linear regression Experiment Consider spectrophotometry as an example Beer-Lamberts law: A = cå Experiment Make three known references
More informationSurface Analysis - The Principal Techniques
Surface Analysis - The Principal Techniques 2nd Edition Editors johnc.vickerman Manchester Interdisciplinary Biocentre, University of Manchester, UK IAN S. GILMORE National Physical Laboratory, Teddington,
More informationIntegrating Traditional Handheld Remotely Sensed Measurement Techniques with Chemometric Analyses in the Classroom
Integrating Traditional Handheld Remotely Sensed Measurement Techniques with Chemometric Analyses in the Classroom Authors Kevin P. Price 1, Thomas J. Prebyl 2, Nan An 3, Nathan R. Myers 4, Yared M. Assefa
More informationIntroduction to Machine Learning. PCA and Spectral Clustering. Introduction to Machine Learning, Slides: Eran Halperin
1 Introduction to Machine Learning PCA and Spectral Clustering Introduction to Machine Learning, 2013-14 Slides: Eran Halperin Singular Value Decomposition (SVD) The singular value decomposition (SVD)
More informationPerformance of spectrometers to estimate soil properties
Performance of spectrometers to estimate soil properties M. T. Eitelwein¹, J.. M. Demattê², R. G. Trevisan¹,.. nselmi¹, J. P. Molin¹ ¹Biosystems Engineering Department, ESLQ-USP, Piracicaba-SP, Brazil
More informationKronecker Decomposition for Image Classification
university of innsbruck institute of computer science intelligent and interactive systems Kronecker Decomposition for Image Classification Sabrina Fontanella 1,2, Antonio Rodríguez-Sánchez 1, Justus Piater
More informationStructure-Activity Modeling - QSAR. Uwe Koch
Structure-Activity Modeling - QSAR Uwe Koch QSAR Assumption: QSAR attempts to quantify the relationship between activity and molecular strcucture by correlating descriptors with properties Biological activity
More informationBiological and Agricultural Engineering Department UC Davis One Shields Ave. Davis, CA (530)
Exploratory Study to Evaluate the Feasibility of Measuring Leaf Nitrogen Using Silicon- Sensor-Based Near Infrared Spectroscopy for Future Low-Cost Sensor Development Project No.: Project Leader: 08-HORT10-Slaughter
More informationIn Silico Spectra Lab. Slide 1
In Silico Spectra Lab Slide 1 In Silico Spectra Lab Libraries Management (import spectra from different sources) Explore & investigate unknown samples against spectra libraries Extract qualitative & quantitative
More informationAdvanced Spectroscopy Laboratory
Advanced Spectroscopy Laboratory - Raman Spectroscopy - Emission Spectroscopy - Absorption Spectroscopy - Raman Microscopy - Hyperspectral Imaging Spectroscopy FERGIELAB TM Raman Spectroscopy Absorption
More informationApplication of Near Infrared Spectroscopy to Predict Crude Protein in Shrimp Feed
Kasetsart J. (Nat. Sci.) 40 : 172-180 (2006) Application of Near Infrared Spectroscopy to Predict Crude Protein in Shrimp Feed Jirawan Maneerot 1, Anupun Terdwongworakul 2 *, Warunee Tanaphase 3 and Nunthiya
More informationPrinciples of Food and Bioprocess Engineering (FS 231) Solutions to Example Problems on Mass Balance
Principles of Food and Bioprocess Engineering (FS 231) Solutions to Example Problems on Mass Balance 1. The mass balance equation for the system is: 2 + 3 = m This yields, m = 5 kg 2. The mass balance
More informationFunctional Preprocessing for Multilayer Perceptrons
Functional Preprocessing for Multilayer Perceptrons Fabrice Rossi and Brieuc Conan-Guez Projet AxIS, INRIA, Domaine de Voluceau, Rocquencourt, B.P. 105 78153 Le Chesnay Cedex, France CEREMADE, UMR CNRS
More informationCombining Genetic Algorithms, Neural Networks and Wavelet Transforms for Analysis of Raman Spectra
Combining Genetic Algorithms, Neural Networks and Wavelet Transforms for Analysis of Raman Spectra Kenneth Hennessy, Michael G. Madden and Alan G. Ryder hennessy@vega.it.nuigalway.ie, michael.madden@nuigalway.ie
More informationReprinted from February Hydrocarbon
February2012 When speed matters Loek van Eijck, Yokogawa, The Netherlands, questions whether rapid analysis of gases and liquids can be better achieved through use of a gas chromatograph or near infrared
More informationK A F F E E S P E Z I A L I T Ä T E N C O F F E E S P E C I A L I T I E S
K F F E E S P E Z I L I T Ä T E N C F F E E S P E C I L I T I E S L K H L F R E I E E T R Ä N K E NN- L C H L I C B E V E R E S F, X E I S K F F E E, E I S T E E & S H K E I C E C F F E E, I C E T E &
More informationINTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY
INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK SOIL NITROGEN DETECTION USING NEAR INFRARED SPECTROSCOPY SNEHA J. BANSOD Department
More informationBIOLIGHT STUDIO IN ROUTINE UV/VIS SPECTROSCOPY
BIOLIGHT STUDIO IN ROUTINE UV/VIS SPECTROSCOPY UV/Vis Spectroscopy is a technique that is widely used to characterize, identify and quantify chemical compounds in all fields of analytical chemistry. The
More informationMachine Learning Basics
Security and Fairness of Deep Learning Machine Learning Basics Anupam Datta CMU Spring 2019 Image Classification Image Classification Image classification pipeline Input: A training set of N images, each
More informationComputational Biology Course Descriptions 12-14
Computational Biology Course Descriptions 12-14 Course Number and Title INTRODUCTORY COURSES BIO 311C: Introductory Biology I BIO 311D: Introductory Biology II BIO 325: Genetics CH 301: Principles of Chemistry
More informationCharacterization and Classification of Heroin from Illicit Drug Seizures Using the Agilent 7200 GC/Q-TOF
Characterization and Classification of Heroin from Illicit Drug Seizures Using the Agilent 72 GC/Q-TOF Application Note Forensics Authors Koluntaev Dmitry InterLab Inc. Moscow, Russia Sergei Syromyatnikov
More informationCorresponding Author: Jason Wight,
COMPARISON OF COMBUSTION, CHEMICAL, AND NEAR-INFRARED SPECTROSCOPIC METHODS TO DETERMINE SOIL ORGANIC CARBON Jason P Wight, Fred L. Allen, M. Z., Zanetti, and T. G. Rials Department of Plant Sciences,
More informationEEL 851: Biometrics. An Overview of Statistical Pattern Recognition EEL 851 1
EEL 851: Biometrics An Overview of Statistical Pattern Recognition EEL 851 1 Outline Introduction Pattern Feature Noise Example Problem Analysis Segmentation Feature Extraction Classification Design Cycle
More informationCOMPARISON OF PIXEL-BASED AND OBJECT-BASED CLASSIFICATION METHODS FOR SEPARATION OF CROP PATTERNS
COMPARISON OF PIXEL-BASED AND OBJECT-BASED CLASSIFICATION METHODS FOR SEPARATION OF CROP PATTERNS Levent BAŞAYİĞİT, Rabia ERSAN Suleyman Demirel University, Agriculture Faculty, Soil Science and Plant
More informationQuantifying Surface Lipid Content of Milled Rice via Visible/Near-Infrared Spectroscopy 1
ANALYTICAL TECHNIQUES AND INSTRUMENTATION Quantifying Surface Lipid Content of Milled Rice via Visible/Near-Infrared Spectroscopy 1 H. CHEN, B. P. MARKS, and T. J. SIEBENMORGEN 1 ABSTRACT Cereal Chem.
More information9/26/17. Ridge regression. What our model needs to do. Ridge Regression: L2 penalty. Ridge coefficients. Ridge coefficients
What our model needs to do regression Usually, we are not just trying to explain observed data We want to uncover meaningful trends And predict future observations Our questions then are Is β" a good estimate
More information