1 Analytica Chimica Acta 401 (1999) Noise reduction of fast, repetitive GC/MS measurements using principal component analysis (PCA) M. Statheropoulos a,, A. Pappa a, P. Karamertzanis a, H.L.C. Meuzelaar b a Department of Chemical Engineering, Sector 1, National Technical University of Athens (NTUA), 9 Iroon Polytechniou Street, , Athens, Greece b Center for Micro Analysis and Reaction Chemistry, University of Utah, Salt Lake City, USA Received 9 March 1999; received in revised form 3 June 1999; accepted 9 June 1999 Abstract Principal component analysis (PCA) was applied to the noise reduction of low ppb level benzene, toluene, ethyl benzene, xylene (BTEX) type gas chromatography/mass spectrometry (GC/MS) measurements (i.e. BTEX) with a fast, repetitive GC/MS system. The first three principal components (PCs) accounting for approximately 60 80% of the total variance in the original data could be attributed to chemical components, whilst the remaining PCs were found to be due to noise. Reconstruction of the data from the first three PCs resulted in noise reduction with improved signal fidelity. The results of PCA were comparable with those achieved by a Fourier transform method Elsevier Science B.V. All rights reserved. Keywords: Noise reduction; Principal component analysis (PCA); Roving gas chromatography/mass spectrometry (GC/MS) 1. Introduction A roving gas chromatography/mass spectrometry (GC/MS) system, using a zero-emission electric vehicle and equipped with a differential GPC has been used for monitoring and mapping low ppb concentration level benzene, toluene, ethyl benzene, xylene (BTEX) type VOCs in the direct neighborhood of a gas station [1 3]. Concentrations of VOCs in ambient air are usually very low, and in some cases, can be masked by various sources of noise. Thus, the evaluation of low intensity Corresponding author. Tel.: ; fax: address: (M. Statheropoulos) signals can be greatly facilitated by subtraction of the noise. Noise reduction can be achieved by various smoothing or filtering techniques. A number of reduction techniques are known, such as the Gaussian filtering, the Savitzky Golay filter, polynomial filter and the Fourier transforms methods [4 6]. Lee et al.  evaluated Principal component analysis (PCA) as a digital filter to improve the overall quality of GC/MS data on a test mixture of low molecular weight solvents. A marked increase in the signal-to-noise ratio (i.e. by a factor of from 2 to 100) was achieved. The study of the effectiveness of PCA in the noise reduction of low level outdoors BTEX measurements by fast, repetitive GC/MS was the primary target of the present work. PCA examines the degree of correlation between variables, while noise is presumed to control /99/$ see front matter 1999 Elsevier Science B.V. All rights reserved. PII: S (99)
2 36 M. Statheropoulos et al. / Analytica Chimica Acta 401 (1999) random, i.e. non-correlated, signal fluctuations. PCA is used to determine the underlying intrinsic dimensionality of the data. On removing the least significant PCs attributed to the noise and reconstructing the original data set, one expects reduced noise. The results of PCA on noise reduction are compared to those of a fast Fourier transform (FFT) method. It should be mentioned that there are ambiguities in the literature about the definition of noise and as to how it is measured. These ambiguities can be critical when the S/N ratio has to be determined . Our noise estimations were based on N peak-to-peak. 2. Theoretical Multivariate data analysis (MDA) is an established set of techniques for examining the relationships among multiple variables . The term multiple refers to many variables and/or linear combinations of variables. PCA is an MDA technique, which is used whenever it is necessary to form new variables which are linear combinations of the original variables. These components are orthogonal (i.e. not correlated) to each other. The new variables that are formed are referred to as Principal components (PCs). The PCs are extracted so that the first PC accounts for the maximum variance of the data. The second PC accounts for the maximum of the residual variance and so on. Generally, only a handful of PCs is needed to account for the maximum of the variance of the original data set, and for this reason, PCA is generally known as a data reduction technique. In PCA, the square dispersion matrix V is given as follows: V = [ 1 c 1 ] D T D where D is the original or modified data set with size c n, (objects variables). Well-behaved dispersion matrices can often be produced by pretreatment of the original data. Frequently used known pretreatments include mean centering and standardization. For the square matrix V, the eigenvalues and eigenvectors are calculated. The results of PCA are given as the matrices S (scores matrix) and L (loadings matrix). The S matrix is a c f matrix (objects PCs), where f is the number of the first significant PCs and L is an f n matrix (PCs variables). By post multiplying S with L, the mean centered data matrix R is calculated: R = SL R is a c n matrix. To obtain the complete reconstructed matrix of the original data, D rec, the pretreatment of the data, has to be taken into account. Noise reduction is based on reconstruction of the data using a limited number of PCs, which accounts for the maximum variance of the original data and can be attributed to components other than the white noise chemical components. On the other hand, by using only those PCs that are attributed to noise, the noise matrix N is constructed. 3. Experimental 3.1. Roving GC/MS system The fast mobile (roving) GC/MS system used  has an Enviroprobe (Femtoscan Corporation type) inlet system and a Hewlett-Packard MSD type mass analyzer. Single ion monitoring (SIM) was used for recording the mass peaks at m/z 78 (benzene), 91 (toluene) and 106 (xylene or ethyl benzene). The pulsed air sampling duration of the Enviroprobe inlet was 400 ms, and the sampling frequency was one sample per 15 s PCA Twenty four repetitive measurements (sampling points) consisting of 74 scans, each with a sampling frequency of 15 s (that corresponds to 1776 scans over a time period of 6 min) for the mass peaks at m/z 78, 91 and 106, were used as raw data. The size of the raw data matrix D was 24 74: 24 objects (SIM profile corresponds to 24 sampling points) and 74 variables (measurement points within each SIM profile). Three such matrices (D) were produced for each mass peak recorded. Two-dimensional contour plots (pseudo-3d plots) were constructed for the mass peaks at m/z 78, 91 and 106 in order to better visualize the measurements data. The X-axis represents the number of scans during the
3 M. Statheropoulos et al. / Analytica Chimica Acta 401 (1999) Fig. 1. Contour plots of raw data for mass peaks at m/z 78 (a), 91 (b) and 106 (c). A stripe can be attributed to air peak, B to benzene, C to toluene and D to isomers of xylene and/or ethyl benzene.
4 38 M. Statheropoulos et al. / Analytica Chimica Acta 401 (1999) Fig. 2. Reconstructed contour plots and the subtracted noise plot obtained through PCA for mass peaks at m/z 78 (a), 91 (b) and 106 (c). A stripe can be attributed to air peak, B to benzene, C to toluene and D to isomers of xylene and/or ethyl benzene.
5 M. Statheropoulos et al. / Analytica Chimica Acta 401 (1999) Fig. 3. Ninth and 14th SIM profiles after reconstruction by PCA for mass peak signals at m/z 78 (a), 91 (b) and 106 (c). Less intense line corresponds to the raw data. chromatographic analysis time (15 s) of each measurement, the Y-axis the SIM profile number, and the third dimension was a gray scale related to the intensity of the respective signals (abundance in arbitrary units). The pretreatment method of mean centering was used for running the PCA analysis, using PONTOS, an in-house developed multivariate data analysis software package for spectroscopic data .
6 40 M. Statheropoulos et al. / Analytica Chimica Acta 401 (1999) Fig. 4. Frequency spectrum obtained by FFT for the mass peak signals at m/z 78. The first value is not included FFT analysis In FFT analysis, a conversion of the signal from the time domain to the frequency domain occurs. Consequently, the entire data set of roving GC/MS measurements results in a vector for each mass peak. Under Fourier transformation , this produces 1776 different magnitude coefficients ( frequency spectrum ). By rejecting the frequencies related to noise, increased signal-to-noise ratios are obtained, resulting in a smoother spectrum. MATLAB software, version 4.2 of Mathworks, was used for the Fourier transform. 4. Results and discussion In Fig. 1, the contour plots of the mass peaks at m/z 78 (a), 91 (b) and 106 (c) are presented. Furthermore, in Fig. 1a, two dark colour stripes (A and B) appear at 1.5 s (scan 9) and 3 s (scan 15), respectively, whereas in Fig. 1b, two dark colour stripes (A and C) are clearly present at 1.5 s (scan 9) and 5.5 s (scan 27), respectively. In addition, a less intense colour stripe (D) is present at 10.5 s (scan 50). It seems that the less intense stripe presented at 10.5 s in Fig. 1c can be attributed to D. By close examination of the mass spectra of the individual compounds in Fig. 1a c, stripes B, C and D can be attributed to benzene, toluene and the various isomers of xylene and/or ethyl benzene, respectively. Finally, the dark colour stripe (A) at 1.5 s present in all Fig. 1a c can be attributed to the air peak. It should be mentioned that the level of noise expressed as light colours is considered significant. This becomes especially important in the case of stripe D (xylene isomers or ethyl benzene, low S/N ratio) PCA results PCA resulted in 24 PCs. Using the screen plot criterion , three PCs were selected for describing the dimensionality of data. The first three PCs accounted for 59% (m/z 78), 70% (m/z 91) and 76% (m/z 106) of the total variance, respectively. In Fig. 2, the contour plots of reconstructed data using the first three PCs, as well as the extracted noise of mass peaks at m/z 78 (a), 91 (b) and 106 (c) are presented. It should be noted that the same pattern for noise is produced when (a) either the data are reconstructed using the least significant PCs (PC 4 PC 24 )or when (b) the reconstructed matrix D rec (reconstructed through the first three PCs) is subtracted from the original data matrix D. This provides a useful check of the mathematical procedures used. It appears that, by this method, a significant amount of noise is subtracted, whilst the fidelity of the signal is retained. This is shown in Fig. 3, which presents the reconstructed plots by PCA of the ninth (object 9) and 14th (object 14) SIM profiles (mass peak at m/z 78 Fig. 3a, m/z 91 Fig. 3b and m/z 106
7 M. Statheropoulos et al. / Analytica Chimica Acta 401 (1999) Fig. 5. Reconstructed contour plot and subtracted noise through FFT for the mass peak signals at m/z 78. Fig. 3c). An average increase in the S/N ratio (by 130%) was observed for all the data. In addition, stripe D is more clearly shown in the reconstructed plots. Finally, a remarkable volume data reduction is obtained by PCA. For the reconstruction of the initial data after PCA, the scores matrix S (24 3) and the loading matrix L (3 74) are necessary. That corresponds to (24 3) + (74 3)=98 3 values, whereas the initial data volume is values. Taking into account that the data are mean centering, the reconstructed matrix R = SL must be corrected by adding the mean of each column of the original data D to the R matrix, resulting in the final reconstructed matrix
8 42 M. Statheropoulos et al. / Analytica Chimica Acta 401 (1999) Fig. 6. Ninth SIM profile of mass peak signals at m/z 78 for raw data and after noise reduction by PCA and FFT. D rec. Thus, 74 more values (the number of columns of matrix D) are needed. Therefore, the % reduction data volume achieved by PCA is [ ] (24 74) (98 3) = 80% FFT results Fig. 4 presents the magnitudes of the frequencies of the data for mass peak at m/z 78 (frequency spectrum), resulting from FFT. A periodicity of 24 is obvious, as well as the expected symmetry around the central frequency of 889. Thus, peaks appear at 24-base frequencies i.e. 24, 48, 72. This is due to the signal formation, which has a periodicity 1776/24 = 74, in agreement with the roving GC/MS measurements. Assuming that the noise is completely random (white noise with a flat spectrum), retention of only the frequencies 24K, 24+/ 1, 24+/ 2 (Fig. 5) can bring about signal reconstruction. For comparison, the results of noise reduction using both techniques on the ninth SIM profile of the mass peak at m/z 78 are presented in Fig. 6. In conclusion, it seems that both techniques are capable of subtracting comparable amounts of noise while retaining and enhancing the signal profile. Nonetheless, PCA seems to give slightly better results with regard to overall spectrum quality after noise reduction. It should be emphasized that PCA subtracts random peaks that are not correlated (white noise), while the FFT used cannot subtracts the types of noise that have the same frequency of signals. 5. Conclusions PCA has proved to be an efficient tool for the noise reduction of roving CG/MS measurements. PCA extracts the white noise and increases the S/N ratio (an average increase by 130%). Besides, PCA has a remarkable capability of achieving a high degree of data compression (80%), which is important when data transfer by telemetry is needed. PCA results were comparable to those of an FFT method used in this work. References  A. Pappa, M. Statheropoulos, D. Theodossiou, H.L.C. Meuzelaar, Mathematical filtering of noise on roving GC/MS measurements, in: Poster Presentation at Field Screening Europe, September 30 October , Karlsruhe, Germany.
9 M. Statheropoulos et al. / Analytica Chimica Acta 401 (1999)  W. McClennen, C. Vaughn, P. Cole, S.A. Sheya, D. Wager, H.L.C. Meuzelaar, N.S. Arnold, Roving CG/MS: mapping gradients in time and space, in: Proceedings of the Specialist Workshop On Field-Portable Chromatography and Spectrometry, June 3 5, 1996, Snowbird, Utah.  S. Arnold, W.H. McClennen, H.L.C. Meuzelaar, Anal. Chem. 63 (1991) 299.  X.Y. Sun, H. Singh, B. Millier, C.H. Warren, W.A. Aue, J. Chromatogr. A 687 (1994)  B. Barak, Anal. Chem. 67 (1995)  R.E. Synovec, E.S. Yeung, Anal. Chem. 58 (1986)  T.A. Lee, L.M. Headley, J.K. Hardy, Anal. Chem. 63 (1991) 357.  P. Foley, J.G. Dorsey, Chromatographia 18 (1984) 503.  J.F. Hair, R.E. Anderson, R.L. Tatham, Multivariate Data Analysis, 2nd ed., Macmillan, New York,  M. Statheropoulos, H.L.C. Meuzelaar, N. Vassiliadis, Multivariate Data Analysis Techniques for Spectroscopic Data: the PONTOS Case, (software ver. 1.2 and manual), Center for Microanalysis and Reaction Chemistry, The University of Utah,  J.G. Proakis, D.G. Manolakis, Introduction to Digital Signal Processing, MacMillan, New York,  I.T. Jolliffe, Principal Component Analysis, Springer, New York, 1986.
Alignment and Preprocessing for Data Analysis Preprocessing tools for chromatography Basics of alignment GC FID (D) data and issues PCA F Ratios GC MS (D) data and issues PCA F Ratios PARAFAC Piecewise
AppNote 6/2003 Analysis of Flavors using a Mass Spectral Based Chemical Sensor Vanessa R. Kinton Gerstel, Inc., 701 Digital Drive, Suite J, Linthicum, MD 21090, USA Kevin L. Goodner USDA, Citrus & Subtropical
application Note Gas Chromatography/ Mass Spectrometry Author A. Tipler, Senior Scientist PerkinElmer, Inc. Shelton, CT 06484 USA The Determination of Low Levels of Benzene, Toluene, Ethylbenzene, Xylenes
Signal, Noise, and Detection Limits in Mass Spectrometry Technical Note Chemical Analysis Group Authors Greg Wells, Harry Prest, and Charles William Russ IV, Agilent Technologies, Inc. 2850 Centerville
Application Notes # LCMS-35 esquire series Application of LC/APCI Ion Trap Tandem Mass Spectrometry for the Multiresidue Analysis of Pesticides in Water An LC-APCI-MS/MS method using an ion trap system
Pesticide Analysis by Mass Spectrometry Purpose: The purpose of this assignment is to introduce concepts of mass spectrometry (MS) as they pertain to the qualitative and quantitative analysis of organochlorine
AMD Analysis & Technology AG Application Note 120419 Author: Karl-Heinz Maurer APCI-MS Trace Analysis of volatile organic compounds in ambient air A) Introduction Trace analysis of volatile organic compounds
Review Jeopardy Blue vs. Orange Review Jeopardy Jeopardy Round Lectures 0-3 Jeopardy Round $200 How could I measure how far apart (i.e. how different) two observations, y 1 and y 2, are from each other?
The First Quantitative Analysis of Alkylated PAH and PASH by GCxGC/MS and its Implications on Weathering Studies INEF Penn State Conference 2013 Albert Robbat, Jr. and Patrick Antle Tufts University, Chemistry
Basic Filters (7) Convolution/correlation/Linear filtering Gaussian filters Smoothing and noise reduction First derivatives of Gaussian Second derivative of Gaussian: Laplacian Oriented Gaussian filters
Chemistry 321, Experiment 8: Quantitation of caffeine from a beverage using gas chromatography INTRODUCTION The analysis of soft drinks for caffeine was able to be performed using UV-Vis. The complex sample
Expectations for GC-MS Lab Since this is the first year for GC-MS to be used in Dr. Lamp s CHEM 322, the lab experiment is somewhat unstructured. As you move through the two weeks, I expect that you will
Quantitative & Qualitative HPLC i Wherever you see this symbol, it is important to access the on-line course as there is interactive material that cannot be fully shown in this reference manual. Contents
Enhancing GCMS analysis of trace compounds using a new dynamic baseline compensation algorithm to reduce background interference Abstract The advantages of mass spectrometry (MS) in combination with gas
Application Note FTMS-55 Laser/Desorption Ionization FT-ICR Mass Spectrometry as a Tool for Statistical Analysis of Crude Oils Introduction Crude oil is an extremely complex mixture of hydrocarbons and
1 Gas Chromatography/Mass Spectroscopy (GC/MS/MS) Background Information Instructions for the Operation of the Varian CP-3800 Gas Chromatograph/ Varian Saturn 2200 GC/MS/MS See the Cary Eclipse Software
The First International Symposium on Optimization and Systems Biology (OSB 07) Beijing, China, August 8 10, 2007 Copyright 2007 ORSC & APORC pp. 45 51 Integrated Data Mining Strategy for Effective Metabolomic
Version 1, June 13, 2004 Rough Draft form We apologize while we prepare the manuscript for publication but the data are valid and the conclusions are fundamental EEG COHERENCE AND PHASE DELAYS: COMPARISONS
Chemical Analysis of Chardonnay Wines: Identification and Analysis of Important Aroma Compounds ABSTRACT Chardonnay wines from leading world producers were selected for analysis and comparison of volatile
MarkerView Software 1.2.1 for Metabolomic and Biomarker Profiling Analysis Overview MarkerView software is a novel program designed for metabolomics applications and biomarker profiling workflows 1. Using
Detailed simulation of mass spectra for quadrupole mass spectrometer systems J. R. Gibson, a) S. Taylor, and J. H. Leck Department of Electrical Engineering and Electronics, The University of Liverpool,
Component Ordering in Independent Component Analysis Based on Data Power Anne Hendrikse Raymond Veldhuis University of Twente University of Twente Fac. EEMCS, Signals and Systems Group Fac. EEMCS, Signals
EM Clustering Approach for Multi-Dimensional Analysis of Big Data Set Amhmed A. Bhih School of Electrical and Electronic Engineering Princy Johnson School of Electrical and Electronic Engineering Martin
RAPID COMMUNICATIONS IN MASS SPECTROMETRY Rapid Commun. Mass Spectrom. 27; 21: 35 313 Published online in Wiley InterScience (www.interscience.wiley.com).2846 Simulation of high- and low-resolution mass
Advantages of Using Triple Quadrupole over Single Quadrupole Mass Spectrometry to Quantify and Identify the Presence of Pesticides in Water and Soil Samples André Schreiber AB SCIEX Concord, Ontario (Canada)
Aerosol Instrumentation HIGH RESOLUTION MOBILITY SPECTROMETER A NEW FIELD ANALYTICAL TECHNIQUE Ion Mobility Spectrometry (IMS) is an emerging technique in field analysis. IONER has developed a new concept
Toxichem Krimtech 211;78(Special Issue):324 Online extraction LC-MS n method for the detection of drugs in urine, serum and heparinized plasma Daniel M. Mueller, Katharina M. Rentsch Institut für Klinische
Setting up a Quantitative Analysis MS ChemStation Getting Ready 1. Use the tutorial section "Quant Reports" on the MSD Reference Collection CD-ROM that came with your ChemStation software. 2. Know what
Principal Component Analysis ERS70D George Fernandez INTRODUCTION Analysis of multivariate data plays a key role in data analysis. Multivariate data consists of many different attributes or variables recorded
Identification of unknown organic compounds based on comparison of electron ionization mass spectra Andrey S. Samokhin Igor A. Revelsky Chemistry Department Lomonosov Moscow State University Identification
Analysing chromatographic data using data mining to monitor petroleum content in water Geoffrey Holmes, Dale Fletcher, Peter Reutemann and Eibe Frank Computer Science Department, University of Waikato,
Comparison of BTEXS in Olive Oils by Static and Dynamic HT3 Headspace Application Note Abstract The health benefits of consumption of olive oils as part of a healthy diet reaches back to the mid 1950 s.
INTERNATIONAL BLACK SEA UNIVERSITY COMPUTER TECHNOLOGIES AND ENGINEERING FACULTY ELABORATION OF AN ALGORITHM OF DETECTING TESTS DIMENSIONALITY Mehtap Ergüven Abstract of Ph.D. Dissertation for the degree
Numerical Methods for Determining Principal Component Analysis Abstract Factors Béchu, S., Richard-Plouet, M., Fernandez, V., Walton, J., and Fairley, N. (2016) Developments in numerical treatments for
application Note Gas Chromatography/ Mass Spectrometry Authors Lee Marotta Sr. Field Application Scientist Andrew Tipler Senior Scientist PerkinElmer, Inc. Shelton, CT 06484 USA Monitoring Volatile Organic
Software Approaches for Structure Information Acquisition and Training of Chemistry Students Nikolay T. Kochev, Plamen N. Penchev, Atanas T. Terziyski, George N. Andreev Department of Analytical Chemistry,
Application Note No. 051 Determination of N-cyclohexyl-diazeniumdioxide (HDO) containing compounds in treated wood using GC-MS by P Jüngel ¹), J Wittenzellner ²) and E Melcher ¹) ¹) Federal Research Centre
Est. 1984 ORIENTAL JOURNAL OF CHEMISTRY An International Open Free Access, Peer Reviewed Research Journal www.orientjchem.org ISSN: 0970-020 X CODEN: OJCHEG 2012, Vol. 28, No. (1): Pg. 189-202 The Unshifted
Principal Component Analysis Application to images Václav Hlaváč Czech Technical University in Prague Faculty of Electrical Engineering, Department of Cybernetics Center for Machine Perception http://cmp.felk.cvut.cz/
Kapitel 5 5.1. 1. The standardised parameters are given below. Remember to use the population rather than sample standard deviation. The graph of cross-validated error versus component number is presented
Face Recognition using Principle Component Analysis Kyungnam Kim Department of Computer Science University of Maryland, College Park MD 20742, USA Summary This is the summary of the basic idea about PCA
s for Chapter PRBLEM Assuming that the molecular ion is the base peak (00% abundance) what peaks would appear in the mass spectrum of each of these molecules: (a) C5Br (b) C60 (c) C64Br In cases (a) and
INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN USE ICH HARMONISED TRIPARTITE GUIDELINE VALIDATION OF ANALYTICAL PROCEDURES: TEXT AND METHODOLOGY
Fast Continuous Online Analysis of VOCs in Ambient Air using Agilent 5975T LTM GC/MSD and Markes TD Application Note Environmental Author Xiaohua Li Agilent Technologies (Shanghai) Co., Ltd. 412 Ying Lun
Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning SAMSI 10 May 2013 Outline Introduction to NMF Applications Motivations NMF as a middle step
A Study on the Variability of Fresh Diesel from Service Station in Kota Bharu Using GC-MS and PCA Muhammad Suffian Azah, Ahmad Fahmi Lim Abdullah a a Forensic Science Programme, School of Health Sciences,
Application Note # LCMS-81 Introducing New Proteomics Acquisiton Strategies with the compact Towards the Universal Proteomics Acquisition Method Introduction During the last decade, the complexity of samples
Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover CROP CLASSIFICATION WITH HYPERSPECTRAL DATA OF THE HYMAP SENSOR USING DIFFERENT FEATURE EXTRACTION TECHNIQUES Sebastian Mader
CAC/GL 56-2005 Page 1 of 6 GUIDELINES ON THE USE OF MASS SPECTROMETRY (MS) FOR IDENTIFICATION, CONFIRMATION AND QUANTITATIVE DETERMINATION OF RESIDUES CAC/GL 56-2005 CONFIRMATORY TESTS When analyses are
Step-by-Step Analytical Methods Validation and Protocol in the Quality System Compliance Industry BY GHULAM A. SHABIR Introduction Methods Validation: Establishing documented evidence that provides a high
MUSICAL INSTRUMENT FAMILY CLASSIFICATION Ricardo A. Garcia Media Lab, Massachusetts Institute of Technology 0 Ames Street Room E5-40, Cambridge, MA 039 USA PH: 67-53-0 FAX: 67-58-664 e-mail: rago @ media.
Journal of Metals, Materials and Minerals, Vol.20 No.3 pp.145-149, 2010 Development of Headspace Gas Chromatography-Mass Spectrometry for Determination of Residual Monomer in Polymer Latex Nopparat THAWEEWATTHANANON
Cambridge Isotope Laboratories, Inc. www.isotope.com Proteomics Mass Spectrometry Signal Calibration for Protein Quantitation Michael J. MacCoss, PhD Associate Professor of Genome Sciences University of
of melamine-ware by GC-MS and LC-MS/MS Page 1 of 15 Method development for analysis of formaldehyde in foodsimulant extracts of melamine-ware by GC-MS and LC-MS/MS December 2012 Contact Point: Chris Hopley
Authenticity Assessment of Fruit Juices using LC-MS/MS and Metabolomic Data Processing Lukas Vaclavik 1, Ondrej Lacina 1, André Schreiber 2, and Jana Hajslova 1 1 Institute of Chemical Technology, Prague
Guidance for Industry Q2B Validation of Analytical Procedures: Methodology November 1996 ICH Guidance for Industry Q2B Validation of Analytical Procedures: Methodology Additional copies are available from:
Accurate Mass Screening Workflows for the Analysis of Novel Psychoactive Substances TripleTOF 5600 + LC/MS/MS System with MasterView Software Adrian M. Taylor AB Sciex Concord, Ontario (Canada) Overview
Principal Components Analysis (PCA) Janette Walde firstname.lastname@example.org Department of Statistics University of Innsbruck Outline I Introduction Idea of PCA Principle of the Method Decomposing an Association
Aiping Lu Key Laboratory of System Biology Chinese Academic Society APLV@sibs.ac.cn Proteome and Proteomics PROTEin complement expressed by genome Marc Wilkins Electrophoresis. 1995. 16(7):1090-4. proteomics
Random Vectors and the Variance Covariance Matrix Definition 1. A random vector X is a vector (X 1, X 2,..., X p ) of jointly distributed random variables. As is customary in linear algebra, we will write
Analysis of Liquid Samples on the Agilent GC-MS I. Sample Preparation A. Solvent selection. 1. Boiling point. Low boiling solvents (i.e. b.p. < 30 o C) may be problematic. High boiling solvents (b.p. >
C146-E152 Analysis of Phthalate Esters in Children's Toys Using GC-MS GC/MS Technical Report No.4 Yuki Sakamoto, Katsuhiro Nakagawa, Haruhiko Miyagawa Abstract As of February 29, the US Consumer Product
Detecting Network Anomalies using Traffic Modeling Anant Shah Anomaly Detection Anomalies are deviations from established behavior In most cases anomalies are indications of problems The science of extracting
CHE334 Identification of an Unknown Compound By NMR/IR/MS Purpose The object of this experiment is to determine the structure of an unknown compound using IR, 1 H-NMR, 13 C-NMR and Mass spectroscopy. Infrared
Notes for STA 437/1005 Methods for Multivariate Data Radford M. Neal, 26 November 2010 Random Vectors Notation: Let X be a random vector with p elements, so that X = [X 1,..., X p ], where denotes transpose.
Using the Singular Value Decomposition Emmett J. Ientilucci Chester F. Carlson Center for Imaging Science Rochester Institute of Technology email@example.com May 9, 003 Abstract This report introduces
Purdue University: ECE438 - Digital Signal Processing with Applications 1 ECE438 - Laboratory 9: Speech Processing (Week 2) October 6, 2010 1 Introduction This is the second part of a two week experiment.
MassHunter for Agilent GC/MS & GC/MS/MS Next Generation Data Analysis Software Presented by : Terry Harper GC/MS Product Specialist 1 Outline of Topics Topic 1: Introduction to MassHunter Topic 2: Data
ACTA CHROMATOGRAPHICA, NO. 13, 2003 GC METHODS FOR QUANTITATIVE DETERMINATION OF BENZENE IN GASOLINE A. Pavlova and R. Ivanova Refining and Petrochemistry Institute, Analytical Department, Lukoil-Neftochim-Bourgas
Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic
Uncertainty Estimates in XPS Limit of Detection When acquiring XPS data a number of variables determine the quality of a spectrum in terms of signal-to-noise. Examples of variables are: 1. X-ray flux 2.
Teaching notes: Time of flight mass spectrometry These teaching notes relate to section 188.8.131.52 Mass numbers and isotopes of our AS and A-level Chemistry specifications (7404, 7405). This resource aims
C146-E175A Technical Report Automatic Identification and Semi-quantitative Analysis of Psychotropic Drugs in Serum Using GC/MS Forensic Toxicological Database Hitoshi Tsuchihashi 1 Abstract: A sample consisting
A Semi-parametric Approach for Decomposition of Absorption Spectra in the Presence of Unknown Components Payman Sadegh 1,2, Henrik Aalborg Nielsen 1, and Henrik Madsen 1 Abstract Decomposition of absorption
European Medicines Agency June 1995 CPMP/ICH/381/95 ICH Topic Q 2 (R1) Validation of Analytical Procedures: Text and Methodology Step 5 NOTE FOR GUIDANCE ON VALIDATION OF ANALYTICAL PROCEDURES: TEXT AND
1 Example of Time Series Analysis by SSA 1 Let us illustrate the 'Caterpillar'-SSA technique  by the example of time series analysis. Consider the time series FORT (monthly volumes of fortied wine sales
RAPID MARKER IDENTIFICATION AND CHARACTERISATION OF ESSENTIAL OILS USING A CHEMOMETRIC APROACH Cristiana C. Leandro 1, Peter Hancock 1, Christian Soulier 2, Françoise Aime 2 1 Waters Corporation, Manchester,
Introduction to Fourier Transform Infrared Spectrometry What is FT-IR? I N T R O D U C T I O N FT-IR stands for Fourier Transform InfraRed, the preferred method of infrared spectroscopy. In infrared spectroscopy,
Linear Algebra Review By Tim K. Marks UCSD Borrows heavily from: Jana Kosecka firstname.lastname@example.org http://cs.gmu.edu/~kosecka/cs682.html Virginia de Sa Cogsci 8F Linear Algebra review UCSD Vectors The length
Strategies for Developing Optimal Synchronous SIM-Scan Acquisition Methods AutoSIM/Scan Setup and Rapid SIM Technical Overview Introduction The 5975A and B series mass selective detectors (MSDs) provide
EELE445 - Lab 2 Pulse Signals PURPOSE The purpose of the lab is to examine the characteristics of some common pulsed waveforms in the time and frequency domain. The repetitive pulsed waveforms used are
Department of Chemistry College of Science Sultan Qaboos University Title : CHEM 3326 (Applied Spectroscopy) Credits : 3 Course Format : 2 lectures and 2 tutorials Course Text : Spectrometric Identification
Your consent to our cookies if you continue to use this website.