4th EucheMs chemistry congress

thursday, 30-Aug 2012
s617
chem. Listy 106, s587–s1425 (2012)
Analytical chemistry Electrochemistry, Analysis, sample manipulation
Chemometrics – iii
o - 4 2 5
CouPLinG 2d-wAveLet deCoMPoSition And
MuLtivAriAte iMAGe AnALySiS
M. CoCChi 1 , M. Li viGni 1 , J. M. PrAtS MontALBAn 2 ,
A. ferrer 2
1 University of Modena and Reggio Emilia, Chemistry, Modena,
Italy
2 Politechnical University of Valencia, Department of Applied
Statistics Operations Research and Quality, Valencia, Spain
Wavelet transform (WT) is mainly used in image analysis
as a preliminary step for denoising or compression and/or in order
to extract textural features, [1] in this case yelding global image
descriptors to be used for classification or properties prediction.
In the present work, we develop an approach that uses the
2D-DWT (discrete wavelet transform) multiresolution advantage
in the context of defects detection in single images. The basic idea
is to combine the potentiality of the MIA approach [2, 3] with the
wavelet decomposition scheme to take into account pixels
correlation pattern.
To this purpose, given a wavelet filter, the resulting blocks
(Approximation, Horizontal, Vertical and Diagonal coefficients)
from a 2D-WT decomposition of the image (DWT2 and SWT2
decomposition schemes are compared), applied separately to each
color channels, are used as different “versions” of the original
image capturing the different patterns present in the image. We
consider a redundant representation, i.e. including approximations
of every decomposition level considered. In this way, as many
images as 4 times L (decomposition level) times N (color
channels) are obtained. These are unfolded to obtain a data matrix
of dimensions: pixels × (4×L×N). At this point the usual MIA
approach is followed, afterwards constructing multivariate control
charts for Hotelling-T2 and residual sum of squares on the basis
of one or few normal operating images (NOC) so that defects can
be detected in faulty ones.
The new proposal has been tested on different data sets, such
as tiles images with quite difficult to detect defects, oranges
images corresponding to several damages and multispectral bread
images to detect surface defect. The main goal is to highlight, on
one hand, the tipology of defects that can be handled by this
method and how it may be used alternatively or complentary to
the Bharaty and McGregor one, taking advantage of the unique
features of WT, i.e. the fact that the different frequency content
(related to texture) are depicted in disjoint subspaces and on the
other to point out the critical aspects of the methodology.
references:
1. M. Reis, A. Bauer, Wavelet texture analysis of on-line
acquired images for paper formation assessment and
monitoring, Chemometrics and Intelligent Laboratory
Systems 95 (2009) 129–137.
2. Multivariate Image Analysis: a review with applications.
Chemometrics and Intelligent Laboratory Systems, in
press, doi:10.1016/j.chemolab.2011.03.002
3. M.H. Bharati, J.F. MacGregor, Texture analysis of images
using Principal Component Analysis, SPIE/Photonics
Conference on Process Imaging for Automatic Control,
Boston, 2000, pp. 27–37.
Keywords: Multivariate image analysis; texture; wavelets;
fault detetection;
Chemometrics – iii
4 th EucheMs chemistry congress
o - 4 2 6
CheMoMetriCS AS A tooL to inCreASe
effiCienCy of SPeCtroSCoPiC AnALySiS of
food And environMentAL MAtriCeS
t. KuBALLA 1 , S. MuShtAKovA 2 , A. tSiKin 2 ,
d. LAChenMeier 1
1 Chemisches und Veterinäruntersuchungsamt (CVUA),
Karlsruhe, Karlsruhe, Germany
2 Saratov State University, Chemistry Department, Saratov,
Russia
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. In this study we show how
chemometric methods can be efficiently coupled with two
spectroscopic techniques – nuclear magnetic resonance (NMR)
and ultraviolet-visible (UV-VIS) spectroscopy for analysis of
complex matrices.
First, 400 MHz 1H NMR spectroscopy and nontargeted
approach based on principal component analysis (PCA) was
applied to reveal potentially unsafe samples of unrecorded alcohol
as well as for checking the floral origin of honeys, labeling of milk
and milk substitute products and geographical origin of pine nuts.
Validation using the independent test sets by Soft Independent
Modeling of Class Analogy (SIMCA) showed correct
classification in all cases.
The second direction for the application of chemometric
methods in analytical spectroscopy is the quantification of
substances whose signals overlap with signals of other
compounds. This possibility was demonstrated by applying partial
least squares (PLS) regression to quantify several parameters in
alcoholic beverages such as ethyl carbamate, methanol, higher
alcohols, 2-phenyl alcohol and ethyl acetate by means of NMR
spectroscopy and four anions (bromide, bicarbonate, nitrate and
sulphide) in sea water samples based on UV-VIS measurements.
Furthermore, we have applied different multivariate curve
resolution techniques (Alternating Least Squares, Independent
Component Analysis) for self-modeling decomposition of NMR
and UV-VIS spectra. The performance of the algorithms was
shown on several experimental case studies in food science.
The examples provided clearly demonstrate the suitability
of spectroscopic measurements for analysis of food stuffs and
environmental objects. The combination of these spectroscopic
techniques with chemometric methods is a valuable tool to
develop screening methods for checking the authenticity and
quality of these samples.
Keywords: chemometrics; NMR spectroscopy; principal
component analysis; multivariate curve resolution; food
analysis;
AUGUst 26–30, 2012, PrAGUE, cZEcH rEPUbLIc