This blog has been set up for editors, reviewers, authors and readers of Elsevier's Analytical Chemistry Journals - all of which can be seen below. It will be updated from Monday to Friday with general news and announcements concerning the titles listed on this page. It should be noted that the views or claims made in the news items and feeds are not necessarily those of the Publisher.
World Congress on Biosensors 2014
Biosensors 2014
Tuesday 23 July 2013
Just Published: Chemometrics and Intelligent Laboratory Systems
A new issue of this journal has just
been published. To see abstracts of the papers it contains (with links through
to the full papers) click here:
Publication date: 15 August
2013 Source:Chemometrics and Intelligent Laboratory Systems, Volume
127 Author(s): Andrés Mauricio Castillo , Lalita Uribe , Luc Patiny , Julien
Wist An efficient method to extract and store information from NMR spectra is
proposed that is suitable for comparison and construction of a search engine.
This method based on trees doesn't require any peak picking or any pre-treatment
of the data and is found to outperform the currently available methods, both in
terms of compactness and velocity. Our approach was tested for 1D proton spectra
and 2D HSQC spectra and compared with the method proposed by Pretsch and
coworkers [1,2] [Bodis et al. 2007, Bodis et al. 2009]. Additionally, the
correspondence between spectral and structural similarity was evaluated for both
methods.
Publication date: 15 August
2013 Source:Chemometrics and Intelligent Laboratory Systems, Volume
127 Author(s): Marco S. Reis Current classification and regression
methodologies are strongly focused on maximizing prediction accuracy.
Interpretation is usually relegated to a second stage, after model estimation,
where its parameters and related quantities are scrutinized for relevant
information regarding the process and phenomena under analysis. Network-Induced
Supervised Learning (NI-SL) is a recently proposed framework that balances the
goals of prediction accuracy and interpretation [1], by adopting a modelling
formalism that matches more closely the dependency structure of variables in
current complex systems. This framework computes interpretable features that are
incorporated in the final model, which effectively constrain the predictive
space to be used. However, this restriction does not compromise prediction
ability, which quite often is enhanced. Both classification and regression
problems can be handled. Four widely different real world datasets were used to
illustrate the main features claimed for the NI-SL framework.
Publication date: 15 August
2013 Source:Chemometrics and Intelligent Laboratory Systems, Volume
127 Author(s): Marta Alier , Romà Tauler In this paper the application of
the Multivariate Curve Resolution Alternating Least Squares method (MCR-ALS) to
incomplete data multisets is explored. The experimental incomplete data multiset
studied in this work is taken from a previous multiannual atmospheric monitoring
study of the changes of ozone and nitrogen oxide concentrations in an air
quality sampling station located in the city of Barcelona, in which some of the
individual data sets were missing. Based on the preliminary results obtained in
this study, new data multisets, complete and incomplete, with different levels
of noise were simulated and analysed by a new variant of the MCR-ALS method
which optimises a combined error function including all possible complete data
subsets derived from the original incomplete data multiset. Conclusions are
drawn about the effects of data completeness on the results obtained for
different noise levels and on the viability of trilinear models.
Graphical abstract
Highlights
Multivariate Curve Resolution is applied for the
first time to incomplete data multisets. New data multisets, complete and
incomplete, with different levels of noise were simulated and analysed by a new
variant of the MCR-ALS method which optimises a combined error function
including all possible complete data subsets derived from the original
incomplete data multiset.
Publication date: 15 August
2013 Source:Chemometrics and Intelligent Laboratory Systems, Volume
127 Author(s): Asiye Saidi , Mohammad Mirzaei , Sedighe
Zeinali Preparation of antibody coated gold nanoparticles (GNPs) specific to
aflatoxins B1 and B2 and its use in rapid aflatoxin determination method was
presented in this paper. The formation of gold-labeled antibodies was
accomplished at optimal condition. The processes were monitored by UV–visible
light measurements, transmission electron microscopy (TEM) and fluorescence
spectroscopy. It was found that GNPs with definite surface plasmon absorption
can quench the fluorescence of aflatoxins. The quenching of the fluorescence of
excitation emission matrices (EEMs) of both aflatoxin samples, provoked by
gold-labeled antibodies, was studied by multivariate curve resolution with
alternating least squares (MCR–ALS) method. Quantitative results obtained from
aflatoxins in pistachio nut samples by MCR–ALS are compared to those obtained
using the HPLC method. There are no significant differences between the
methodology proposed and the standard one, and may be a good alternative to the
traditional methods of analysis for aflatoxins.
Publication date: 15 August
2013 Source:Chemometrics and Intelligent Laboratory Systems, Volume
127 Author(s): Yong Cong , Bing-ke Li , Xue-gang Yang , Ying Xue , Yu-zong
Chen , Yi Zeng The quantitative structure–activity relationship (QSAR) for
the prediction of the activity of two different scaffolds of 108 influenza
neuraminidase A/PR/8/34 (H1N1) inhibitors was investigated. A feature selection
method, which combines Genetic Algorithm with Partial Least Square (GA–PLS), was
applied to select proper descriptor subset for QSAR modeling in a linear model.
Then Genetic Algorithm-Support Vector Machine coupled approach (GA–SVM) was
first used to build the nonlinear models with nine GA–PLS selected descriptors.
With the SVM regression model, the corresponding correlation coefficients (R) of
0.9189 for the training set, 0.9415 for the testing set and 0.9254 for the whole
data set were achieved respectively. The two proposed models gained satisfactory
prediction results and can be extended to other QSAR studies.
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