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  Subjects -> COMPUTER SCIENCE (Total: 1969 journals)
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    - COMPUTER SCIENCE (1147 journals)
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COMPUTER SCIENCE (1147 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 11)
Abakós     Open Access   (Followers: 3)
Academy of Information and Management Sciences Journal     Full-text available via subscription   (Followers: 67)
ACM Computing Surveys     Hybrid Journal   (Followers: 23)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 11)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 12)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 3)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 9)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 24)
Advanced Science Letters     Full-text available via subscription   (Followers: 5)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 14)
Advances in Artificial Neural Systems     Open Access   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Computer Science : an International Journal     Open Access   (Followers: 13)
Advances in Computing     Open Access   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 9)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Human-Computer Interaction     Open Access   (Followers: 19)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 35)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access  
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 7)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 9)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 3)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 6)
American Journal of Sensor Technology     Open Access   (Followers: 2)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 5)
Analysis in Theory and Applications     Hybrid Journal  
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 8)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 12)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 2)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 13)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 1)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 31)
Applied Medical Informatics     Open Access   (Followers: 9)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 5)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access  
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 3)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 8)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Basin Research     Hybrid Journal   (Followers: 3)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 232)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 31)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 45)
British Journal of Educational Technology     Hybrid Journal   (Followers: 118)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 12)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
Cell Communication and Signaling     Open Access   (Followers: 1)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 12)
Circuits and Systems     Open Access   (Followers: 13)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Communication Methods and Measures     Hybrid Journal   (Followers: 11)
Communication Theory     Hybrid Journal   (Followers: 18)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 47)
Communications of the Association for Information Systems     Open Access   (Followers: 18)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access  
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access  
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 1)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 8)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 12)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 13)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 27)
Computer     Full-text available via subscription   (Followers: 78)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 8)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 22)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 10)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 1)
Computer Music Journal     Hybrid Journal   (Followers: 13)
Computer Physics Communications     Hybrid Journal   (Followers: 6)
Computer Science - Research and Development     Hybrid Journal   (Followers: 7)
Computer Science and Engineering     Open Access   (Followers: 17)
Computer Science and Information Technology     Open Access   (Followers: 10)
Computer Science Education     Hybrid Journal   (Followers: 12)
Computer Science Journal     Open Access   (Followers: 20)
Computer Science Master Research     Open Access   (Followers: 9)
Computer Science Review     Hybrid Journal   (Followers: 10)

        1 2 3 4 5 6 | Last

Journal Cover Chemometrics and Intelligent Laboratory Systems
  [SJR: 0.697]   [H-I: 92]   [15 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0169-7439
   Published by Elsevier Homepage  [3031 journals]
  • A variable selection method for soft sensor development through mixed
           integer quadratic programming
    • Abstract: Publication date: Available online 25 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Weiyu Jian, Lingyu Zhu, Zuhua Xu, Xi Chen
      Soft sensors are widely employed in industry to predict quality variables, which are difficult to measure online, by using secondary variables. To build an accurate soft sensor, a proper variable selection is critical. In this project, a method of selecting the optimal secondary variables for a soft sensor model is proposed. It is formulated as a nested optimization problem. In each iteration, a mixed integer quadratic programming (MIQP) is conducted with the Bayesian information criterion (BIC) to estimate the prediction error. A warm start (WS) technique is developed to speed up the convergence. The proposed method is evaluated using a number of instances from the UCI Machine Learning Repository. The computational results demonstrate that this method is well suited for finding the best variable subsets. The method is successfully applied to build soft sensors for an industrial distillation column. The results show that the proposed method can effectively select feature variables that will improve the model prediction performance and reduce the model complexity. Comparisons with other methods, including the traditional partial least square technique, are also presented.

      PubDate: 2017-05-28T06:13:36Z
       
  • Selecting local constraint for alignment of batch process data with
           dynamic time warping
    • Abstract: Publication date: Available online 25 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Max Spooner, David Kold, Murat Kulahci
      There are two key reasons for aligning batch process data. The first is to obtain same-length batches so that standard methods of analysis may be applied, whilst the second reason is to synchronise events that take place during each batch so that the same event is associated with the same observation number for every batch. Dynamic time warping has been shown to be an effective method for meeting these objectives. This is based on a dynamic programming algorithm that aligns a batch to a reference batch, by stretching and compressing its local time dimension. The resulting ”warping function” may be interpreted as a progress signature of the batch which may be appended to the aligned data for further analysis. For the warping function to be a realistic reflection of the progress of a batch, it is necessary to impose some constraints on the dynamic time warping algorithm, to avoid an alignment which is too aggressive and which contains pathological warping. Previous work has focused on addressing this issue using global constraints. In this work, we investigate the use of local constraints in dynamic time warping and define criteria for evaluating the degree of time distortion and variable synchronisation obtained. A local constraint scheme is extended to include constraints not previously considered, and a novel method for selecting the optimal local constraint with respect to the two criteria is proposed. For illustration, the method is applied to real data from an industrial bacteria fermentation process.

      PubDate: 2017-05-28T06:13:36Z
       
  • Tchebichef-Hermite image moment method: A novel tool for chemometric
           analysis of three-dimensional spectra
    • Abstract: Publication date: Available online 22 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Bao Qiong Li, Shao Hua Lu, Xue Wang, Min Li Xu, Hong Lin Zhai
      Image moment methods have been applied to the qualitative and quantitative analyses in analytical chemistry owing to their availability. As anther attractive image moment method, Tchebichef-Hermite moment (THM) method was proposed for the first time in this work, which originated from the Tchebichef moment method and Hermite moment method. The performances of the THM method were tested with two instances for the quantitative analysis of multiple target compounds on the basis of HPLC-PAD and LC-MS three-dimensional (3D) spectra, respectively. Experimental results indicate that the THM method not only inherits the common advantages of these discrete orthogonal moments to deal with some fundamental challenges (such as partially overlapped signals, uncalibrated interferences, peak shifts and baseline drifts) during the analytical process of different kinds of 3D spectra, but also has its unique superiority in information extraction ability that simplify the determination of optimum maximal orders in moment methods. Compared with the previously used Tchebichef moment method, THM method is much more convenient and efficient.

      PubDate: 2017-05-23T05:49:06Z
       
  • Itakura-Saito distance based autoencoder for dimensionality reduction of
           mass spectra
    • Abstract: Publication date: Available online 20 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Yuji Nozaki, Takamichi Nakamoto
      Small signals may contain important information. Mass spectra of chemical compounds are usually given in a format of sparse high-dimensional data of large dynamic range. As peaks at high m/z (mass to charge ratio) region of a mass spectrum contribute to sensory information, they should not be ignored during the dimensionality reduction process even if the peak is small. However, in most of dimensionality reduction techniques, large peaks in a dataset are typically more enphasized than tiny peaks when Euclidean space is assessed. Autoencoders are widely used nonlinear dimensionality reduction technique, which is known as one special form of artificial neural networks to gain a compressed, distributed representation after learning. In this paper, we present an autoencoder which uses IS (Itakura-Saito) distance as its cost function to achieve a high capability of approximation of small target inputs in dimensionality reduction. The result of comparative experiments showed that our new autoencoder achieved the higher performance in approximation of small targets than that of the autoencoders with conventional cost functions such as the mean squared error and the cross-entropy.

      PubDate: 2017-05-23T05:49:06Z
       
  • “Slicing” data array in quadrilinear component model: A alternative
           quadrilinear decomposition algorithm for third-order calibration method
    • Abstract: Publication date: Available online 20 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Li-Xia Xie, Hai-Long Wu, Xiao-Hua Zhang, Tong Wang, Li Zhu, Shou-Xia Xiang, Zhi Liu, Ru-Qin Yu
      A three-way data array in the trilinear component model can be described into sliced matrices and then decomposed into three underlying matrices by iterative procedure. In this paper, we make an in-depth study of the quadrilinear component model, generalize the “slice” to the four-way scenario, and develop a novel quadrilinear decomposition algorithm for third-order calibration, i.e., slicing alternating quadrilinear decomposition (SAQLD). The presently developed algorithm can be considered as a generalization of ATLD to four-way case. In the algorithm, updates of four underlying matrices are alternately iterated until convergence is reached. Operation of extracting diagonal elements is adopted, which makes SAQLD focus on extracting the quadrilinear part in data, leading to a significant decrease in the loss function and finally a high-performance computing strategy for SAQLD, i.e., fast convergence. Owing to its specific optimization approach, the proposed SAQLD algorithm recovers parameter matrices faster when compared with the existing quadrilinear decomposition algorithms. Both numerical simulations and experimental measurements demonstrate that third-order calibration based on the SAQLD algorithm allows one to obtain quantitative information regarding known constituents present in samples without worrying about other interferents. Moreover, quantitative results supplied by the SAQLD algorithm are still satisfying when the number of components used in calculation is excessive. Such a feature is very useful in quantitative chemical analysis since it is not easy to accurately determine the appropriate number of components due to the complex of chemical substrates.

      PubDate: 2017-05-23T05:49:06Z
       
  • DD-SIMCA — A MATLAB GUI tool for data driven SIMCA approach
    • Abstract: Publication date: Available online 19 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Y.V. Zontov, O.Ye. Rodionova, S.V. Kucheryavskiy, A.L. Pomerantsev


      PubDate: 2017-05-23T05:49:06Z
       
  • Performance of hybrid electronic tongue and HPLC coupled with chemometric
           analysis for the monitoring of yeast biotransformation
    • Abstract: Publication date: Available online 19 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Marcin Zabadaj, Iwona Ufnalska, Karolina Chreptowicz, Jolanta Mierzejewska, Wojciech Wróblewski, Patrycja Ciosek-Skibińska
      Monitoring of process parameters is of great importance in the field of bioprocess control. In this work two analytical systems: hybrid electronic tongue combining potentiometric and voltammetric detection (hET) and HPLC coupled with Partial Least Squares (PLS) analysis (HPLC-PLS) have been evaluated and compared as novel, rapid techniques for biotransformation monitoring. They were applied for the analysis of yeast culture media during batch fermentation carried out for 2-phenylethanol production. Work-flow of numerical analysis of sample fingerprints provided by these both techniques was presented. The ability of hET-PLS and HPLC-PLS to predict main process parameters, such as optical density, time of culture, glucose and 2-phenylethanol concentration was studied. Both non-selective analytical systems revealed to be suitable tools for yeast fermentation monitoring, while slightly better results were obtained by hET-PLS.
      Graphical abstract image

      PubDate: 2017-05-23T05:49:06Z
       
  • A new kernel function of support vector regression combined with
           probability distribution and its application in chemometrics and the QSAR
           modeling
    • Abstract: Publication date: Available online 19 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Sujie Xue, Xuefeng Yan
      Quantitative structure–activity relationship (QSAR) models are extensively used to identify new chemicals affecting human health and speed up the drug discovery process. The development of accurate QSAR models can lead to a reduced number of experiments conducted on rats and mice to analyze new compounds. In a typical QSAR model, only the relationship among variables is considered, and the probability distribution of the samples is disregarded. Thus, a new kernel function of support vector regression (SVR) that integrates probability distribution is proposed. The proposed kernel function, called SVR-pk, satisfies kernel function theory, and the mean and variance of the sample are used to reflect the main distribution information. To verify the performance of the new kernel function, simulation example, two sets of data from UCI (University of California, Irvine) and two experiments about the compounds toxicity in rodents data obtained from the Carcinogenic Potency Database are employed. Results show that compared with other SVR models utilizing kernel functions, SVR-pk exhibits better performance and is more suitable for QSAR model.

      PubDate: 2017-05-23T05:49:06Z
       
  • Receptor modeling of environmental aerosol data using MLPCA-MCR-ALS
    • Abstract: Publication date: Available online 19 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Yahya Izadmanesh, Jahan B. Ghasemi, Roma Tauler
      Receptor models apportion the measured mass of an ambient particulate matter (PM) sample at a given site, called the receptor, to its emission sources by using multivariate factor analysis. In this work, a general workflow for PM data quality assessment, measurements uncertainty calculations, receptor modeling and error in model parameters estimation is proposed. Maximum likelihood principal component analysis - multivariate curve resolution–alternating least squares (MLPCA-MCR-ALS) is proposed for general bilinear receptor modeling of noisy environmental datasets and compared with other approaches used in the field. Equations to obtain PM-source contribution estimates (PM-SCE) and contribution-to-species to identify emission sources and their attributes are proposed. Propagation of experimental uncertainties in the parameters of the receptor model is obtained using extensive computer resampling methods. Results are shown for source apportionment of a particulate matter (PM10) air monitoring data set obtained under Fairmode-WG3 project.

      PubDate: 2017-05-23T05:49:06Z
       
  • Principal Component Analysis to interpret changes in chromatic parameters
           on paint dosimeters exposed long-term to urban air
    • Abstract: Publication date: Available online 19 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Agustín Herrera, Davide Ballabio, Natalia Navas, Roberto Todeschini, Carolina Cardell
      Atmospheric pollutants can originate the decay of historic paintings exposed to the outdoor elements. This is a cause of great concern, since such contaminants can produce physical-chemical alterations manifested initially in undesirable color change. This paper tests an unsupervised multivariate approach on discrete data color parameters in a pioneering study which combines spectrophotometric data and principal components analysis to detect unaesthetic color change on paint dosimeters in open-air monuments exposed long-term to the urban atmosphere of the city of Granada (South Spain). To this end the chromatic parameter of the CIEL*a*b* and CIEL*C*h* systems (L*, a*, b*, h*, C* and ΔE) were used as variables for subsequent multivariate analysis in order to determine the intrinsic color change trends. The aim is to evaluate the specific chromatic parameter(s) that cause the unaesthetic damage for each type of paint dosimeter, while also considering the influence of the binder (egg yolk/rabbit glue), the pigment (azurite, malachite and lapis lazuli) and for the first time, the grain size of the studied pigments (azurite). Results demonstrated that this approach is capable of discriminating samples on the basis of dosimeter composition, so enabling interpretation of their aging process. Azurite and lapis lazuli-laden dosimeters tended to turn green over time as a result of exposure to city air regardless of binder composition and location. By contrast, all malachite-laden dosimeters became bluer over time. Luminosity remained stronger in dosimeters prepared with collagen, an important parameter in binder discrimination. This information is also of great value for restoration purposes.

      PubDate: 2017-05-23T05:49:06Z
       
  • Computerized delimitation of odorant areas in
           gas-chromatography-olfactometry by kernel density estimation: Data
           processing on French white wines
    • Abstract: Publication date: Available online 19 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Jean Blanquet, Yves Le Fur, Jordi Ballester
      GC-O using the detection frequency method gives a list of odor events (OEs) where each OE is described by a linear retention index (LRI) and by the aromatic descriptor given by a human assessor. The aim of the experimenter is to gather OEs in a total olfactogram on which he tries to delimit odorant areas (OAs), then to compute each detection frequency. This paper proposes a computerized mathematical method based on kernel density estimation that makes up the total olfactogram as continuous and differentiable function from the OEs LRI only. The corresponding curve looks like a chromatogram, the peaks of which are potential OAs. The limits of an OA are the LRI of the two minima surrounding the peak. The method was applied on a big data set: 18 white wines, 17 assessors, 13,037 OEs. A previous manual delimitation made by the experimenter was used as benchmark to test the quality of the rendition by the computed delimitation. A contingency table containing the numbers of OEs that belonged to both benchmark OAs and computed OAs was built. This table enabled to assess the quality of the global rendition (Tschuprow's T coefficients) and the quality of individual rendition of each benchmark OA. In order to define a suitable range of application, the kernel-based method was tested on sub-sets from the global dataset, by randomly drawing n wines out of 18 and p assessors out of 17. The method gave very satisfying results for at least n = 9 wines, p = 7 assessors for the peaks gathering at least (n + p)/2 OEs.

      PubDate: 2017-05-23T05:49:06Z
       
  • Dynamic learning on the manifold with constrained time information and its
           application for dynamic process monitoring
    • Abstract: Publication date: Available online 19 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Jian Yang, Mingshan Zhang, Hongbo Shi, Shuai Tan
      Complex industrial processes exhibit dynamic behavior. Typically, samples are correlate in time. Therefore monitoring methods based on a single process may not perform well under such conditions. In this paper, a novel algorithm named time information constrained embedding (TICE) is proposed to improve the monitoring performance for the dynamic process. In this study, the neighbors are selected to reconstruct the current data point. With the consideration of the serial correlation, the time window with a certain length is adopted to restrict the scope of the neighbors' selection. To reveal the distance in the time scale as well as to preserve the neighborhood structure, a new expression of time weight is given to quantify the importance of sequential neighbors. Furthermore, an enhanced objective function is constructed to calculate the transformation matrix. Finally, the superiority of the proposed method is illustrated by an application example (TecQuipment CE117 process trainer) and the Tennessee Eastman (TE) process.

      PubDate: 2017-05-23T05:49:06Z
       
  • On the structure of dynamic principal component analysis used in
           statistical process monitoring
    • Abstract: Publication date: Available online 18 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Erik Vanhatalo, Murat Kulahci, Bjarne Bergquist
      When principal component analysis (PCA) is used for statistical process monitoring it relies on the assumption that data are time independent. However, industrial data will often exhibit serial correlation. Dynamic PCA (DPCA) has been suggested as a remedy for high-dimensional and time-dependent data. In DPCA the input matrix is augmented by adding time-lagged values of the variables. In building a DPCA model the analyst needs to decide on (1) the number of lags to add, and (2) given a specific lag structure, how many principal components to retain. In this article we propose a new analyst driven method to determine the maximum number of lags in DPCA with a foundation in multivariate time series analysis. The method is based on the behavior of the eigenvalues of the lagged autocorrelation and partial autocorrelation matrices. Given a specific lag structure we also propose a method for determining the number of principal components to retain. The number of retained principal components is determined by visual inspection of the serial correlation in the squared prediction error statistic, Q (SPE), together with the cumulative explained variance of the model. The methods are illustrated using simulated vector autoregressive and moving average data, and tested on Tennessee Eastman process data.

      PubDate: 2017-05-23T05:49:06Z
       
  • Predicting DNase I hypersensitive sites via un-biased pseudo trinucleotide
           composition
    • Abstract: Publication date: Available online 18 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Muhammad Kabir, Dong-Jun Yu
      DNase I Hypersensitive sites (DHS) are the regions that are sensitive to cleavage by the DNase I enzyme. Knowledge regarding these sites is helpful for decryption of the functions of non-coding genomic regions. Various biological processes need its intervention. Traditional techniques are laborious and time-consuming to predict DHS sites. Particularly, with the avalanche of DNA sequences generated in the post-genomic era, the development of computational approaches is highly essential to precisely and timely predict DHS sites in DNA sequences. The existing feature encoding schemes such as pseudo dinucleotide composition, pseudo trinucleotide composition etc. cannot effectively express features from DHS sequences. In the current study, we proposed a new computational technique to predict DHS sites which uses Un-biased Pseudo Trinucleotide Composition (Unb-PseTNC) strategy to extract nominal descriptors from the DHS benchmark dataset and avoid biasness among the classes during the classification phase. Several classification algorithm including Support vector machine (SVM), probabilistic neural network and k-nearest neighbor are employed to classify extracted features. It was observed that SVM in conjunction with Unb-PseTNC outperforms than other techniques. By comparing with other existing predictors, it was perceived that our proposed method achieved higher prediction rates by applying rigorous jackknife test. This indicates that the proposed model will become a useful tool to predict DHS sites and can also be utilized for in-depth study of DNA and genome analysis.

      PubDate: 2017-05-23T05:49:06Z
       
  • Visualising interactions in bi- and triadditive models for three-way
           tables
    • Abstract: Publication date: Available online 18 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Casper Albers, John Gower
      This paper concerns the visualisation of interaction in three-way arrays. It extends some standard ways of visualising biadditive modelling for two-way data to the case of three-way data. Three-way interaction is modelled by the Parafac method as applied to interaction arrays that have main effects and biadditive terms removed. These interactions are visualised in three and two dimensions. We introduce some ideas to reduce visual overload that can occur when the data array has many entries. Details are given on the interpretation of a novel way of representing rank-three interactions accurately in two dimensions. The discussion has implications regarding interpreting the concept of interaction in three-way arrays.

      PubDate: 2017-05-23T05:49:06Z
       
  • Determination of allura red dye in hard candies by using digital images
           obtained with a mobile phone and N-PLS
    • Abstract: Publication date: Available online 18 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Bruno G. Botelho, Kele C.F. Dantas, Marcelo M. Sena
      This paper describes the development of an optical sensor device using a smartphone and a homemade dark chamber built with recycled materials. This low cost instrument was employed in the development of multivariate image regression methods for the determination of the azo dye allura red in hard candies. To build the models, 238 candy samples of four flavors and different brands and batches were used. Firstly, a multivariate calibration model using RGB histograms and partial least squares (PLS) was built. This model provided high prediction errors, which were attributed to the presence of textural variations in the images. Then, a more complex image analysis methodology that incorporates spatial information, and consists of preprocessing by a two-dimensional fast Fourier transform followed by multi-way calibration with N-way PLS, provided better results, decreasing the prediction errors around 25–35%. The final model was submitted to a complete multivariate analytical validation, being considered precise, linear, sensitive and unbiased. The analytical range was established between 22.9 and 78.8 mg kg−1 of allura red. Root mean square errors of calibration (RMSEC) and prediction (RMSEP) of 4.8 and 6.1 mg kg−1 were estimated. The developed method is simple, rapid, and nondestructive.
      Graphical abstract image

      PubDate: 2017-05-23T05:49:06Z
       
  • Fuzzy decision fusion system for fault classification with analytic
           hierarchy process approach
    • Abstract: Publication date: Available online 17 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Yue Liu, Zhiqiang Ge
      Performance of the most existing fault detection and classification methods can only be guaranteed when each of their own assumptions are met. In other words, a method works well in one condition may not perform well in another. In this paper, a new analytic hierarchy process (AHP) based fuzzy decision fusion system is proposed to tackle the fault classification problem. The AHP approach is introduced to determine the priorities of different classifiers, which are further utilized as the weights in ensemble system. Comparing to conventional equal weighted fusion system, the proposed fuzzy fusion system is able to provide more rational and convincing fault classification result. Effectiveness of the proposed fuzzy fusion system with model evaluation is verified through the Tennessee Eastman (TE) benchmark process.

      PubDate: 2017-05-18T05:43:26Z
       
  • Efficient android electronic nose design for recognition and perception of
           fruit odors using Kernel Extreme Learning Machines
    • Abstract: Publication date: Available online 17 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Ayşegül Uçar, Recep Özalp
      This study presents a novel android electronic nose construction using Kernel Extreme Learning Machines (KELMs). The construction consists of two parts. In the first part, an android electronic nose with fast and accurate detection and low cost are designed using Metal Oxide Semiconductor (MOS) gas sensors. In the second part, the KELMs are implemented to get the electronic nose to achieve fast and high accuracy recognition. The proposed algorithm is designed to recognize the odor of six fruits. Fruits at two concentration levels are placed to the sample chamber of the electronic nose to ensure the features invariant with the concentration. Odor samples in the form of time series are collected and preprocessed. This is a newly introduced simple feature extraction step that does not use any dimension reduction method. The obtained salient features are imported to the inputs of the KELMs. Additionally, K-Nearest Neighbor (K-NN) classifiers, the Support Vector Machines (SVMs), Least-Squares Support Vector Machines (LSSVMs), and Extreme Learning Machines (ELMs) are used for comparison. According to the comparative results for the proposed experimental setup, the KELMs produced good odor recognition performance in terms of the high test accuracy and fast response. In addition, odor concentration level was visualized on an android platform.
      Graphical abstract image

      PubDate: 2017-05-18T05:43:26Z
       
  • Evaluation of calibration transfer methods using the ATR-FTIR technique to
           predict density of crude oil
    • Abstract: Publication date: 15 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 166
      Author(s): Rayza R.T. Rodrigues, Julia T.C. Rocha, L. Mirela S.L. Oliveira, Júlio Cesar M. Dias, Edson I. Müller, Eustáquio V.R. Castro, Paulo R. Filgueiras
      Multivariate calibration combined with infrared technique is an alternative to traditional methods of determination of physicochemical parameters in crude oil. However, a multivariate model can only be applied for the instrument in which the spectra were measured. In case of equipment upkeep or change of instrument, transferring the calibration model is necessary for the new instrumental condition or new instrument. In this study, Fourier transform infrared spectra (FTIR) were measured in the mid-infrared region (MIR) in two different instruments for 96 crude oil samples with API gravity ranging from 11.2 to 54.0. Multivariate calibration models by PLS (Partial Least Squares) and OPLS (Orthogonal Projections to Latent Structures) were developed and forefront techniques in the calibration transfer area were tested, namely SBC (Slope and Bias Correction), DS (Direct Standardization) and PDS (Piecewise Direct Standardization). The OPLS method stands out for not requiring transfer samples, although it compromises the accuracy of prediction. Applying spectra transferred by PDS to the OPLS model resulted in accuracy statistically equal to that of the PLS original model.

      PubDate: 2017-05-07T14:46:50Z
       
  • Joint-individual monitoring of large-scale chemical processes with
           multiple interconnected operation units incorporating multiset CCA
    • Abstract: Publication date: Available online 5 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Yang Wang, Qingchao Jiang, Xuefeng Yan, Jingqi Fu
      Large-scale processes with multiple interconnected operation units have become popular, and monitoring such processes is imperative. A joint-individual monitoring scheme that incorporates multiset canonical correlation analysis (MCCA) for large-scale chemical processes with several interconnected operation units is proposed. First, MCCA is employed to extract the joint features throughout the entire process. Second, for each operation unit, the measurements are projected into a joint feature subspace and its orthogonal complement subspace that contains the individual features of the unit. Then, corresponding statistics are constructed to examine the joint and individual features simultaneously. The proposed joint-individual monitoring scheme considers the global information throughout the entire process and the local information of a local operation unit and therefore exhibits superior monitoring performance. The joint-individual monitoring scheme is applied on a numerical example and the Tennessee Eastman benchmark process. Monitoring results indicate the efficiency of the proposed monitoring scheme.

      PubDate: 2017-05-07T14:46:50Z
       
  • Estimation of missing values in a food property database by matrix
           completion using PCA-based approaches
    • Abstract: Publication date: Available online 3 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Samuel Mercier, Martin Mondor, Bernard Marcos, Christine Moresoli, Sébastien Villeneuve
      In this work, five matrix completion algorithms were investigated for the estimation of missing values in a food property database: iterative PCA with (IPCAE) and without (IPCA) early stopping, trimmed scores regression with (TSRE) and without (TSR) early stopping and variational Bayesian PCA (VBPCA). Matrix completion was applied in the context of a food property database (31 properties × 663 observations) developed by meta-analysis for new food product development, a novel application of matrix completion. The database contained 68.7% of missing values. VBPCA and TSRE were the most accurate algorithms and explained on average 42% and 40%, respectively, of the variance of the missing values. The incorporation of an early stopping step in the TSR and IPCA algorithms decreased overfitting and improved significantly their accuracy. The accuracy of the missing value estimates varied significantly according to the property, and the coefficient of determination for each property with VBPCA ranged from 0.02 to 0.84. The accuracy of the missing value estimates was higher when the property known for only a few observations were included in the database, indicating that the matrix completion algorithms successfully used the additional information that those properties provided to improve the estimation of the other properties in the database. For 17% of the database, the matrix completion algorithms could identify if the missing value was above or below the average value of the property with a confidence level above 90%, providing additional information for product characterization at no experimental cost.

      PubDate: 2017-05-07T14:46:50Z
       
  • Detection of Nonlinearity in Soil Property Prediction Models Based on
           Near-infrared Spectroscopy
    • Abstract: Publication date: Available online 1 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Lu Yan, Matheus S. Escobar, Hiromasa Kaneko, Kimito Funatsu
      Soil property analysis is indispensable in precision agriculture, an advanced field regarding site-specific management for crop production enhancement and environmental sustainability. Because of the difficulties in soil sample collection and measurement of soil properties, such as moisture content, total carbon, total nitrogen, electricity, and pH, near-infrared (NIR) spectroscopy is a useful technique to predict soil properties by using statistical learning methods. However, the prediction of soil properties without any knowledge about how different variables might influence their behavior is not adequate. Soil properties differ depending on location and environment. The variability within the same area could cause nonlinearity on a global scale. Therefore, to determine which method and strategy are suitable for this task, the detection of nonlinearity between NIR spectroscopy and soil properties is the main purpose of this study. Various numerical tools and graphical methods were applied to this soil property dataset, such as variable selection, sample splitting, applicability domain evaluation, and residual inspection. Global nonlinearity for all five soil properties was confirmed, and the strength of such nonlinearities was found to be property dependent.

      PubDate: 2017-05-02T14:35:46Z
       
  • Fuzzy clustering as rational partition method for QSAR
    • Abstract: Publication date: Available online 27 April 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Alfonso Pérez-Garrido, Francisco Girón-Rodríguez, Andrés Bueno-Crespo, Jesús Soto, Horacio Pérez-Sánchez, Aliuska Morales Helguera
      Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD.
      Graphical abstract image Highlights

      PubDate: 2017-05-02T14:35:46Z
       
  • Double Outlyingness Analysis in Quantitative Spectral Calibration:
           Implicit Detection and Intuitive Categorization of Outliers
    • Abstract: Publication date: Available online 27 April 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Hui Cao, Yajie Yu, Yan Zhou, Xiali Hei
      In this study, outliers in the spectral calibration set were analyzed and categorized based on their error introducing patterns. A double outlyingness analysis (DOA) method for outlier detection and categorization was proposed as a tool to detail the error structures of outliers. Two outlyingness values were calculated based on a proposed procedure. The outlier diagnosis diagram based on the inlier model was drawn to distinguish four types of samples: type I (outlier), incorrect concentration(s) with contaminated spectral signals; type II (outlier), incorrect concentration(s) with uncontaminated spectral signals; type III (outlier), correct concentration(s) with contaminated spectral signals; type IV (inlier), correct concentration(s) with uncontaminated spectral signals. Four data sets for quantitative spectral calibrations were used to compare DOA and five existing methods. Results show that DOA is able to detect all types of outliers and provide a tool to analyze outlier structures.

      PubDate: 2017-05-02T14:35:46Z
       
  • Recognition and alignment of variables from UV-Vis chromatograms and
           application to industrial enzyme digests classification
    • Abstract: Publication date: Available online 26 April 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Clara Burgos-Simón, Enrique Javier Carrasco-Correa, Miriam Beneito-Cambra, Guillermo Ramis-Ramos, Ernesto Francisco Simó-Alfonso
      In the last years, industrial applications of chemometrics have largely increased due to their capacity to extract important information from complex records as chromatograms or spectra data. The use of chemometric methods also can avoid the use of detectors of elevated cost. In this work, a procedure to recognize the relevant chemical information contained in complex UV-Vis chromatograms, after a trypsin digestion, to identify the three enzyme main classes (proteases, amylases and cellulases) commonly employed in the cleaning industry, has been developed. In order to recognize the chromatogram peaks, six indices of peak identity or identifiers were defined. A program written in MATLAB was elaborated to accomplish multiple comparisons between chromatograms to construct 3rd order tensors, which contain the common peaks of two or more chromatograms. Using a training test and these tensors, the target and sample chromatograms were ordered according to the proximity to its respective class centroids. Further, the peaks with the best warranties of being correctly recognized as belonging to characteristic peptides, common to at least two chromatograms, were used to align the sample chromatograms. Afterwards, to construct an LDA model for enzyme classification, the relative peak area of the aligned and identified peaks were employed. The LDA model was validated showing a 100% of prediction capability by leave-one-out, by dividing the samples in training and evaluation sets and also by the successful prediction of some spiked real samples.

      PubDate: 2017-05-02T14:35:46Z
       
  • Biomarker comparison and selection for prostate cancer detection in
           Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI)
    • Abstract: Publication date: Available online 19 April 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): E. Aguado-Sarrió, J.M. Prats-Montalbán, R. Sanz-Requena, G. Garcia-Martí, L. Martí-Bonmatí, A. Ferrer
      In this work, the capability of imaging biomarkers obtained from multivariate curve resolution-alternating least squares (MCR-ALS), in combination with those obtained from first and second-generation pharmacokinetic models, have been studied for improving prostate cancer tumor depiction using partial least squares-discriminant analysis (PLS-DA). The main goal of this work is to improve tissue classification properties selecting the best biomarkers in terms of prediction. A wrapped double cross-validation method has been applied for the variable selection process. Using the best PLS-DA model, prostate tissues can be classified obtaining 13.4% of false negatives and 7.4% of false positives. Using MCR-ALS biomarkers yields the best models in terms of parsimony and classification performance.

      PubDate: 2017-04-25T14:22:01Z
       
  • Sparse statistical health monitoring: A novel variable selection approach
           to diagnosis and follow-up of individual patients
    • Abstract: Publication date: 15 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 164
      Author(s): J. Engel, L. Blanchet, U.F.H. Engelke, R.A. Wevers, L.M.C. Buydens
      The –omics technologies are becoming increasingly important in health care and are expected to contribute to personalized health care. In a typical experiment, cases and controls are compared as a two-class classification problem. This approach is often unsuitable, for example, because the classes are not well defined due to associated populations being biologically too heterogeneous. Recently, statistical health monitoring (SHM) was introduced as a complementary approach to allow for predictions at the individual level. This approach could be of use in all sorts of applications such as diagnosis of rare diseases, analysis of individual patterns in disease manifestation, disease monitoring, or personalized therapy. SHM uses the framework of statistical process monitoring (SPM) in a clinical setting. The method essentially combines estimation of Mahalanobis distances (MD) with principal component analysis (PCA) to evaluate the difference in the –omics data of an individual subject to a normal reference range (normal operating conditions). It is well known from SPM, however, that reliable identification of the variables primarily responsible for this difference is hampered by the smearing effect, which is a result of the PCA step. To avoid this problem, we propose to combine estimation of the MD with variable selection via an l1-norm penalty instead of using dimension reduction. This way a sparse MD metric is obtained. The effectiveness of this method is illustrated by several simulation studies and its application to urine 1H-NMR metabolomics data for diagnosis of multiple inborn errors of metabolism.

      PubDate: 2017-04-18T14:01:59Z
       
  • Multivariate curve resolution – Alternating least squares analysis of
           the total synchronous fluorescence spectra: An attempt to identify
           polyphenols contribution to the emission of apple juices
    • Abstract: Publication date: 15 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 164
      Author(s): Katarzyna Włodarska, Katarzyna Pawlak-Lemańska, Igor Khmelinskii, Ewa Sikorska
      Presently we applied multivariate curve resolution – alternating least squares (MCR-ALS) method for the analysis of front-face total synchronous fluorescence spectra (TSFS) of differently processed apple juices. This analysis enabled extracting of the TSFS profiles of five fluorescent components with distinct spectral characteristics and different contributions to the fluorescence of individual juices. Based on the spectral profiles and quantitative relationship with the chemical parameters describing the antioxidant properties of juices, three of the resolved components may be tentatively attributed to phenolic compounds. The analysis using multiple linear regression (MLR) and partial least square (PLS) regression confirmed better performance of fluorescence for the prediction of the total flavonoid content (TFC) as opposed to the total phenolic content (TPC) and total antioxidant capacity (TAC). This study demonstrated that MCR-ALS decomposition of the TSFS may provide a selective tool for understanding and interpretation of the observed relationships between the fluorescence and the total antioxidant indices of the apple juices.

      PubDate: 2017-04-18T14:01:59Z
       
  • Water eutrophication assessment based on rough set and multidimensional
           cloud model
    • Abstract: Publication date: 15 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 164
      Author(s): Huyong Yan, Di Wu, Yu Huang, Guoyin Wang, Mingsheng Shang, Jianjun Xu, Xiaoyu Shi, Kun Shan, Botian Zhou, Yufei Zhao
      This investigation developed a hybrid rough set (RST) and multidimensional cloud model (RSMCM) to leverage the unique strengths of RST and cloud modeling to evaluate the trophic level. In the proposed hybrid model, RST is used to decrease the data scale and extract the qualitative rules, and the multidimensional cloud model is employed to quantitatively analyze the average values, uniformity and stability of water eutrophication. The experimental results reveal that the hybrid model achieves more accurate assessment results than other mainstream models. Therefore, the hybrid model is a promising alternative for a water eutrophication information system and offers a quantitative measure for evaluating the uniformity and stability of eutrophication in utilities management and for operations staff.

      PubDate: 2017-04-18T14:01:59Z
       
  • Extension of SO-PLS to multi-way arrays: SO-N-PLS
    • Abstract: Publication date: 15 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 164
      Author(s): Alessandra Biancolillo, Tormod Næs, Rasmus Bro, Ingrid Måge
      Multi-way data arrays are becoming more common in several fields of science. For instance, analytical instruments can sometimes collect signals at different modes simultaneously, as e.g. fluorescence and LC/GC-MS. Higher order data can also arise from sensory science, were product scores can be reported as function of sample, judge and attribute. Another example is process monitoring, where several process variables can be measured over time for several batches. In addition, so-called multi-block data sets where several blocks of data explain the same set of samples are becoming more common. Several methods exist for analyzing either multi-way or multi-block data, but there has been little attention on methods that combine these two data properties. A common procedure is to “unfold” multi-way arrays in order to obtain two-way data tables on which classical multi-block methods can be applied. However, it is a known fact that unfolding can lead to overfitted models due to increased flexibility in parameter estimation. In this paper we present a novel multi-block regression method that can handle multi-way data blocks. This method is a combination of a multi-block method called Sequential and Orthogonalized-PLS (SO-PLS) and the multi-way version of PLS, N-PLS. The new method is therefore called SO-N-PLS. We have compared the method to Multi-block-PLS (MB-PLS) and SO-PLS on unfolded data. We investigate the hypotheses that SO-N-PLS has better performances on small data sets and noisy data, and that SO-N-PLS models are easier to interpret. The hypotheses are investigated by a simulation study and two real data examples; one dealing with regression and one with classification. The simulation study show that SO-N-PLS predicts better than the unfolded methods when the sample size is small and the data is noisy. This is due to the fact that it filters out the noise better than MB-PLS and SO-PLS. For the real data examples, the differences in prediction are small but the multi-way method allows easier interpretation.

      PubDate: 2017-04-18T14:01:59Z
       
  • L0-constrained regression using mixed integer linear programming
    • Abstract: Publication date: Available online 12 April 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Mark J. Willis, Moritz von Stosch
      In this work, sparse regression using a penalized least absolute deviations objective function is considered. Regression model sparsity is promoted using a L0 - pseudo norm penalty (the cardinality of the model parameter vector). Implemented using mixed integer linear programming (MILP) it is demonstrated that the use of the L0 - norm (without approximation) enables efficient and accurate solutions to sparse regression problems of practical size. For model development with a large number of potential model parameters (or features) methods to relax the MILP are also developed; using nonlinear function approximations to the L0- norm, penalty terms are linearized and solved using sequential linear programming. Experimental results (using both simulated and real data) demonstrate that these algorithms are also computationally efficient producing accurate and parsimonious model structures. Applications considered are the development of a calibration model for prediction with Near Infrared (NIR) data and the development of a model for the prediction of chemical toxicity - a quantitative structure activity relationship (QSAR).

      PubDate: 2017-04-18T14:01:59Z
       
  • Quantitative Analysis of Biofluids Based on Hybrid Spectra Space
    • Abstract: Publication date: Available online 11 April 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Zhigang Li, Tianhe Li, Hong Lv, Qiaoyun Wang, Guangyuan Si, Zhonghai He
      Direct determination of chemical constituents in complex biofluids without the need for any reagent or pre-processing of samples has become a promising technique for clinical analysis. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy has been widely studied as a powerful method of reagent-free biofluids analysis. In this work, to further utilize information and improve prediction performance, new hybrid spectra space was constructed based on different derivative spectra space. Then, hybrid spectra space ensemble interval partial least squares modeling (HSEiPLS) was proposed for quantification analysis of biofluids. In the experiment of determining glucose concentrations in 58 whole blood samples, the F-test is used to determine the optimal number of latent variables for models and the F-test significance level is set to 0.25. HSEiPLS model provided lower root mean square error of prediction (RMSEP) values 0.352mM/L compared with other methods. In the experiment of determining cholesterol concentrations in 50 whole blood samples, HSEiPLS model provided RMSEP values 0.205mM/L under the same condition of the significance level. Experimental results demonstrate that the proposed HSEiPLS based on hybrid spectra space provides superior predictive power for biofluids.
      Graphical abstract image

      PubDate: 2017-04-11T13:31:45Z
       
  • A statistical strategy to assess cleaning level of surfaces using
           Fluorescence spectroscopy and Wilks’ ratio
    • Abstract: Publication date: Available online 5 April 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Iuliana Madalina Stoica, Hamid Babamoradi, Frans van den Berg
      There is a high demand for techniques able to monitor on-line, in real-time, the bio-contamination level of contact surfaces in the food industry. Such techniques could help to react promptly whenever failures in the cleaning or sanitation operations occur, keep the safety parameters in control at any time during production, and ultimately tailor the operations towards more sustainable and efficient practices. However, monitoring surface areas such as conveyor belts comes with a distinct set of challenges from the construction materials used in food processing equipment such as compositional-heterogeneity, background signals and continuous changes due to wear and tear. In this work we demonstrate the potential of front-face fluorescence spectroscopy in combination with Wilks’ ratio statistics for monitoring large surface areas fouled under industrial working conditions. The technique was tested in both off-line and on-line mode, for a polymer-based conveyor surface, which presents an intrinsic natural variation across its running length and which was contaminated artificially for a proof of principle. Results show that any potential contamination will shift the variance and covariance structure of the in-control fluorescence landscapes modeled with PARAFAC, and detected this shift as a deviation from the reference clean state in a Wilks’ ratio based monitoring charts.

      PubDate: 2017-04-11T13:31:45Z
       
  • Fixed volume sequential standard addition calibration: Value assignment of
           impurities in zero gas
    • Abstract: Publication date: 15 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 164
      Author(s): Richard J.C. Brown, Paul J. Brewer, Ki-Hyun Kim
      A fixed volume case of sequential standard addition calibration (S-SAC) for the value assignment of impurities in zero gas is described. A mathematical description of this technique has been derived and has been shown to exhibit a similar systematic bias during the extrapolation process to that seen for other S-SAC cases. The use of S-SAC in the gas phase has demonstrated that variation in the sample volume for S-SAC in the liquid phase is not the generalised cause of the bias experienced during extrapolation. Instead it is the variation in the volume of the original sample with respect to the overall volume of the mixture following the addition of standard. In addition, the requirement to perform a bias correction on the extrapolated values has been discussed and best practice solutions for correction proposed.

      PubDate: 2017-04-04T13:24:31Z
       
  • NMR-based metabolomic analyses for the componential differences and the
           corresponding metabolic responses of three batches of Farfarae Flos
    • Abstract: Publication date: Available online 29 March 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): ZhenYu Li, Jing Li, ZhengZheng Zhang, Xia Mi, GuanHua Du, XueMei Qin
      Farfarae Flos (FF) is a commonly used herbal drug which has been used for a long time in the Tradition Chinese Medicines (TCM). Nowadays, the cultivated FF in the Northern China is the main source of FF used in the clinic of TCM. The chemical compositions of the herbal drugs are always influenced by the weather, geographic location, soil conditions, and cultivation patterns. Thus it is difficult to guarantee the homogeneity or uniformity of the herbal drugs. In this study, a metabolomic approach was used to compare three batches of FF collected from different growing regions. The results showed that three batches of FFs differed from each other both in the primary and secondary metabolites, and there also existed in vivo differences among three groups of FFs. The clustering pattern of n-butanol extracts was similar to those of crude water extracts and serum, indicating that the polar compounds, such as phenylpropanoids and flavonoids, play an important role in the water extracts of FF. The results presented here suggested that the metabolomic approach can be used as a valuable method to evaluate the difference of herbal drugs from various origins.
      Graphical abstract image

      PubDate: 2017-04-04T13:24:31Z
       
  • Comparison of CCA and PLS to explore and model NIR data
    • Abstract: Publication date: Available online 28 March 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Fernando Gatius, Carlos Miralbés, Calin David, Jaume Puy
      Partial Least Squares (PLS) regression is the most widely used technique for developing NIR calibrations. PLS uses several factors to reach the optimum models which can be helpful in a physical interpretation of the sources of correlation between x and y variables. However, it suffers from later factors not arising in the order of the explained variance. Canonical Correlation Analysis (CCA) overcomes this problem by selecting the latent variables as the directions of maximum x-y correlation. Calibration of moisture, crude protein, dry gluten and resistance of dough to deformation of wheat flour samples from NIR spectra is here studied using PLS-1, PLS-2, CCA-1 and CCA-2. The calibration set contains 429 samples while 215 extra independent samples are used for the validation set. It is shown that a 2-D CCA-2 calibration model gathers the highest explained variance between the models studied. When particular calibration models of each property are compared, CCA requires regularization to avoid instability of the regression coefficients. A regularization term that tends to reduce the regression coefficients and the Durbin-Watson test or the Test for Runs to select the regularization parameter have been used. Both statistical tests led to similar values of the regularization parameter and the resulting regression coefficients and RMSEP of the CCA-1 models became similar to those of the PLS-1 models.

      PubDate: 2017-04-04T13:24:31Z
       
  • Response Surface Experiments: A Meta-Analysis
    • Abstract: Publication date: Available online 25 March 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Rebecca A. Ockuly, Maria L. Weese, Byran J. Smucker, David J. Edwards, Le Chang
      Response Surface Methodology is a set of experimental design techniques for system and process optimization that is commonly employed as a tool in chemometrics. In the last twenty years, thousands of studies involving response surface experiments have been published. The goal of the present work is to study regularities observed among factor effects in these experiments. Using the Web of Science Application Program Interface, we searched for journal articles associated with response surface studies and extracted over 20,000 records from all Science Citation Index and Social Science Citation Index disciplines between 1990 and the end of 2014. We took a random sample of these papers, stratified by the number of factors, and ended up with a total of 129 experiments and 183 response variables. Extracting the data from each publication, we reanalyzed the experiments and combined the results together in a meta-analysis to reveal information about effect sparsity, heredity, and hierarchy. We empirically quantify these principles to provide a better understanding of response surface experiments, to calibrate experimenter expectations, and to guide researchers toward more realistic simulation scenarios and improved design construction.

      PubDate: 2017-03-27T13:11:43Z
       
  • An implication of Fuzzy ANOVA: Metal uptake and transport by corn grown on
           a contaminated soil
    • Abstract: Publication date: Available online 24 March 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Abbas Parchami, Reyhane Ivani, Mashaallah Mashinchi, İhsan Kaya
      Three organic fertilizers of manure sheep, sewage sludge and municipal waste were mixed with soil at the rates of 0%, 1.25 % and 2.5 % (w/w), respectively. In a greenhouse experiment, corn seed were grown on pots of 3kg treated contaminated soils and irrigated. Sixty days after sowing, the aerial plant parts were harvested and analyzed for Cadmium (Cd) and Lead (Pb) contents. Since all of data in this research are fuzzy, we need an extended version of analysis of variance (ANOVA) to investigate on these fuzzy observations. In this paper, as a method to compare several populations, the fuzzy analysis of variance (FANOVA) has been used where the collected data considered fuzzy rather than crisp numbers and therefore all calculations are based on FANOVA method. Although, ANOVA based on vague data can lead to a fuzzy decision, but measuring the vagueness of this fuzzy decision is one of advantages of proposed method from the applied point of view.

      PubDate: 2017-03-27T13:11:43Z
       
  • Stacked Interval Sparse Partial Least Squares Regression Analysis
    • Abstract: Publication date: Available online 21 March 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Dominic V. Poerio, Steven D. Brown
      A new method based on a combination of stacked interval partial least squares (SIPLS) and sparse partial least squares (SPLS) regression, called stacked interval sparse PLS (SISPLS) regression, is explored. The proposed method is based on splitting spectral data into discrete, equally spaced intervals, building optimized SPLS models on each region, and weighting the local models based on the cross-validation error achieved during the optimization. The method is highly flexible and only performs explicit variable selection when advantageous; instead the aim is to find favorable rotations of the classical PLS solution while also utilizing local information in a spectrum. The SISPLS model regression vector clearly highlights regional and variable importance in the data, permitting a straightforward interpretation of the model. For a specific dataset, the optimal interval size is determined via a random sampling of the calibration data and exhaustive testing of the feasible interval sizes. The method is demonstrated on two NIR datasets and a Raman dataset. In addition to the multi-faceted interpretational advantage from the variable selection and weighting, we show that the predictions from the method are competitive with PLS, SPLS, SIPLS, and VIP selection.

      PubDate: 2017-03-27T13:11:43Z
       
  • Establishment of auto-sampling frequency using a two-state Markov chain
           model
    • Abstract: Publication date: 15 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 164
      Author(s): K. Govindaraju, M. Bebbington, R. Kissling
      Modern bulk material production processes are high volume and high quality processes. The manual grab sampling of bulk material is known to be biased and unrepresentative. Auto-samplers, which are robotic samplers of bulk material in small increments, provide for better representative samples of the production process. The amount and sampling frequency for an auto-sampler can be varied depending on the product type and quality characteristic of interest. This article presents a statistical methodology for determining the sampling frequency for auto-samplers using a two-state Markov chain model for detecting the foreign matter contamination in the production.

      PubDate: 2017-03-20T13:23:30Z
       
  • A NEW MATCHING IMAGE PREPROCESSING FOR IMAGE DATA FUSION
    • Abstract: Publication date: Available online 16 March 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Sara Piqueras, Marcel Maeder, Romà Tauler, Anna de Juan
      Hyperspectral images collected with different spectroscopic techniques can be combined to benefit from complementary information and to improve the general description of chemical systems. The simultaneous analysis of images collected by different spectroscopic platforms can only be carried out when images are spatially matched with each other (i.e., different pixel sizes should be balanced and translation/rotation/scaling transformations should be done if required). The main goal of this work is the proposal of a general methodology to match image spatial properties that uses all pixels acquired in the images and, therefore, avoids the step of selecting analogous reference pixels to be compared. The effect of working with different kinds of image starting information on the robustness of the retrieved optimal translation and rotation parameters has also been assessed. The study has been tested in two different representative situations, namely: a) imaged sample with a clear contour b) imaged sample without defined contour. A final study on the effect of proper image matching is performed by applying Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) to the multiset formed by the appended images from different spectroscopic platforms before and after matching.

      PubDate: 2017-03-20T13:23:30Z
       
  • Integrating untargeted metabonomics, partial least square regression
           analysis and MetPA to explore the targeted pathways involved into Huangqi
           Jiangzhong Tang against chronic atrophic gastritis rats
    • Abstract: Publication date: Available online 11 March 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Yuetao Liu, Jiajia Cui, Yinghuan Hu, Guanhua Du, Xuemei Qin
      Huangqi Jiangzhong Tang (HQJZ), a famous traditional Chinese medicine (TCM) formula, has been widely used for treating chronic atrophic gastritis (CAG) in China. However, its action mechanism was still lack of holistic interpretation. Here, an integrated untargeted metabonomics, partial least square regression analysis (PLS-RA) and MetPA was applied to investigate the intervention of HQJZ against CAG. Our metabonomic study revealed that HQJZ showed the potential protection from metabolic perturbation induced by CAG. Eleven altered metabolites and seven involved metabolic pathways were significantly regulated with pretreatment of HQJZ. Regulation of two pathways: glycine, serine and threonine metabolism and taurine and hypotaurine metabolism, were recognized to be the most relevant efficacy of HQJZ against CAG based on PLS-RA and MetPA analysis, which were related to the improvement of the pathological changes including energy imbalance, excessive oxidative stress, alterations of immune system, as well as inflammation. The result suggested that the proposed strategy could decipher the scientific basis of TCM well, and give us new insights into the pathogenesis of CAG and the targets for clinical treatment.

      PubDate: 2017-03-16T13:16:05Z
       
  • Fault Diagnosis with Between Mode Similarity Analysis Reconstruction for
           Multimode Processes
    • Abstract: Publication date: Available online 9 March 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Rongrong Sun, Yingwei Zhang
      In this paper, a new approach on between mode similarity analysis (BMSA) reconstruction is proposed. Compared with the traditional method, similarity between different modes, higher-order and independent statistics are considered to separate independent subspaces, which contain increased subspace, decreased subspace, unchanged subspace and residual subspace. And further, the fault amplitude and fault directions are accurately extracted from the independent components to realize fault reconstruction, thus fault feature is highlighted, and the diagnosis performance is improved. Fault diagnosis indices are developed based on BMSA reconstruction for various fault alarms. The proposed method is applied to penicillin fermentation process, and is compared to traditional multiple modeling method. Experiment results show that the proposed method can more accurately diagnose fault than traditional multiple modeling method.

      PubDate: 2017-03-16T13:16:05Z
       
  • Constructing 3-level saturated and supersaturated designs using cyclic
           generators
    • Abstract: Publication date: 15 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 164
      Author(s): Nam-Ky Nguyen, Tung-Dinh Pham
      Saturated designs (SDs) and supersaturated designs (SSDs) are designs used at the primary stage of investigations when the number of factors equals or exceeds the number of runs. As many factors in science and engineering are quantitative, this paper discusses an algorithm for constructing 3-level SDs and SSDs using cyclic generators. The E ( s 2 ) -lower bound and examples illustrating the use of these designs are given.

      PubDate: 2017-03-09T04:36:46Z
       
  • Simultaneous determination of amino acid mixtures in cereal by using
           terahertz time domain spectroscopy and chemometrics
    • Abstract: Publication date: Available online 7 March 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Xin Zhang, Shaohua Lu, Yi Liao, Zhuoyong Zhang
      Terahertz (THz) spectroscopy displays special features comparing to the commonly used spectroscopies like infrared and Raman and provides potential applications in broad areas. In this work, terahertz time-domain spectroscopy (THz-TDS) has been utilized for the qualitative and quantitative analyses of ternary mixtures of L-Glutamic acid, L-Glutamine, and L-Tyrosine which have similar chemical structures and properties. Mixtures of amino acids were prepared with yellow foxtail millet matrix to simulate more natural situation instead of polyethylene (PE). Partial least squares (PLS) and support vector machine (SVM) were compared for quantitative analysis in this work. Preprocessing methods, multiplicative scatter correction (MSC), Savitzky-Golay (S-G) smoothing, first derivative and wavelet transform, were investigated based on bootstrapped Latin partitions and external test. The SVM model with MSC preprocessing yielded a stable model and gave accurate prediction for the ternary amino acid in foxtail millet analyzed by THz-TDS. The THz absorption spectra and corresponding concentration profiles of three amino acids components from mixtures were resolved by multivariate curve resolution alternating least squares (MCR-ALS). The results show that THz spectroscopy could be applicable for analyzing more nutritional compounds in different cereals in the future.

      PubDate: 2017-03-09T04:36:46Z
       
  • A novel ensemble L1 regularization based variable selection framework with
           an application in near infrared spectroscopy
    • Abstract: Publication date: 15 April 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 163
      Author(s): Zhang Rui, Chen Yuanyuan, Wang Zhibin, Li Kewu
      Variable selection is an essential part during the whole process of qualitative and quantitative analysis of spectroscopy. Traditional methods like interval partial least square (iPLS), uninformative variable elimination (UVE), successive projections algorithm (SPA) etc. often have some disadvantages such as many parameters need to be tuned, weak robustness and so on. To solve these problems, this paper proposed a novel variable selection framework which combines UVE algorithm and ensemble L1 regularization framework together. The whole process of proposed method includes two phases: rough and fine selection. Firstly, UVE algorithm is used to eliminate the uninformative variables (rough selection). Secondly, the variable selection problem is mapped into a L1 regularization optimization problem with constraint (fine selection). To improve the stability and robustness of proposed method, an ensemble variable selection framework is designed which ensemble the results of many L1 regularization selectors. To validate the performance of proposed method, the following two public near infrared spectral datasets were tested: (1) Spectral (range from 900nm to 1700nm) and octane data of gasoline; (2) Spectral (range from 1100nm to 2498nm) and moisture data of corn. The experimental results showed that the proposed method can not only select the most featured wavelengths, but also can improve the stability and robustness of variable selection results.

      PubDate: 2017-02-17T03:39:37Z
       
  • Gaussian process regression with functional covariates and multivariate
           response
    • Abstract: Publication date: Available online 3 February 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Bo Wang, Tao Chen, Aiping Xu
      Gaussian process regression (GPR) has been shown to be a powerful and effective nonparametric method for regression, classification and interpolation, due to many of its desirable properties. However, most GPR models consider univariate or multivariate covariates only. In this paper we extend the GPR models to cases where the covariates include both functional and multivariate variables and the response is multidimensional. The model naturally incorporates two different types of covariates: multivariate and functional, and the principal component analysis is used to de-correlate the multivariate response which avoids the widely recognised difficulty in the multi-output GPR models of formulating covariance functions which have to describe the correlations not only between data points but also between responses. The usefulness of the proposed method is demonstrated through a simulated example and two real data sets in chemometrics.

      PubDate: 2017-02-04T10:02:47Z
       
 
 
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