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  Subjects -> COMPUTER SCIENCE (Total: 2052 journals)
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COMPUTER SCIENCE (1194 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: 20)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 27)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 12)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 5)
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: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 17)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 15)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 5)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 4)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 7)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 29)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Informatica Malaysia     Open Access  
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 28)
Advanced Science Letters     Full-text available via subscription   (Followers: 10)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
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: 19)
Advances in Computer Engineering     Open Access   (Followers: 4)
Advances in Computer Science : an International Journal     Open Access   (Followers: 15)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 51)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 13)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
Advances in Materials Sciences     Open Access   (Followers: 14)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 6)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 44)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 5)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 8)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
AI EDAM     Hybrid Journal  
Air, Soil & Water Research     Open Access   (Followers: 11)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 5)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 11)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
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: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 12)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 11)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Applied System Innovation     Open Access  
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 146)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 7)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
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   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 4)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 11)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Big Data and Cognitive Computing     Open Access   (Followers: 2)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 294)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 21)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 37)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 46)
British Journal of Educational Technology     Hybrid Journal   (Followers: 144)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal   (Followers: 2)
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 15)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 3)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 14)
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: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
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  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 21)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Computational Physics     Full-text available via subscription   (Followers: 2)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 52)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
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: 8)
Computación y Sistemas     Open Access  
Computation     Open Access   (Followers: 1)
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   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 11)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 16)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 5)
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: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 30)
Computer     Full-text available via subscription   (Followers: 94)
Computer Aided Surgery     Open Access   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 16)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 9)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 23)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 12)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 2)

        1 2 3 4 5 6 | Last

Journal Cover Chemometrics and Intelligent Laboratory Systems
  Journal Prestige (SJR): 0.697
  Citation Impact (citeScore): 92
  Number of Followers: 14  
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0169-7439
   Published by Elsevier Homepage  [3162 journals]
  • Noisy matrix completion on a novel neural network framework
    • Authors: Samuel Mercier; Ismail Uysal
      Pages: 1 - 7
      Abstract: Publication date: 15 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 177
      Author(s): Samuel Mercier, Ismail Uysal
      A novel matrix completion algorithm based on the iterative application of neural networks is presented. It is shown that Bayesian regularization provides proper protection against overfitting, more so than early-stopping or a combination of both. The flexibility to increase the size of the hidden layer provides a better description of increasingly nonlinear relationships between the known and missing values in the data with a limited loss in generalization ability. The proposed neural network algorithm provides a more accurate estimation of missing values than current matrix completion algorithms based on iterative regression approaches or PCA applications for many datasets with fractions of missing values from 5 to 40%. The neural network algorithm performs particularly well on datasets where the number of observations significantly exceeds the number of features.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.04.001
      Issue No: Vol. 177 (2018)
       
  • Fitness partition-based multi-objective differential evolutionary
           algorithm and its application to the sodium gluconate fermentation process
           
    • Authors: Zhan Guo; Xuefeng Yan
      Pages: 8 - 16
      Abstract: Publication date: 15 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 177
      Author(s): Zhan Guo, Xuefeng Yan
      The operating conditions of the fermentation process of sodium gluconate play a key role in the quality and quantity of its production. The fermentation process is highly nonlinear and dynamic, and several objects must be considered. To implement a global and efficient optimization for the fermentation process, a fitness partition-based multi-objective differential evolutionary algorithm (FPMDE) is proposed. In the FPMDE algorithm, the information in the target space, which expresses some superiority message, is used to guide the evolutionary process. Namely, according to the fitness values, the target space is divided into some sub-region, and then some optimal directions are extracted for individuals to search for the optimal region and finally approximate the Pareto front. Experimental results on 20 benchmark functions show its advantage in convergence and diversity compared with 5 other state-of-art algorithms. Further, three objective functions for the fermentation process of sodium gluconate are proposed, and the FPMDE algorithm is applied to obtain its Pareto front; the conversion rates and utilization rate of equipment has been improved. It is shown that the FPMDE can optimize the conditions of the production of sodium gluconate effectively and efficiently.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.04.006
      Issue No: Vol. 177 (2018)
       
  • Duality based interpretation of uniqueness in the trilinear decompositions
    • Authors: Mahdiyeh Ghaffari; Hamid Abdollahi
      Pages: 17 - 25
      Abstract: Publication date: 15 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 177
      Author(s): Mahdiyeh Ghaffari, Hamid Abdollahi
      Application of trilinearity constraint in curve resolution of three-way data sets can play an important role in the triadic decompositions. Matrix Augmented-MCR-ALS is a two-way analysis method while its incorporation with the trilinearity constraint can provide a triadic decomposition like PARAFAC model. Trilinearity is a very strong constraint which can lead unique decomposition under mild conditions. Additionally, duality concept represents a relation between column and row spaces of bilinear data matrices. Thus, in case of a unique solution or uniqueness condition, the duality concept is a general and powerful approach for visualization. Based on the duality concept, it is necessary to define a particular hyper-plane in the dual abstract space in order to have a unique profile. It is discussed and visualized that the trilinearity constraint with the idea of parallel proportional profiles can define a particular hyper-plane in the space causing/leading to uniqueness in the dual space. Several simulated and real data sets were exemplified to show the ability of the duality concept to study and visualize the uniqueness due to the application of trilinearity constraint.
      Graphical abstract image

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.04.007
      Issue No: Vol. 177 (2018)
       
  • OCPMDM: Online computation platform for materials data mining
    • Authors: Qing Zhang; Dongping Chang; Xiuyun Zhai; Wencong Lu
      Pages: 26 - 34
      Abstract: Publication date: Available online 11 April 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Qing Zhang, Dongping Chang, Xiuyun Zhai, Wencong Lu
      With the rapid development of the Materials Genome Initiative (MGI), scientists and engineers are confronted with the need to conduct sophisticated data analytics in modeling the behaviors of materials. Nowadays, it is inconvenient for material researchers to carry out materials data mining work without an efficient platform for materials machine learning. So, it is meaningful to develop an online platform for material researchers in urgent need of using machine learning techniques by themselves. The typical case study is given to demonstrate the applications of the online computation platform for material data mining (OCPMDM) in our lab: The quantitative structure property relationship (QSPR) model for rapid prediction of Curie temperature of perovskite material can be applied to screen out perovskite candidates with higher Curie temperature than those of training dataset collected from references, efficiently. Material data mining tasks can be implemented via the OCPMDM, which provides powerful tools for material researchers in machine learning-assisted materials design and optimization. The URL of OCPMDM is http://materialdata.shu.edu.cn.
      Graphical abstract image

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.04.004
      Issue No: Vol. 177 (2018)
       
  • A variable selection method for multiclass classification problems using
           two-class ROC analysis
    • Authors: Miguel de Figueiredo; Christophe B.Y. Cordella; Delphine Jouan-Rimbaud Bouveresse; Xavier Archer; Jean-Marc Bégué; Douglas N. Rutledge
      Pages: 35 - 46
      Abstract: Publication date: Available online 12 April 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Miguel de Figueiredo, Christophe B.Y. Cordella, Delphine Jouan-Rimbaud Bouveresse, Xavier Archer, Jean-Marc Bégué, Douglas N. Rutledge
      Modern procedures in analytical chemistry generate enormous amounts of data, which must be processed and interpreted. The treatment of such high-dimensional datasets often necessitates the prior selection of a reduced number of variables in order to extract knowledge about the system under study and to maximize the predictability of the models built. Therefore, this article describes a variable selection method for multiclass classification problems using two-class ROC analysis and its associated area under the ROC curve as a variable selection criterion. The variable selection method has been successfully applied to two datasets. For comparison purposes, two other variable selection methods, ReliefF and mRMR, were used and double cross-validation PLS-DA was applied using: (1) all variables and (2) the variables selected using the three methods. It has been demonstrated that correct variable selection can substantially reduce the dimensionality of the datasets, while maximizing the predictability of the models.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.04.005
      Issue No: Vol. 177 (2018)
       
  • Quantitative SERS analysis based on multiple-internal-standard embedded
           core-shell nanoparticles and spectral shape deformation quantitative
           theory
    • Authors: Xue-Qin Zhang; Sheng-Xian Li; Zeng-Ping Chen; Yao Chen; Ru-Qin Yu
      Pages: 47 - 54
      Abstract: Publication date: 15 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 177
      Author(s): Xue-Qin Zhang, Sheng-Xian Li, Zeng-Ping Chen, Yao Chen, Ru-Qin Yu
      Due to the practical impossibility in controlling the number and distribution of "hot spots" on or close to the surfaces of enhancing substrates, internal standards are generally used to improve the accuracy and precision of quantitative surface-enhanced Raman spectroscopy (SERS). Traditional internal standard methods based on either univariate ratiometric models or conventional multivariate calibration models can use the information of only one characteristic SERS peak of only one internal standard, which weakens the efficiency of traditional internal standard methods in improving the quantitative results of SERS assays. In this contribution, the concept of using multiple internal standards was introduced for the first time to quantitative SERS assays by incorporating a unique spectral shape deformation (SSD) quantitative theory with multiple-internal-standard embedded core-shell nanoparticles. The quantification of phosmet residues on apple skins by SERS technique with Au-core/Ag-shell nanoparticles embedded with both 2-MB and PATP as enhancing substrate was used to illustrate the applicability of the proposed strategy. Experimental results demonstrated that the proposed multiple-internal-standard strategy based on SSD achieved much more accurate and precise concentration predictions for phosmet residues on apple skin than traditional single-internal-standard methods based on either univariate ratiometric calibration models or conventional multivariate calibration models. The SERS quantitative results of the multiple-internal-standard strategy were comparable to those obtained by LC-MS/MS. The average relative deviation between the quantitative results of the proposed method and those of ICP-MS was about 8%. The good accuracy of the proposed method makes it a promising alternative for quantitative SERS assays.

      PubDate: 2018-04-25T02:40:13Z
      DOI: 10.1016/j.chemolab.2018.04.014
      Issue No: Vol. 177 (2018)
       
  • A fuzzy-based decision making software for enzymatic electrochemical
           nitrate biosensors
    • Authors: Keyvan Asefpour Vakilian; Jafar Massah
      Pages: 55 - 63
      Abstract: Publication date: 15 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 177
      Author(s): Keyvan Asefpour Vakilian, Jafar Massah
      So far, many analytical biosensors have been introduced to determine the concentration of a wide variety of analytes in environmental and agricultural samples. However, a major part of these biosensors has not been yet developed in commercial portable devices due to the fact that implementation of these methods requires an analyst to interpret the biosensor's response for analyte concentration determination. In this study, a mobile application for iOS platform is developed to predict samples' nitrate concentration in a mediated enzyme-based three-electrode biosensor. The introduced application uses fuzzy inference system (FIS) for nitrate concentration determination. The limiting cathodic current from the cyclic voltammetry, along with the sample’ pH and mediator concentration were considered as the input variables of the FIS, whilst nitrate concentration in the sample was considered as the output variable. In order to design the FIS, fuzzy rules were defined by an expert considering the nature of the problem. Furthermore, the values of the membership function parameters were optimized using a genetic algorithm-based optimization method. The performance of the fuzzy system was acceptable for nitrate concentration prediction since the normalized R 2 and MSE of the prediction in test patterns were 0.95 and 0.005, respectively. Although the FIS model has been used in an intelligent nitrate biosensor in this study, the proposed model can be used in a wide range of environmental, agricultural and food biosensors. An open source version of the software in MATLAB programming environment is available at www.plba.ir/publications.html.

      PubDate: 2018-04-25T02:40:13Z
      DOI: 10.1016/j.chemolab.2018.04.016
      Issue No: Vol. 177 (2018)
       
  • Partial constrained least squares (PCLS) and application in soft sensor
    • Authors: Kaiyi Zheng; Kimito Funatsu
      Pages: 64 - 73
      Abstract: Publication date: 15 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 177
      Author(s): Kaiyi Zheng, Kimito Funatsu
      With the combination of extracting latent variables and setting constrained samples, partial constrained least squares (PCLS) is proposed and applied for soft sensor. Similar to constrained least squares (CLS), for the purpose of generating matrix equations with Lagrange multiplier, PCLS assigns some samples in calibration set as constrained ones and others as non-constrained ones. Then, this regression coefficients vector for calibration can be obtained by solving matrix equations with partial least squares (PLS). In moving window method of soft sensor, the sample to be predicted is highly related to the samples in previous adjacent sequential time points, thus those samples can be set as constrained ones and other samples not close to those sequential time points in the window as non-constrained ones. Based on the constrained and non-constrained samples, PCLS can be applied to calibrating a model and estimating the predicted sample. Two batches of datasets containing Sulfur Recovery Unite (SRU) and simulated datasets generated by random walk were tested by the proposed method. The results showed that PCLS is the generation of CLS, while CLS is the special case of PCLS when the number of latent variables equals the total number of variables and constrained samples. Meanwhile, in contrast with least squares (LS), PLS and CLS, PCLS can result in smaller prediction errors. Furthermore, four simulated datasets (SIM1, SIM2, SIM3 and SIM4) with trend and/or random walk, or, without trend and/or random walk, showed PCLS can be applied to the datasets when the samples in sequential time points are correlated to those in the previous adjacent sequential time points.

      PubDate: 2018-04-25T02:40:13Z
      DOI: 10.1016/j.chemolab.2018.04.010
      Issue No: Vol. 177 (2018)
       
  • Automatic outlier sample detection based on regression analysis and
           repeated ensemble learning
    • Authors: Hiromasa Kaneko
      Pages: 74 - 82
      Abstract: Publication date: 15 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 177
      Author(s): Hiromasa Kaneko
      The fields of chemoinformatics and chemometrics require regression models with high prediction performance. To construct predictive regression models by appropriately detecting outlier samples, a new outlier detection and regression method based on ensemble learning is proposed. Multiple regression models are constructed and y-values are estimated based on ensemble learning. Outlier samples are then detected by comprehensively considering all regression models. Furthermore, it is possible to detect outlier samples robustly and independently by repeated calculations. By analyzing a numerical simulation dataset, two quantitative structure-activity relationship datasets and two quantitative structure-property relationship datasets, it is confirmed that automatic outlier sample detection can be achieved, informative compounds can be selected, and the estimation performance of regression models is improved.

      PubDate: 2018-04-25T02:40:13Z
      DOI: 10.1016/j.chemolab.2018.04.015
      Issue No: Vol. 177 (2018)
       
  • Discrete Hermite moments and their application in chemometrics
    • Authors: Barmak Honarvar Shakibaei Asli; Jan Flusser
      Pages: 83 - 88
      Abstract: Publication date: 15 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 177
      Author(s): Barmak Honarvar Shakibaei Asli, Jan Flusser
      In this paper, we provide comments on the recent paper by Bao Qiong Li et al. [1] that proposed a novel tool for chemometric analysis of three-dimensional spectra using Tchebichef–Hermite image moment method. We show that the proposed combined moments are not stable since the Tchebichef polynomials have a discrete instinct and Hermite polynomials are continuous. We use Gauss–Hermite quadrature to discretize continuous Hermite polynomials. A correct use of the discrete Hermite moments (DHMs) for numerical experiments is presented.

      PubDate: 2018-04-25T02:40:13Z
      DOI: 10.1016/j.chemolab.2018.04.011
      Issue No: Vol. 177 (2018)
       
  • A tool for simulating multi-response linear model data
    • Authors: Raju Rimal; Trygve Almøy; Solve Sæbø
      Pages: 1 - 10
      Abstract: Publication date: Available online 23 February 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Raju Rimal, Trygve Almøy, Solve Sæbø
      Data science is generating enormous amounts of data, and new and advanced analytical methods are constantly being developed to cope with the challenge of extracting information from such “big-data”. Researchers often use simulated data to assess and document the properties of these new methods, and in this paper we present an extension to the R-package simrel, which is a versatile and transparent tool for simulating linear model data with an extensive range of adjustable properties. The method is based on the concept of relevant components, and is equivalent to the newly developed envelope model. It is a multi-response extension of R-package simrel which is available in R-package repository CRAN, and as simrel the new approach is essentially based on random rotations of latent relevant components to obtain a predictor matrix X , but in addition we introduce random rotations of latent components spanning a response space in order to obtain a multivariate response matrix Y . The properties of the linear relation between X and Y are defined by a small set of input parameters which allow versatile and adjustable simulations. Sub-space rotations also allow for generating data suitable for testing variable selection methods in multi-response settings. The method is implemented as an update to the R-package simrel.

      PubDate: 2018-02-26T08:05:50Z
      DOI: 10.1016/j.chemolab.2018.02.009
      Issue No: Vol. 176 (2018)
       
  • Neural component analysis for fault detection
    • Authors: Haitao Zhao
      Pages: 11 - 21
      Abstract: Publication date: Available online 16 February 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Haitao Zhao
      Principal component analysis (PCA) is largely adopted for chemical process monitoring and numerous PCA-based systems have been developed to solve various fault detection and diagnosis problems. Since PCA-based methods assume that the monitored process is linear, nonlinear PCA models, such as autoencoder models and kernel principal component analysis (KPCA), has been proposed and applied to nonlinear process monitoring. However, KPCA-based methods need to perform eigen-decomposition (ED) on the kernel Gram matrix whose dimensions depend on the number of training data. Moreover, prefixed kernel parameters cannot be most effective for different faults which may need different parameters to maximize their respective detection performances. Autoencoder models lack the consideration of orthogonal constraints which is crucial for PCA-based algorithms. To address these problems, this paper proposes a novel nonlinear method, called neural component analysis (NCA), which intends to train a feedforward neural work with orthogonal constraints such as those used in PCA. NCA is a unified model including a nonlinear encoder and a linear decoder. NCA can adaptively learn its parameters through backpropagation and the dimensionality of the nonlinear features has no relationship with the number of training samples. Extensive experimental results on the Tennessee Eastman (TE) benchmark process show the superiority of NCA in terms of missed detection rate (MDR) and false alarm rate (FAR). The source code of NCA can be found in https://github.com/haitaozhao/Neural-Component-Analysis.git.

      PubDate: 2018-02-26T08:05:50Z
      DOI: 10.1016/j.chemolab.2018.02.001
      Issue No: Vol. 176 (2018)
       
  • k-nearest neighbor normalized error for visualization and reconstruction
           – A new measure for data visualization performance
    • Authors: Hiromasa Kaneko
      Pages: 22 - 33
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): Hiromasa Kaneko
      It can be very difficult to automatically determine the hyperparameter values of nonlinear data visualization methods. In this study, a new measure called the k-nearest neighbor normalized error for visualization and reconstruction (k3n-error) is developed to compare the visualization performance and automatically optimize the hyperparameters of nonlinear visualization methods using only unsupervised data. For a given sample, the k3n-error approach is based on the standardized errors between the Euclidean distances to neighboring samples before and after projection onto the latent space. Case studies are conducted using two numerical simulation datasets and four quantitative structure-activity/property relationship datasets. The results confirm that, for each nonlinear visualization method, samples can be mapped to the two-dimensional space while maintaining their proximity relationship from the original space by selecting the hyperparameters using the proposed k3n-error.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.001
      Issue No: Vol. 176 (2018)
       
  • libPLS: An integrated library for partial least squares regression and
           linear discriminant analysis
    • Authors: Hong-Dong Li; Qing-Song Xu; Yi-Zeng Liang
      Pages: 34 - 43
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): Hong-Dong Li, Qing-Song Xu, Yi-Zeng Liang
      Partial least squares (PLS) have gained wide applications especially in chemometrics, metabolomics/metabonomics as well as bioinformatics. Here, we present libPLS, a library that integrates not only basic PLS modeling algorithms but also advanced and/or recently developed methods on model assessment, outlier detection, and variable selection. This package is featured in a set of Model Population Analysis (MPA)-type approaches that have not been integrated into a single package yet and thus functionally complement existing toolboxes. libPLS provides an integrated platform for developing PLS regression and/or linear discriminant analysis (PLS-LDA) models. It is written in MATLAB and freely available at www.libpls.net.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.003
      Issue No: Vol. 176 (2018)
       
  • Robust ridge regression based on self-paced learning for multivariate
           calibration
    • Authors: Jiangtao Peng; Long Tian
      Pages: 44 - 52
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): Jiangtao Peng, Long Tian
      In this paper, we propose a robust ridge regression model based on self-paced learning (RR-SPL) for the high-dimensional spectroscopic data. The proposed RR-SPL model consists of a weighted least-squares loss term on all training samples, a self-paced regularizer on sample weights, and a smoothness penalty on the model parameter. Designating an explicit form of the self-paced regularizer, the weights that indicate the importance of training samples can be automatically optimized in an augmented ridge regression framework. By increasing the model age, more and more training samples from easy to hard are added into the training set to learn a mature model. As a result, the RR-SPL model can weaken the effect of outliers and obtain an accurate spectra-concentrate relation. Experimental results on simulated data sets and four real near-infrared (NIR) spectra data sets show the effectiveness of the proposed RR-SPL method in a wide range of specific prediction tasks with or without outliers.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.004
      Issue No: Vol. 176 (2018)
       
  • A new data representation based on relative measurements and fingerprint
           patterns for the development of QSAR regression models
    • Authors: Irene Luque Ruiz; Miguel Ángel Gómez Nieto
      Pages: 53 - 65
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): Irene Luque Ruiz, Miguel Ángel Gómez Nieto
      Relative distance matrixes represent measurements of the structural characteristics of the molecules, having into account a reference pattern common to the whole data set considered in the development of QSAR regression models. These matrixes store relationships between the data set molecules, measuring the transformation cost between pairs of molecules and a pattern from the common fragments to the entire data set. These measurements are quite related with the activity value changes and, therefore, its use allows the building of robust QSAR regression models. In this paper, we describe the building of relative distance matrixes for the representation of two data sets with clearly different characteristics and previously used as benchmark. Applying Support Vector machine algorithms, several training models and external validation were carried out randomly selecting both sets. The results obtained with correlation coefficient greater than 0.9, low values of error and values of slope and bias close to the ideality have shown the goodness of the presented proposal, clearly improving the results obtained in the literature.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.007
      Issue No: Vol. 176 (2018)
       
  • Study of the effect of the presence of silver nanoparticles on migration
           of bisphenol A from polycarbonate glasses into food simulants
    • Authors: C. Reguera; S. Sanllorente; A. Herrero; L.A. Sarabia; M.C. Ortiz
      Pages: 66 - 73
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): C. Reguera, S. Sanllorente, A. Herrero, L.A. Sarabia, M.C. Ortiz
      The impact that the presence of nanoparticles in food can have on the migration from food contact materials (FCMs) of substances, which occurrence in foodstuffs is regulated, is posed in this paper through a case-study. Migration of bisphenol A (BPA) from polycarbonate glasses into aqueous food simulant B (3% acetic acid, w/v) and simulant D1 (50% ethanol, v/v), both in the absence and presence of silver nanoparticles is tested. The analysis of the amount of BPA released into the food simulants is conducted by comparing population results instead of using the classical location and scatter estimates. β-content tolerance intervals are used to model the statistical distribution of BPA migrated from the polycarbonate glasses. Experimental measurements are performed by HPLC-FLD, and partial least squares regression models are then fitted to determine the concentration of BPA. The analytical procedure fulfils the trueness property. The capability of detection of the method is between 1.7 and 2.3 μg L−1 when the probabilities of false positive and false negative are fixed at 0.05. Using β-content tolerance intervals, in 90% of the specimens of a population of polycarbonate glasses, the amount of BPA migrated into simulant B in the presence of AgNPs is 13.34 μg L−1, at least twice the quantity that migrated in the absence of them.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.005
      Issue No: Vol. 176 (2018)
       
  • Wavelet based classification of MALDI-IMS-MS spectra of serum N-Linked
           glycans from normal controls and patients diagnosed with Barrett's
           esophagus, high grade dysplasia, and esophageal adenocarcinoma
    • Authors: B.K. Lavine; C.G. White; T. Ding; M.M. Gaye; D.E. Clemmer
      Pages: 74 - 81
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): B.K. Lavine, C.G. White, T. Ding, M.M. Gaye, D.E. Clemmer
      Profiling of complex biological samples (e.g., serum) using mass spectrometry continues to be an active area of research with a large and growing literature. Pattern recognition techniques can be effective methods for the analysis of complex data sets generated in these types of studies. Currently, we are investigating the discrimination of disease phenotypes associated with esophageal adenocarcinoma by analysis of single N-linked glycans using matrix assisted laser desorption ionization-ion mobility spectrometry-mass spectrometry (MALDI-IMS-MS). The glycans were extracted from sera of healthy (normal) controls (NC) and patients diagnosed with Barrett's Esophagus (BE), high grade dysplasia (HGD), and esophageal adenocarcinoma (EAC). MALDI-IMS-MS spectral images were collected in duplicate for these 58 serum samples: BE (14 individuals), HGD (7 individuals), EAC (20 individuals) and NC (17 individuals). Ion mobility distributions of N-linked glycans that possessed sufficient signal to noise in all 116 spectra were extracted from the images by box selection across a specific drift bin and m/z range corresponding to a single linked N-glycan ion. A composite ion mobility distribution profile was obtained for each image by sequentially splicing together the mobility distributions of each N-linked glycan across an arbitrary drift bin axis. Wavelet preprocessing of the composite ion mobility distribution profiles was performed using the discrete wavelet transform, which was coupled to a genetic algorithm for variable selection to identify a subset of wavelet coefficients within the data set that optimized the separation of the four classes (BE, HGD, EAC, and NC) in a plot of the two largest principal components of the wavelet transformed data. A discriminant developed from the wavelet coefficients identified by the pattern recognition GA correctly classified all ion mobility distribution profiles in the training set (45 individuals and 87 distribution profiles) and 23 of 26 blinds (13 individuals and 26 distribution profiles) in the prediction set. The proposed MALDI-IMS-MS and pattern recognition methodology has the potential to exploit molecules in serum samples that can serve as the basis of a potential method for cancer prescreening.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.008
      Issue No: Vol. 176 (2018)
       
  • Gaussian process regression coupled with MPT-AES for quantitative
           determination of multiple elements in ginseng
    • Authors: Yangwei Ying; Wei Jin; Yuwei Yan; Ying Mu; Qinhan Jin
      Pages: 82 - 88
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): Yangwei Ying, Wei Jin, Yuwei Yan, Ying Mu, Qinhan Jin
      A comprehensive understanding of spectral information is the most reliable approach to identify ginseng characteristics and quality. More data is produced due to the use of a high resolution spectrometer, which in turn requires high-efficiency computational capability. To well address such problems, chemometrics plays a significant part and shows satisfactory results. In this paper, a Gaussian process regression (GPR) is proposed to couple microwave plasma torch-atomic emission spectrometry (MPT-AES) for quantitative determination of multiple elements in ginseng. GPR as a probabilistic method is capable of dealing with massive data and illustrating the outputs with probabilistic meanings. With high-dimension input variables involved, the principal component analysis (PCA) is proposed to reduce computational amount and improve variable representativeness. The results show that GPR is superior to the conventional approach compared with support vector regression (SVR) by evaluation indexes. All the work is performed based on real ginseng spectral data. Taking into account the computational amount caused by large datasets of scales, three approximation methods (subset of datasets (SD), subset of regressors (SR) and projected process (PP)) are applied to address this problem, which in a way shrink the training set with a certain similar result. It is concluded that the Gaussian process performs well in spectral analysis and has the potential for further practical applications.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.002
      Issue No: Vol. 176 (2018)
       
  • An intelligent non-optimality self-recovery method based on reinforcement
           learning with small data in big data era
    • Authors: Yan Qin; Chunhui Zhao; Furong Gao
      Pages: 89 - 100
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): Yan Qin, Chunhui Zhao, Furong Gao
      Batch processes have attracted extensive attention as a crucial manufacturing way in modern industries. Although they are well equipped with control devices, batch processes may operate at a non-optimal status because of process disturbances, equipment aging, feedstock variations, etc. As a result, the quality indices or economic benefits may be undesirable using the pre-defined normal operation conditions. And this phenomenon is called non-optimality here. Therefore, it is indispensable to timely remedy the process to its optimal status without accurate models or amounts of data. To solve this problem, this study proposes an intelligent non-optimality self-recovery method based on reinforcement learning. First, the causal variables that lead to the non-optimality are identified by developing a status-degraded Fisher discriminant analysis with consideration of sparsity. Second, on the basis of self-learning mechanism, an intelligent self-recovery method is proposed using the reinforcement learning to automatically adjust the set-points of the causal controlled variables. The self-recovery action is taken iteratively through the Actor-Critic structure with two neural networks. In this way, effective actions are taken to remedy the process to its expected status which only require small data. Finally, the efficacy of the proposed method is illustrated by both numerical case and a typical batch-type manufacturing process, i.e., the injection molding process.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.010
      Issue No: Vol. 176 (2018)
       
  • Experimental design and solid state fermentation: A holistic approach to
           improve cultural medium for the production of fungal secondary metabolites
           
    • Authors: Quentin Carboué; Magalie Claeys-Bruno; Isabelle Bombarda; Michelle Sergent; Jérôme Jolain; Sevastianos Roussos
      Pages: 101 - 107
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): Quentin Carboué, Magalie Claeys-Bruno, Isabelle Bombarda, Michelle Sergent, Jérôme Jolain, Sevastianos Roussos
      Solid-state fermentation (SSF) is a cultural method that holds tremendous potentials for the production of numerous microbial value-added compounds in various industries. As for every other process, experimental designs can provide tools to improve the product yields, diminish the production time and thus eventually decreasing the cost of the whole process. However, SSF, because of its solid nature, implies some constraints which consequently require specific tools to efficiently overcome them. The aim of this study was the improvement of the production of antioxidant naphtho-gamma-pyrones produced by Aspergillus niger G131 cultivated using SSF. Two experimental designs were presented, a combined design, taking into account two different types of variables to determine a proper solid medium, and a screening design with mixed-level factors to find solutes with significant positive effects on the output.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.011
      Issue No: Vol. 176 (2018)
       
  • Multi-class classification method using twin support vector machines with
           multi-information for steel surface defects
    • Authors: Maoxiang Chu; Xiaoping Liu; Rongfen Gong; Liming Liu
      Pages: 108 - 118
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): Maoxiang Chu, Xiaoping Liu, Rongfen Gong, Liming Liu
      Focus on the efficiency and accuracy of multi-class classification for steel surface defects, we propose a new twin support vector machines with multi-information (MTSVMs). The new MTSVMs model is based on binary twin support vector machines. It infuses three kinds of information: boundary samples information, representative samples information and feature weight information. Boundary samples information describes the distribution of samples in boundary region for defect dataset. Representative samples information provides important samples in global and local distribution. They make MTSVMs classifier have perfect execution efficiency and anti-noise performance. Feature weight information excavates strongly relevant features, which improves the accuracy of classifier. For six types of steel surface defect, the MTSVMs model is extended as a multi-class classifier. Experimental results show that our proposed multi-information algorithms have satisfactory performance. Moreover, the final comparative experiments prove that our MTSVMs model has perfect performance in efficiency and accuracy, especially for corrupted defect dataset.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.014
      Issue No: Vol. 176 (2018)
       
  • New estimation methods for the Grubbs model
    • Authors: Lorena Cáceres Tomaya; Mário de Castro
      Pages: 119 - 125
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): Lorena Cáceres Tomaya, Mário de Castro
      Generalized fiducial inference for the precision of a measuring instrument without available replications on the observations is our main interest. In this paper, we study two new estimation procedures for the precision parameters, the product variability and the mean of the quantity of interest under the Grubbs model considering the two-instrument case. One method is based on a fiducial generalized pivotal quantity and the other one is built on the method of the generalized fiducial distribution. The behavior of the point and interval estimators is assessed numerically through Monte Carlo simulation studies. Comparisons with two existing frequentist approaches and five existing Bayesian approaches are reported. Finally, a data set is analyzed using the proposed methodology.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.009
      Issue No: Vol. 176 (2018)
       
  • Functional extensions of Mandel's h and k statistics for outlier detection
           in interlaboratory studies
    • Authors: Miguel Flores; Javier Tarrío-Saavedra; Rubén Fernández-Casal; Salvador Naya
      Pages: 134 - 148
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): Miguel Flores, Javier Tarrío-Saavedra, Rubén Fernández-Casal, Salvador Naya
      Functional data analysis (FDA) alternatives, based on the classical Mandel h and k statistics, are proposed to identify the laboratories that supply inconsistent results in interlaboratory studies (ILS). ILS is the procedure performed by a number of laboratories to test the precision of an analytical method, to measure the proficiency of laboratories in implementing an analytical procedure, to certify reference materials, and to evaluate a new experimental standard. The use of outlier tests, such as h and k Mandel statistics proposed by the ASTM E691, is crucial to assess these aims, estimating inter- and intra-laboratory data position and variability from a univariate point of view. Considering that experimental results obtained in analytical sciences are often functional, the use of FDA techniques can prevent the loss of important data information. The FDA approaches of h and k statistics are presented and point-wise obtained to deal with functional experimental data. Both functional statistics are estimated for each laboratory, their functional critical limits are obtained by bootstrap resampling, and new FDA versions of h and k graphics are presented. Real and synthetic thermogravimetric data are utilized to assess the good performance of the proposed FDA h and k statistics and their advantages with respect to the univariate approach.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.016
      Issue No: Vol. 176 (2018)
       
  • Bayesian estimation of the analyte concentrations using the sensor
           responses and the design optimization of a sensor system
    • Authors: David Han; Kevin Johnson
      Pages: 149 - 156
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): David Han, Kevin Johnson
      Using an array of sensors with well calibrated but different tuning curves, it is possible to appreciate a wide range of stimuli. In this work, we first revisit the statistical estimation of the stimuli concentrations given the responses of a sensor array, discussed in Sanchez-Montanes & Pearce [18]. Since it is not a typical regression problem, the Bayesian concept is adopted to develop an estimation method by elucidating the dynamic and uncertain nature of the environment-dependent stimuli with a proper choice of the probability distribution. Other studies confirm that the proposed method can demonstrate a superior performance in terms of accuracy and precision when compared to the popular frequentist methods in addition to the theoretical soundness it enjoys as a statistical estimation problem. Under the proposed framework, the design optimization of an artificial sensory system is also formulated using the expected Bayes risk as an objective function to minimize. The same approach may be equally applied to any sensory system in order to optimize its performance within a population of sensors. Finally, illustrative examples are provided to describe how the proposed method can be applied for the optimal configuration of a sensory system for a given sensing task.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.015
      Issue No: Vol. 176 (2018)
       
  • A novel multivariate calibration method based on variable adaptive
           boosting partial least squares algorithm
    • Authors: Pao Li; Guorong Du; Yanjun Ma; Jun Zhou; Liwen Jiang
      Pages: 157 - 161
      Abstract: Publication date: 15 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 176
      Author(s): Pao Li, Guorong Du, Yanjun Ma, Jun Zhou, Liwen Jiang
      With the aid of chemometric methods, near-infrared (NIR) spectroscopy is widely applied to the analysis of complex samples. Boosting partial least squares (BPLS) is one of the important approaches and has many applications to improve the predictive stability and accuracy of the NIR models. However, accurate calculation results are difficult to obtain due to the redundant variables that contribute more collinearity and noise than relevant information to models. In this work, an algorithm named as variable adaptive boosting partial least squares (VABPLS) was proposed to get higher robustness models and enhance the prediction ability. The theory and calculation of VABPLS are just similar with BPLS algorithm, but a variable adaptive strategy based on adaptive reweighted sampling (ARS) theory is fused into the algorithm to improve the accuracy, instead of simply adding. Simultaneous weighting of samples and variables in the boosting series is found to be more effective than the single weighting. The performance of VABPLS is tested with the NIR spectral datasets of corn and tobacco leaf samples. Results show that the advantages of VABPLS are many, such as enhanced accuracy, less parameters and easy to implement, compared with the traditional approaches.
      Graphical abstract image

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.013
      Issue No: Vol. 176 (2018)
       
  • Near infrared spectroscopy for classification of bacterial pathogen
           strains based on spectral transforms and machine learning
    • Abstract: Publication date: Available online 15 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Ke-Xin Mu, Yao-Ze Feng, Wei Chen, Wei Yu
      The potential of near-infrared spectroscopy in classifying individual bacterial strains from different species was investigated in this study. Bacterial samples in liquid nutrient culture were collected periodically (0, 6 and 12 h) during incubation and their spectra were acquired in the near-infrared (NIR) range of 1000–2500 nm. Spectral transforms, including absorbance (A), transmittance (T) and Kubelka-Munk (KM) units were explored in order to enhance classification performance. Partial least squares discriminant analysis (PLS-DA), radial basis function neural network (RBF) and support vector machine (SVM) were used in classification model development. The results illustrated that nonlinear methods such as SVM and RBF neural network outperformed PLS-DA, where the overall correct classification rates (OCCRs) were both above 96%. Successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and random forest (RF) were employed to reduce spectral redundancy and to identify important wavelengths for simplifying models. The RF model yielded the best predictions as indicated by the shortest modeling time and the excellent OCCRs (100%) for both calibration and prediction. The overall results demonstrated the suitability of NIR spectroscopy with RF for the simultaneous classification of water-borne pathogenic strains from different species.

      PubDate: 2018-06-18T11:44:19Z
       
  • Comparison of integration rules in the case of very narrow chromatographic
           peaks
    • Abstract: Publication date: Available online 6 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Yuri Kalambet, Yuri Kozmin, Andrey Samokhin
      Theory of peak integration is revised for very narrow peaks. It is shown, that Trapezoidal rule area is efficient estimate of full peak area with extraordinary low error. Simpson's rule is less efficient in full area integration. Theoretical conclusions are illustrated by digital simulation and processing of experimental data. It was shown that for Gaussian peak Trapezoidal rule requires 0.62 points per standard deviation (2.5 points per peak width at baseline) to achieve integration error of only 0.1%, while Simpson's rule requires 1.8 times higher data rates. Asymmetric peaks require higher data rates as well. Reasons of poor behavior of Simpson's rule are discussed; averaged Simpson's rules are constructed, these rules coincide with those based on Euler-Maclaurin formula. Euler-Maclaurin rules can reduce error in the case of partial peak integration. Higher peak moments (average retention time, dispersion, skewness, etc.) also exhibit extraordinary low errors and can potentially be used for evaluation of peak shape.

      PubDate: 2018-06-06T11:27:10Z
       
  • Variable selection optimization for multivariate models with Polar
           Qualification System
    • Abstract: Publication date: Available online 4 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Shikhar Mohan, Bruce R. Buchanan, Glen D. Wollenberg, Benoît Igne, James K. Drennen, Carl A. Anderson
      Multivariate models are used in many fields to predict a response from a set of variables having an undetermined covariate structure. Variable selection often improves multivariate model performance by removing information not related to the response of interest. Many variable selection methods exist for this purpose. This study investigates Polar Qualification System (PQS) as a tool for variables selection. A Raman transmission dataset of tablets containing Niacinamide (active pharmaceutical ingredient) and Niacin (degradant) was modeled for degradant weight concentration using Partial Least Squares (PLS) regression. Three variable selection techniques were compared for the development of a stability indicating method: specific peak selection (manual selection), genetic algorithms (GA-PLS), and a newly developed PQS-Hadamard method. The model performance of these techniques was compared to a model developed with the whole spectrum. All models built with selected variables showed reduced prediction error compared to model created with the full variable range. However, the PQS-Hadamard method was demonstrated to be more computationally efficient compared to GA-PLS. Further, it is a potentially automatable process, unlike the specific peak selection, which requires expert selection of variables.

      PubDate: 2018-06-06T11:27:10Z
       
  • Predicting protein lysine methylation sites by incorporating
           single-residue structural features into Chou's pseudo components
    • Abstract: Publication date: Available online 1 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Hao Qiu, Yanzhi Guo, Lezheng Yu, Xuemei Pu, Menglong Li
      Identification of the methylated residues is helpful for us to understand the molecular mechanism of many biological processes. Currently, almost all existing computational methods for methylation site prediction are based on the protein sequences. However, the 3-D structures of proteins are more directly correlated with their biological properties than the sequences. Therefore, in view of few similar works have been done before, a novel method for predicting protein lysine methylation sites were firstly proposed based on single-residue structural features. Different from previous works extracting fragments with the methylated site in the center which contain several neighboring residues as samples, only the single methylated lysine site is considered as a sample in this paper. Then, on basis of the 3-D structures of methylated proteins, we gave a comprehensive feature representation for each methylated lysine by combing accessible surface area (ASA), protrusion index (CX) and depth index (DPX), secondary structure (SS), residue interaction network (RIN) and electrostatics potential (EP). All of these features can well characterize the environmental information of each methylated lysine, in other words, the structural information of the neighboring residues has been integrated into the features of it. According to our analysis, we suggest that it’s more efficient to establish the model focusing on single sites than adding adjacent residues. The prediction model was assessed by the testing set and yielded a good performance with the sensitivity of 95.1% and specificity of 89.0%. Moreover, a common independent dataset was collected for further evaluating our model and other five existing sequence-based methods. The prediction results indicate that our method outperforms them and all experimentally confirmed methylated sites are successfully identified by our model. Finally, we conducted predictions on a proteomic scale in order to provide guidance for further experiments. All results indicate that our method can be a useful implement in identifying methylated lysine sites.

      PubDate: 2018-06-03T11:18:09Z
       
  • Generalization of Powered–Partial-Least-Squares
    • Abstract: Publication date: Available online 28 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Francis B. Lavoie, Koji Muteki, Ryan Gosselin
      Indahl originally proposed a variant to Wold's PLS1 algorithm in which weight coefficients were all modified by an exponent coefficient. This led to Powered-PLS (P-PLS). The aim of this paper is to revisit Indahl's P-PLS algorithm in order to make a robust and fast regression methodology calculating easy to interpret models. We first demonstrate that P-PLS is in fact a regression based on correlation maximization, but constrained by weight coefficients originally calculated in standard PLS1. From that, we propose a generalization of P-PLS by replacing the power transformation function by β Cumulative Density Functions (β-CDFs), leading to our proposed regression methodology called β-PLS. With two public datasets, we demonstrate that P-PLS and even more β-PLS regressions outperform standard PLS1 in terms of cross-validation performances in the case where the number of calibration observations is largely lower than the number of variables in X .

      PubDate: 2018-05-31T11:00:29Z
       
  • Multivariate standard addition for the analysis of overlapping
           voltammetric signals in the presence of matrix effects: Application to the
           simultaneous determination of hydroquinone and catechol
    • Abstract: Publication date: 15 July 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 178
      Author(s): Kassandra Martínez, Cristina Ariño, José Manuel Díaz-Cruz, Núria Serrano, Miquel Esteban
      A multivariate version of the classical univariate standard addition method is tested as a proof of concept for the voltammetric analysis of complex samples generating overlapped signals in the presence of significant matrix effects. The proposed strategy applies a multivariate calibration method such as partial least squares (PLS) to the full voltammograms measured for the sample alone and after combined additions of a series of standard solutions (one for every analyte). Then, a calibration model is built and further applied to the prediction of the concentration added to a blank, i.e., a full voltammetric signal measured in the absence of analytes. The absolute value of such predicted concentration is taken as the concentration of the analyte in the sample. The method has been successfully tested in different natural water samples spiked with hydroquinone and catechol and appears to be a promising tool for the analysis of overlapped signals in complex matrices.
      Graphical abstract image

      PubDate: 2018-05-28T04:32:04Z
       
  • Solver, an Excel application to solve the difficulty in applying different
           univariate linear regression methods
    • Abstract: Publication date: 15 July 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 178
      Author(s): Miriam Delgado-Aguilar, Lucia Valverde-Som, Luis Cuadros-Rodríguez
      Different fitting methods to carry out a regression analysis could be applied. Ordinary least squares regression (OLS) is the most popular and used because it is easily accessible while the application of other methods generally requires specific statistical software. As consequence, OLS is profusely used without taking account its constrains which could provide erroneous predictions. The use of Solver, a default implemented Excel add-in, could overcome this drawback because it can be used without any knowledge in programming. Solver uses iterative methods to get the parameters which satisfy the imposed condition. This paper describes a valuable but straightforward methodology to apply Solver with a selected group of univariate linear regression methods. It has been applied to parametric regressions, which have more constraints but offer more reliable results, and robust regression which can allow more flexibility in the data characteristics. The result of the fitting provided by Solver for each regression has been checked and it is reliable enough.

      PubDate: 2018-05-28T04:32:04Z
       
  • HSIC-based kernel independent component analysis for fault monitoring
    • Abstract: Publication date: 15 July 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 178
      Author(s): Lin Feng, Tianran Di, Yingwei Zhang
      For nonlinear and non-Gaussian industrial processes, KICA is a mature and successful method for fault monitoring. However, a fixed-point ICA algorithm uses a negative entropy method, which has relatively high requirements for non-quadratic function and initial point; accuracy is unsatisfactory. The calculation of control limits of Hotelling's T2 statistic is based on a kernel density estimation, which is difficult to calculate and implement and sometimes cannot guarantee accuracy. In this paper, the kernel independent component analysis method based on the Hibert-Schmidt independence criterion (HSIC) is used instead of the fixed-point ICA algorithm for fault monitoring to improve the accuracy of the independent element. We obtain the independent element directly from the objective function, rather than through the combination of KPCA and ICA. At the same time, we use the direct binomial expansion theorem to obtain the control limit, which reduces computational complexity and implementation difficulty and improves accuracy. The control limit is improved to obtain multi-fault diagnosis. Gray-level information and color information of each frame of the video are respectively read through the information entropy and HSV spatial color histogram. Experimental results show the advantage and effectiveness of the proposed approach. Meanwhile, shadow variables are introduced to smooth the statistics. The contributions of this paper are as follows. 1) Using the kernel independent component method based on HSIC for fault monitoring improves speed and accuracy. 2) The binomial expansion theorem is used instead of traditional kernel density estimation to calculate the control limit, which improves the results of fault monitoring. 3) A method of fault detection using information entropy, HSV color histogram and multivariate statistical analysis is presented.

      PubDate: 2018-05-28T04:32:04Z
       
  • Computer aided compound identification based on a highly selective
           topological index
    • Abstract: Publication date: 15 July 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 178
      Author(s): Kaixia Xiao, Mengyao Chen, Tanfeng Zhao, Qingyou Zhang
      As the number of natural and synthesized compounds quickly increases, the management of compounds in large databases is becoming a challenging work. Especially for a compound that can be represented by several different graphs such as benzene can be drawn in single and double bonds, or aromatic bonds. These different graphs are the same for the chemists and are called equivalent chemical graphs or equivalent chemical structures herein (H atoms are found at more than one locations are not called equivalent chemical structures such as enol form and keto form). Researchers mostly hope that each group of equivalent chemical structures (represented by different graphs) could be denoted by the same value, and make them easy to be deal with. For this goal a highly discriminating index ATID (adjacent topology identification), which was derived from a graph theoretical index – 3-EAID, was suggested. In order to reduce the chance that the different compounds were mistaken for duplicate compounds, the uniqueness test of ATID was performed by over 60 million alkanes and over 19 million benzenoids with high similarity, and the results indicate that ATID possesses high discriminating ability. Finally, the ATID was successfully applied to retrieval of duplicate compounds in large databases. The results indicate that the ATID could be a valuable tool for chemical information administration. The ATID can give comparable results with InChI (International Chemical Identifier) dependent on atom number and derived from complicated rules.

      PubDate: 2018-05-28T04:32:04Z
       
  • Comparative study of mixture designs for complex phenomena
    • Abstract: Publication date: 15 July 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 178
      Author(s): M. Claeys-Bruno, C. Gomes, C. Bruno, M. Sergent
      In the field of formulation, the behaviour of phenomena studied are often complex, including non-linear or chaotic zones which make classical strategies based on polynomial models inappropriate. In these situations, uniform experimental designs and kriging models are recommended. Nonetheless, when researchers have to set up an experimental strategy, they must choice the class of designs to be constructed, the number of experiments to be performed and the modeling kriging parameters to be used. To help with these choices, we propose a catalogue combining various uniform designs characterized by several intrinsic quality criteria and to establish decision-making rules linking these criteria to the quality of the information obtained from the design.

      PubDate: 2018-05-28T04:32:04Z
       
  • Multivariate optimization of Pb(II) removal for clinoptilolite-rich tuffs
           using genetic programming: A computational approach
    • Abstract: Publication date: 15 June 2018
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 177
      Author(s): O. May Tzuc, A. Bassam, M. Abatal, Y. El Hamzaoui, A. Tapia
      In this study, a genetic programming (GP) model was developed to predict and optimize the Pb(II) removal capacity for natural, sodium, and acid-modified clinoptilolite-rich tuffs. Experimental process evaluated the sorption behavior of lead in aqueous solutions using unmodified and modified natural zeolite considering: the contact time, pH value, lead initial concentration, and sorbent dosage. The GP model was trained and tested with the experimental measurements and subsequently, compared with others multivariate analysis methods using three statistical criteria (coefficient of determination (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE)). The results indicate that GP getting the better performance achieving a fitness of R2 = 98.0%, RMSE = 5.06 × 10−2, and MAPE = 17.58%. Sensitivity analysis (SA) showed that the sorbent dosage was the most influential parameter with a sensitivity index of 0.219, following by the pH (0.059), and contact time (0.031). Based on GP model and SA, a multivariate optimization was conducted to compute the adequate conditions for a required sorption efficiency (98%). Optimize values were obtained at 0.10 g of sorbent mass, pH 5.0, 300.0 mg L−1, and 5.1 min contact time for natural clinoptilolite-rich tuffs; 0.65 g of sorbent mass, pH 5.0, 400.0 mg L−1, and 3.6 min contact time for sodium modified clinoptilolite-rich tuffs; and 0.65 g of sorbent mass, pH 3.0, 400.0 mg L−1, and 71.6 min contact time for acid modified clinoptilolite-rich tuffs. The computational approach presented can perform an assessment with errors less than 6%, indicating that it is a promising tool for the modeling and optimization of the sorption onto zeolite materials minimizing the time and operation cost. The proposed methodology can be used to take appropriate actions in the removing of this toxic heavy metal from the water. Besides, it can be implemented in studies corresponding to other sorption processes or similar.

      PubDate: 2018-05-28T04:32:04Z
       
  • Survival forest with partial least squares for high dimensional censored
           data
    • Abstract: Publication date: Available online 26 May 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Lifeng Zhou, Hong Wang, Qingsong Xu
      Random forest and partial least squares have proved wide applicability in numerous contexts. However, the combination of these versatile tools has seldom been studied. Inspired by a relatively new decision tree ensemble called rotation forest, we introduce a new survival ensemble algorithm using partial least squares regression and the Buckley-James estimator within the framework of random forest. First, the approach taken to cope with the high dimensionality is to reduce the dimension by a random subspace method. Then, censored survival times are imputed by the Buckley-James estimator. After dimension reduction and time imputation, partial least squares regression is applied to extract the features. Similar to rotation forest, all extracted components are used as covariates in a bagged survival tree to predict the survival probabilities. Experimental results on a variety of simulation and real datasets demonstrate that the proposed approach is a strong competitor to other popular survival prediction models under high or ultra-high dimensional setting.

      PubDate: 2018-05-28T04:32:04Z
       
  • Sequential “asymmetric” d-optimal designs: A practical solution in
           case of limited resources and not equally expensive experiments
    • Authors: D. Copelli; A. Falchi; M. Ghiselli; E. Lutero; R. Osello; D. Riolo; F. Schiaretti; R. Leardi
      Abstract: Publication date: Available online 25 April 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): D. Copelli, A. Falchi, M. Ghiselli, E. Lutero, R. Osello, D. Riolo, F. Schiaretti, R. Leardi
      When the cost of an experiment depends on the values of the variables not all the experiments are equally expensive. Since in all the standard Designs of Experiments the experiments are symmetrically distributed, in case of finite resources the number of experiments is limited by the cost of the most expensive ones. The approach here shown produces asymmetrical designs, with an increase of the number of the cheapest experiments and a decrease of the number of the expensive ones. Compared to the standard designs having the same total cost, the information obtained by this strategy is slightly worse in the region corresponding to the most expensive experiments, but much better in the region corresponding to the cheapest experiments. This approach was successfully applied to the micronization of an Active Pharmaceutical Ingredient.

      PubDate: 2018-05-02T03:01:04Z
      DOI: 10.1016/j.chemolab.2018.04.017
       
  • Multivariate evaluation of the effect of the particle size distribution of
           an active pharmaceutical ingredient on the performance of a pharmaceutical
           drug product: A real-case study
    • Authors: D. Copelli; A. Cavecchi; C. Merusi; R. Leardi
      Abstract: Publication date: Available online 18 April 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): D. Copelli, A. Cavecchi, C. Merusi, R. Leardi
      In the pharmaceutical field, and in particular for inhalation drug products based on Dry Powder Inhaler, the Active Pharmaceutical Ingredient particle size distribution is one of the key parameters to drive the final drug product performance. In this paper the impact of the Active Pharmaceutical Ingredient particle size on the Aerodynamic Particle Size Distribution of the final drug product was evaluated by applying different multivariate approaches. By using both the commonly employed particle size distribution descriptors (D10, D50, D90 and SPAN) and the whole particle size distribution curves it has been demonstrated that the latter gives an information which is easier to understand and interpret. Finally, models estimating the effects of the Active Pharmaceutical Ingredient particle size distribution, device life and drug product dosage on the Aerodynamic Particle Size Distribution of the final drug product were also established.

      PubDate: 2018-04-25T02:40:13Z
      DOI: 10.1016/j.chemolab.2018.04.013
       
  • A Modified Moving Window dynamic PCA with Fuzzy Logic Filter and
           application to fault detection
    • Authors: Mustapha Ammiche; Abdelmalek Kouadri; Abderazak Bensmail
      Abstract: Publication date: Available online 13 April 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Mustapha Ammiche, Abdelmalek Kouadri, Abderazak Bensmail
      Principal Component Analysis (PCA) model is constructed from measured data and used to monitor new testing samples. In fact, the statistical independency assumption between observations is true only for long sampling intervals. Nowadays, industrial systems are sophisticated and fast for which this assumption becomes no longer valid and the current observation becomes highly dependent on the past observations. In another hand, Dynamic PCA (DPCA) is a PCA extension to deal with the aforementioned problem, but monitoring process using this method with fixed control limits showed a high False Alarms Rate (FAR), high Missed Detection Rate (MDR) and long Detection Time Delay (DTD). In this paper, a Modified Moving Window DPCA (MMW-DPCA) with Fuzzy Logic Filter (FLF) is proposed to address the above issue. The developed monitoring scheme continually updates control limits throughout an obtained DPCA-based model. The adaptive thresholds are established by moving a fixed size window over the data. The dynamic behavior of the data is handled by DPCA, whereas the sensitivity enhancement and the FAR reduction are handled by the developed adaptive thresholds for which the FLF is employed to ensure robustness to outliers and noise without affecting the fault detection performances. The proposed technique has been tested on Tennessee Eastman Process (TEP). It has been compared to other well-known fault detection methods. The obtained results demonstrate that the MMW-DPCA with FLF detects different types of faults with high accuracy and in a short time delay. The experimental application of the MMW-DPCA with FLF has been carried out on cement rotary kiln. The obtained results illustrate that the proposed method has successfully detected a real fault.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.04.012
       
  • Support vector machine with truncated pinball loss and its application in
           pattern recognition
    • Authors: Liming Yang; Hongwei Dong
      Abstract: Publication date: Available online 10 April 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Liming Yang, Hongwei Dong
      Support vector machine(SVM) with pinball loss(PINSVM) has been recently proposed and shown its advantages in pattern recognition. In this paper, we present a robust bounded loss function (called L t -loss) that truncates pinball loss function. Then a novel robust SVM formulation with L t -loss(called TPINSVM) is proposed to enhance noise robustness. Moreover, we demonstrate that the proposed TPINSVM satisfies Bayes rule and it has a certain sparseness. However, the non-convexity of the proposed TPINSVM makes it difficult to optimize. We develop a continuous optimization method, DC(difference of convex functions) programming method, to solve the proposed TPINSVM. The resulting DC optimization algorithm converges finitely. Furthermore, the proposed TPINSVM is directly applied to recognize the purity of hybrid maize seeds using near-infrared spectral data. Experiments show that the proposed method achieves better performance than the traditional methods in most spectral regions. Meanwhile we simulate the proposed TPINSVM in benchmark datasets in different situations. In noiseless setting, the proposed TPINSVM either improves or shows no significant difference in generalization compared to the traditional approaches. While in noise situations, TPINSVM improves generalization in most cases.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.04.003
       
  • In-situ particle segmentation approach based on average background
           modeling and graph-cut for the monitoring of l-glutamic acid
           crystallization
    • Authors: Zhi M. Lu; Fan C. Zhu; Xue Y. Gao; Bing C. Chen; Tao Liu; Zhen G. Gao
      Abstract: Publication date: Available online 9 April 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Zhi M. Lu, Fan C. Zhu, Xue Y. Gao, Bing C. Chen, Tao Liu, Zhen G. Gao
      The crystal morphology often represents a critically important property for the reason that different morphological characteristics such as crystal shape and size distribution will directly affect crystal growth and the quality of final products. However, minor changes in reaction conditions can have a significant impact on crystal morphology. As a consequence, crystallization processes demand an on-line technique for real-time monitoring of crystal morphology. Image-based monitoring methods show a great potential for real-time monitoring, and performing particle segmentation accurately becomes a key issue in the particle image analysis. To avoid the influences of droplets and to eliminate the particle shadow of particle images obtained by invasive imaging system, an effective approach is proposed for particle segmentation based on combing the background difference method and the graph-cut based local threshold method. Through building the background model and performing subtraction between particle image and background model, droplets could be eliminated effectively. Then the local threshold method is performed further to eliminate the influences of particle shadow. Several particle images are used to demonstrate the efficiency and accuracy of the proposed method. In order to further evaluate the real-time performance of the proposed method, comparison experiments between the proposed method and other advanced or classic algorithms are presented. Experimental results show that the proposed method is efficient and has an impressive real-time performance.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.04.009
       
  • Detecting and classifying minor bruised potato based on hyperspectral
           imaging
    • Authors: Dandan Ye; Laijun Sun; Wenyi Tan; Wenkai Che; Mingcan Yang
      Abstract: Publication date: Available online 4 April 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Dandan Ye, Laijun Sun, Wenyi Tan, Wenkai Che, Mingcan Yang
      Potato with minor bruise is difficult to be detected in the process of damage identification and is perishable in storage, thus leading to a serious problem of food safety and economic issue. In view of the complexity of bruise detection for potatoes, a nondestructive detection method, based on hyperspectral imaging technique, was proposed in this study. All samples, including healthy potatoes and bruised potatoes belonging to 3 different levels, were taken as experiment objects. First of all, the background in every hyperspectral image was removed by masking aiming at acquiring the average spectra of each potato. Then, Savitzky-Golay smoothing, first derivative, second derivative, standard normal variate and its combinatorial methods were applied to pre-process spectral data, respectively, and the grid search algorithm was applied to optimize modeling parameters. Confirm that the standard normal variate pre-processing technique reinforced the model performance at utmost, and the identification accuracy of bruised samples reached 90.63%. In addition, given the interference of redundant information, the optimized simulated annealing algorithm based on correlation coefficient algorithm was applied to reduce the dimension of the spectral data, which promoted the identification accuracy of bruised samples to 96.88%. Furthermore, the bruise levels of samples were classified using above methods, and the higher classification accuracy of 100% was achieved. The results indicated that potato with minor bruise could be identified accurately and effectively and the bruise levels could be classified by the hyperspectral imaging technique, which provided an idea for on-line and non-destructive testing of potato.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.04.002
       
  • Birnbaum-Saunders spatial regression models: Diagnostics and application
           to chemical data
    • Authors: Fabiana Garcia-Papani; Víctor Leiva; Miguel A. Uribe-Opazo; Robert G. Aykroyd
      Abstract: Publication date: Available online 29 March 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Fabiana Garcia-Papani, Víctor Leiva, Miguel A. Uribe-Opazo, Robert G. Aykroyd
      Geostatistical modelling is widely used to describe data with spatial dependence structure. Such modelling often assumes a Gaussian distribution, an assumption which is frequently violated due to the asymmetric nature of variables in diverse applications. The Birnbaum-Saunders distribution is asymmetrical and has several appealing properties, including theoretical arguments for describing chemical data. This work examines a Birnbaum-Saunders spatial regression model and derives global and local diagnostic methods to assess the influence of atypical observations on the maximum likelihood estimates of its parameters. Modelling and diagnostic methods are then applied to experimental data describing the spatial distribution of magnesium and calcium in the soil in the Parana state of Brazil. This application shows the importance of such a diagnostic analysis in spatial modelling with chemical data.

      PubDate: 2018-04-15T09:57:29Z
      DOI: 10.1016/j.chemolab.2018.03.012
       
  • An online sequential multiple hidden layers extreme learning machine
           method with forgetting mechanism
    • Authors: Dong Xiao; Beijing Li; Shengyong Zhang
      Abstract: Publication date: Available online 7 February 2018
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Dong Xiao, Beijing Li, Shengyong Zhang
      In many practical applications, training data are presented one-by-one or chuck-by-chuck and also have the property of timeliness very frequently. The ensemble of an online sequential extreme learning machine (EOS-ELM) can learn data one-by-one or chunk-by-chunk with fixed or varying chunk size. The online sequential extreme learning machine with forgetting mechanism (FOS-ELM) can learn data with the property of timeliness. In many practical applications, such as stock forecasting or weather forecasting, the training accuracy can be improved by discarding the outdated data and reducing the influence on later training processes. Since the real-time variations of data are accompanied by a series of unavoidable noise signals, to make the training output closer to the actual output, an online sequential multiple hidden layers extreme learning machine with forgetting mechanism (FOS-MELM) is proposed in this paper. The proposed FOS-MELM can retain the advantages of FOS-ELM, eliminate the influence of unavoidable noise and improve the prediction accuracy. In this work, experiments have been completed on chemical (styrene) data. The experimental results show that FOS-MELM has high accuracy, better stability and better short-term prediction than FOS-ELM.

      PubDate: 2018-02-26T08:05:50Z
      DOI: 10.1016/j.chemolab.2018.01.014
       
 
 
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