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  Subjects -> COMPUTER SCIENCE (Total: 1993 journals)
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COMPUTER SCIENCE (1157 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: 13)
Abakós     Open Access   (Followers: 3)
Academy of Information and Management Sciences Journal     Full-text available via subscription   (Followers: 69)
ACM Computing Surveys     Hybrid Journal   (Followers: 22)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 9)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 11)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 13)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 3)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 20)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 11)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 26)
Advanced Science Letters     Full-text available via subscription   (Followers: 7)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 16)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Computer Science : an International Journal     Open Access   (Followers: 13)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 37)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 1)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 7)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 4)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 7)
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: 3)
Annals of Data Science     Hybrid Journal   (Followers: 9)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 12)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 2)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 14)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 1)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 32)
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)
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: 125)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 6)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access  
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 3)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 9)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 308)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 17)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 31)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 46)
British Journal of Educational Technology     Hybrid Journal   (Followers: 123)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 2)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 14)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
Cell Communication and Signaling     Open Access   (Followers: 1)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access  
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 12)
Circuits and Systems     Open Access   (Followers: 16)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Communication Methods and Measures     Hybrid Journal   (Followers: 11)
Communication Theory     Hybrid Journal   (Followers: 20)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 53)
Communications of the Association for Information Systems     Open Access   (Followers: 18)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access  
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
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: 14)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 13)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 31)
Computer     Full-text available via subscription   (Followers: 84)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 7)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 22)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 10)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 1)
Computer Music Journal     Hybrid Journal   (Followers: 16)
Computer Physics Communications     Hybrid Journal   (Followers: 6)
Computer Science - Research and Development     Hybrid Journal   (Followers: 7)
Computer Science and Engineering     Open Access   (Followers: 17)
Computer Science and Information Technology     Open Access   (Followers: 11)
Computer Science Education     Hybrid Journal   (Followers: 12)
Computer Science Journal     Open Access   (Followers: 20)
Computer Science Master Research     Open Access   (Followers: 10)
Computer Science Review     Hybrid Journal   (Followers: 10)

        1 2 3 4 5 6 | Last

Journal Cover Chemometrics and Intelligent Laboratory Systems
  [SJR: 0.697]   [H-I: 92]   [15 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0169-7439
   Published by Elsevier Homepage  [3044 journals]
  • Quantification of dopamine in biological samples by surface-enhanced Raman
           spectroscopy: Comparison of different calibration models
    • Abstract: Publication date: Available online 12 September 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Cai-Xia Shi, Zeng-Ping Chen, Yao Chen, Qing Liu, Ru-Qin Yu
      In this contribution, an attempt was made to quantify dopamine (DA) in complex biological samples (i.e., plasma and urine) using surface-enhanced Raman spectroscopy. Silver nanoparticles modified with iron-nitrilotriacetic acid (DA-selective probe) and 4-mercaptobenzoic acid (internal standard) were used as enhancing substrate. The performance of different calibration models for the quantification of DA in plasma and urine samples spiked with DA was evaluated and compared. The calibration models investigated were univariate ratiometric model based on the intensity ratio between the characteristic SERS peaks of DA and 4-mercaptobenzoic acid, PLS models built on the raw, standardized or pre-processed SERS measurements, and multiplicative effects model for surface-enhanced Raman spectroscopy (MEMSERS). Experimental results showed that among the models considered in this contribution, only MEMSERS achieved quite accurate and precise concentration predictions for DA in the spiked plasma and urine samples with recovery rates varying within the range of 91.9–112%, surprisingly better than the corresponding values of the quantitative results obtained by LC-MS/MS. This work provided further evidence of the effectiveness of MEMSERS in solving the problem of poor accuracy of quantitative SERS assays.

      PubDate: 2017-09-14T04:54:50Z
  • Rapid identification of milk samples by high and low frequency unfolded
           partial least squares discriminant analysis combined with near infrared
    • Abstract: Publication date: Available online 12 September 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Xihui Bian, Caixia Zhang, Peng Liu, Junfu Wei, Xiaoyao Tan, Ligang Lin, Na Chang, Yugao Guo
      A high and low frequency unfolded partial least squares discriminant analysis (HLFUPLS-DA) for building a pattern recognition model of near-infrared (NIR) spectra is proposed to identify milk samples. In the approach, the spectra are decomposed into different frequency components by empirical mode decomposition (EMD) at first. Then the former high frequency components are summed as a high frequency matrix and vice versa. Thirdly, the high and low frequency matrices are extended to an extended matrix in the variable dimension. Finally, PLS-DA model is built between the extended matrix and the target vectors. Coupled with NIR spectroscopy, HLUPLS-DA is applied to identify milk samples of different qualities. Comparing with PLS-DA and other signal processing techniques combined with PLS-DA, the proposed method is proved to be a promising tool for spectral qualitative analysis of complex samples.

      PubDate: 2017-09-14T04:54:50Z
  • Comparing unfolded and two-dimensional discriminant analysis and support
           vector machines for classification of EEM data
    • Abstract: Publication date: Available online 8 September 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Camilo L.M. Morais, Kássio M.G. Lima
      Three-way data has been increasingly used in chemical applications. However, few algorithms are capable of properly classifying this type of data maintaining its original dimensions. Unfolding procedures are commonly employed to reduce the data dimension and enable its classification using first order algorithms. In this paper, modified versions of two-dimensional principal component analysis with linear discriminant analysis (2D-PCA-LDA), quadratic discriminant analysis (2D-PCA-QDA), and support vector machines (2D-PCA-SVM) have been proposed to classify three-way chemical data. Applications were performed for two-category classification using fluorescence excitation emission matrix (EEM) of simulated and three real data sets, in which the performance of the proposed algorithms were compared with regular PCA-LDA, PCA-QDA and PCA-SVM using unfolding proceedings. The results show that 2D algorithms had equal or superior classification performance in the four data sets analyzed, thus indicating its ability to classify this type of data.
      Graphical abstract image

      PubDate: 2017-09-14T04:54:50Z
  • Targeted multivariate adulteration detection based on fatty acid profiles
           and Monte Carlo one-class partial least squares
    • Abstract: Publication date: Available online 8 September 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Liangxiao Zhang, Zhe Yuan, Peiwu Li, Xuefang Wang, Jin Mao, Qi Zhang, Chundi Hu
      To develop effective adulteration detection methods is essential as food quality and safety draw particular concern all over the world. In this study, Monte Carlo one-class partial least squares (MCOCPLS) was proposed and employed as a novel one class classification model for authentication identification by using virgin olive oil (VOO) as an example. Monte Carlo sampling was proposed for selecting variable subspace to improve the performance of one-class partial least squares (OCPLS) classifier. MCOCPLS was used to establish a one-class model, the performance of which was validated by an independent test set consisting of 5000 adulterated oils simulated by the Monte Carlo method. The prediction for the best model of MCOCPLS reaches a correct rate of 99.10%. Moreover, authentic VOOs were analyzed and assessed for the adulteration risk. In conclusion, the proposed MCOCPLS method could be used to effectively detect olive oils adulterated with other vegetable oils at a concentration of as low as 3%. Therefore, MCOCPLS provides an effective tool and new insights in adulteration detection for edible oils and other foods.

      PubDate: 2017-09-14T04:54:50Z
  • Standardized maximim D-optimal designs for enzyme kinetic inhibition
    • Abstract: Publication date: Available online 6 September 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Ping-Yang Chen, Ray-Bing Chen, Heng-Chin Tung, Weng Kee Wong
      Locally optimal designs for nonlinear models require a single set of nominal values for the unknown parameters. An alternative is the maximin approach that allows the user to specify a range of values for each parameter of interest. However, the maximin approach is difficult because we first have to determine the locally optimal design for each set of nominal values before maximin types of optimal designs can be found via a nested optimization process. We show that particle swarm optimization (PSO) techniques can solve such complex optimization problems effectively. We demonstrate numerical results from PSO can help find, for the first time, formulae for standardized maximin D-optimal designs for nonlinear model with 3 or 4 parameters on the compact and nonnegative design space. Additionally, we show locally and standardized maximin D-optimal designs for inhibition models are not necessarily supported at a minimum number of points. To facilitate use of such designs, we create a web-based tool for practitioners to find tailor-made locally and standardized maximin optimal designs.

      PubDate: 2017-09-08T08:46:06Z
  • Highly selective atomic chiral index and its application to automatic
           assignment of chiral centers in chiral compounds
    • Abstract: Publication date: Available online 6 September 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Kaixia Xiao, Mengyao Chen, Tanfeng Zhao, Yanmei Zhou, Xiaoqiang Liu, Qingyou Zhang, Lu Xu
      As the requirement of chiral compounds increases, the studies on asymmetric catalytic synthesis and separation are of interest. For these kinds of researches, identifications of chiral centers are often required. However, it's time-consuming, especially for the compounds with multiple chiral centers. Thus, a method to automatically identify chiral center based on highly selective atomic chiral index was suggested in this paper. At first, an atomic chiral index—aEAID was suggested based on molecular index EAID (one of the most highly discriminating indices). The uniqueness test of aEAID was performed by a virtual data set of 3,851,864 atoms, no degeneracy occurred. However, the uniqueness test was performed by NCI database of 3,814,521 atoms (NCI database were taken from the network), 12 pairs of degenerated atoms within 7 pairs of molecules were found. In order to improve the discriminating ability of aEAID, a distance factor was introduced into aEAID to generate a new highly selective atomic chiral index—d-aEAID. The two atomic data sets above were also used to test the uniqueness of the index—d-aEAID, and there was no degeneration. Furthermore, the two atomic indices, i.e., aEAID and d-aEAID, were applied to automatic identification of chiral centers for a total of more than 100,000 chiral compounds for three data sets (each compound possesses 1–38 chiral centers), and both atomic indices correctly identified all the chiral centers.

      PubDate: 2017-09-08T08:46:06Z
  • Chemometric algorithms for analyzing high dimensional temperature
           dependent near infrared spectra
    • Abstract: Publication date: Available online 6 September 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Xiaoyu Cui, Jin Zhang, Wensheng Cai, Xueguang Shao
      High dimensional data analysis has gained widespread acceptance with the rapid development of analytical instruments and experimental techniques. Benefiting from the second–order advantage, high order chemometric algorithms have shown a great ability to match the nature of data and extract the latent components from the data. In this study, multiway principal component analysis (NPCA), parallel factor analysis (PARAFAC) and alternating trilinear decomposition (ATLD) were employed, respectively, to extract the information from temperature dependent near infrared (NIR) spectra of alcohol aqueous solutions. The variations of the structure induced by temperature and concentration in the solutions were analyzed by the three algorithms. Spectral features can be observed from the loadings obtained by NPCA, which explain the maximum variances. Spectral profiles computed by PARAFAC and ATLD contain the spectral information of the components. The former prefers to show the information of ethanol, water and ethanol–water cluster, while the latter opts for describing the information of the ethanol and different water clusters in the solution. However, all the three algorithms are able to capture the quantitative information from the spectra. Therefore, high order chemometric algorithms may provide powerful tools for analyzing temperature dependent NIR spectra to obtain the structural and quantitative information of the aqueous solutions.
      Graphical abstract image

      PubDate: 2017-09-08T08:46:06Z
  • A reliable multiclass classification model for identifying the subtypes of
           parotid neoplasms constructed with variable combination population
           analysis and partial least squares regression based on Raman spectra
    • Abstract: Publication date: Available online 6 September 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Yongning Yang, Fanfan Xie, Bing Yan, Yi Li, Junmei Xu, Yuan Liu, Zhining Wen, Menglong Li
      Pleomorphic adenoma (PA), Warthin's tumor (WT) and mucoepidermoid carcinoma (MEC) are three common subtypes of salivary gland tumors, for which the occurrence site is located in the parotid gland. Accurately diagnosing the subtypes of parotid tumors plays a vital role in the surgical treatment. Unfortunately, the current studies mainly focus on the binary classification of parotid tumors. The preoperative multi-classification of them has still been underexplored. For the purpose of broadening the application area of the predictive models and facilitating the clinical preoperative diagnosis, we suggested a multi-classification model, which was constructed by combining the variable combination population analysis (VCPA) algorithm with the partial least squares regression (PLSR), to simultaneously discriminate the three subtypes of parotid tumors as well as the normal parotid gland tissue based on the Raman spectra of the tissue samples. In addition, we investigated the impact of generating Raman spectra from different sampling locations on the reliability of the predictive models. For the validation set, the overall accuracy in predicting the subtypes of parotid tumors and the normal parotid gland tissue was 0.867. Similarly, the accuracies achieved by the models constructed with the Raman spectra from two different sampling locations were 0.877 and 0.883, respectively, indicating the minor influence of the sampling locations on the predictive models. Our findings can be helpful for establishing the method of rapidly diagnosing the salivary gland tumors preoperatively in clinics. Moreover, the characteristic wavenumbers used in model construction were highly associated with the variations of the structures and contents of nucleic acids, collagen, proteins, lipids and DNA/RNA in gland tissue, which revealed the mainly difference among three types of parotid tumors and can be conductive to better understanding the molecular mechanisms of them.

      PubDate: 2017-09-08T08:46:06Z
  • PyDescriptor: A new PyMOL plugin for calculating thousands of easily
           understandable molecular descriptors
    • Abstract: Publication date: 15 October 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 169
      Author(s): Vijay H. Masand, Vesna Rastija
      The field of Quantitative Structure-Activity Relationship (QSAR) relies heavily on molecular descriptors. Among various guidelines suggested by Organisation for Economic Co-operation and Development (OECD), a very important guideline demands the mechanistic interpretation of a QSAR model. For this, a very attractive idea is to build a QSAR model using easily understandable molecular descriptors. To address this important issue, in the present work, we present an innovative chem-informatics tool, PyDescriptor. It can calculate a diverse pool of 11,145 molecular descriptors comprising easily understandable 1D- to 3D- descriptors encoding pharmacophoric patterns, atomic fragments and a variety of fingerprints. It is a new Python based plugin implemented within the commonly used visualization software PyMOL. PyDescriptor has several advantages like easy to install, open source, works on all major platforms (Windows, Linux, MacOS), easy to use through graphical user interface (GUI) and command-line, and output is saved in comma separated values (CSV) file format for further QSAR procedure. The plugin is freely available for academia.
      Graphical abstract image

      PubDate: 2017-09-02T08:34:10Z
  • Mixture of D-Vine copulas for chemical process monitoring
    • Abstract: Publication date: 15 October 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 169
      Author(s): Wenjing Zheng, Xiang Ren, Nan Zhou, Da Jiang, Shaojun Li
      Complex dependence structures often exist in chemical process multivariate data. Although they are difficult to capture, vine copula shows good performance and stronger flexibility in depicting the highly nonlinear dependencies. To identify and fully understand the complex dependence patterns in multivariate data, a mixture of D-vine copulas (MDVC) is proposed. By using the expectation-maximization (EM) algorithm and stepwise semi-parametric (SSP) estimation for parameter estimation, the proposed model can depict the complex dependence structures in multivariate data. The highest density region (HDR) and the density quantile approach (DQA) are both used to construct the highest density region distance-based probability (HDRP) index to achieve a real-time process fault monitoring. The effectiveness and benefits of the proposed model are illustrated with a numerical example, the Tennessee Eastman (TE) benchmark process and a real acetic acid dehydration distillation system for fault detection. The results show that the proposed mixture of D-vine copulas can achieve good performance in chemical process fault monitoring.

      PubDate: 2017-09-02T08:34:10Z
  • Stability analysis of hyperspectral band selection algorithms based on
           neighborhood rough set theory for classification
    • Abstract: Publication date: 15 October 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 169
      Author(s): Yao Liu, Junjie Yang, Yuehua Chen, Kezhu Tan, Liguo Wang, Xiaozhen Yan
      Band selection is a well-known approach for reducing the dimensionality of hyperspectral data. When the neighborhood rough set theory is used to select informative bands, different criteria of the band selection algorithms may lead to different optimal band subsets. Many studies have been analyzed the classification performance of band selection algorithms and have demonstrated that different algorithms are similar for classification. Therefore, rather than evaluating band selection algorithms using only classification accuracy, their stability should also be explored. The stability of an algorithm, which is quantified by the sensitivity of the algorithm to variations in the training set, is a topic of recent interest. Most studies on stability compare the band subsets chosen either from perturbation datasets by randomly removing methods or from perturbation datasets by cross validation methods. These methods either result in an unknown degree of overlap between perturbation datasets, or an invariable degree of overlap. In this work, we propose an adjustable degree of overlap method to construct perturbation datasets, which can set different levels of perturbation. By introducing the Jaccard index as a metric of stability, we explore the stability of six band selection algorithms based on the neighborhood rough set theory. We experimentally demonstrate that the level of perturbation, the degree of overlap, the size of the subset, and the size of the neighborhood affect stability. The results show that the maximal relevance minimal redundancy difference band selection algorithm has the greatest stability overall and better classification ability.

      PubDate: 2017-09-02T08:34:10Z
  • Full validation using β-content, γ-confidence tolerance interval:
           Application for LC-MS/MS determination of Doxycycline in human plasma
    • Abstract: Publication date: 15 September 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 168
      Author(s): Hasnaa Haidara, Taoufiq Saffaj, Aimin Tan, Abdeslam Bentama, Wafaa Outmoulait, Yassine Hameda Benchekroun, Bouchaib Ihssane
      A statistical strategy for full analytical validation has been applied to ensure the reliability of a bioanalytical method and to control the risk associated with future use. To this end, the applicability of this approach, called uncertainty profile, has been demonstrated by evaluating the liquid chromatography–mass spectrometry/mass spectrometry (LC-MS/MS) bioanalysis, for the determination of Doxycycline in human plasma. This innovative procedure combines two main concepts, namely analytical validation and measurement uncertainty; its aim is to guarantee that a known amount of future results obtained with the bioanalytical method will be within the acceptance limits set beforehand. In addition, this approach allows to calculate the measurement uncertainty of the method without any additional effort, by using data coming from the analytical validation when we respect as best as possible the intermediate precision conditions at each concentration level. Thus, the LC-MS/MS method for the determination of Doxycycline in human plasma was found to be valid in the studied concentrations range since the tolerance intervals of type β-content, γ-confidence fell into the acceptable limits of ±15%, and the relative expanded uncertainty did not exceed 11% of the values of β-proportion and α-risk equal to 90% and 5% respectively.

      PubDate: 2017-09-02T08:34:10Z
  • Distributed plant-wide process monitoring based on PCA with minimal
           redundancy maximal relevance
    • Abstract: Publication date: Available online 30 August 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Chen Xu, Shunyi Zhao, Fei Liu
      Generally, the plant-wide processes involve abundant measured variables that have complex relationship, and considerable information of variables should be concerned in the process of block division. In this paper, a new distributed monitoring scheme that integrates minimal redundancy maximal relevance (mRMR), Bayesian inference and principal component analysis (PCA) is proposed for plant-wide processes. This method considers not only the relevance between variables, but also their redundancy in block division. Once sub-PCA monitoring models are built in each subblock, the overall results are combined through the Bayesian inference. With a numerical example and the Tennessee Eastman benchmark process, the effectiveness of the proposed method is demonstrated.

      PubDate: 2017-09-02T08:34:10Z
  • A similarity elastic window based approach to process dynamic time delay
    • Abstract: Publication date: Available online 26 August 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Bo Yang, Hongguang Li
      Due to a large number of correlated process variables involved in industrial processes, dynamic characteristics of time delays between correlated process variables are generally major concerns. Traditionally, the time delay is approximately estimated by static sliding time windows, which could not better deal with the dynamics of time delays. In response to this problem, this paper proposes a dynamic time delay analysis (e-DTA, dynamic time delay analysis by elastic windows) method based on similarity elastic windows, which is aiming at effectively estimating the transfer time delay between process variables. According to contrast similarities between correlated variables, the size of the elastic window is self-tuned and the dynamic delay time can be estimated offline. Subsequently, through an additional correlation analysis for time series of the time delay estimated from historical data, main variables influencing the time delay can be obtained. By providing relevant trend variables, an improved fuzzy interpolation prediction method is suggested to estimate the transfer time delay between process correlated variables online. In addition, an e-DTA dynamic directed time graph is created by combining dynamic transfer time delays of mutually dependent variables. Finally, performances of the e-DTA method are tested through a numerical study and a distillation column simulation.

      PubDate: 2017-09-02T08:34:10Z
  • A new model selection criterion for partial least squares regression
    • Abstract: Publication date: Available online 26 August 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): José L. Martínez, Helton Saulo, Humberto Barrios Escobar, Jeremias Leao
      Choosing the right number of latent factors to be used in PLS regression (Partial Least Squares Regression) has been a matter of concern among users, academics and researchers. In this paper, we introduce a statistic to select the appropriate number of latent factors according to the model predictive ability. This method is based on the Predicted Residual Error Sum of Squares (PRESS) for PLS regression. Our mathematical development is based on matrix calculations obtained from the orthogonal vectors that compose the matrix of latent factors. Currently, the leave-one-out method is widely used for this, where one observation is left out and then the regression model is estimated. This technique is repeated as many times as the number of observations. The advantage of using the PRESS statistic for PLS regression (P-PLS), developed in this work, is to have the possibility of selecting the best predictive model straightforwardly. Additionally, the P-PLS can be used for analyzing the impact caused by the ith observation on the PLS regression vector of coefficients, as well as for detecting other kinds of data that affect the model.

      PubDate: 2017-09-02T08:34:10Z
  • New insights to improve resolution and reliability of Raman spectral
           analysis using higher-density multiscale regression
    • Abstract: Publication date: Available online 26 August 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Xi Han, Zhi-xuan Huang, Su-fang Wang, Xiao-dong Chen, Qi-feng Li, Ke-xin Xu, Da Chen
      When paired with sophisticated multivariate calibration methods, spectroscopy techniques stand out conspicuously for high-throughput analysis of complex samples. However, despite the identification capability of multivariate analysis, fluctuant spectral interference and complicated chemometrics procedure often combine to limit conventional spectroscopy techniques. In this regard, we proposed a novel strategy, higher-density multiscale regression (HDMR), to adaptively process Raman spectra for quantitative analysis. HDMR firstly splits the Raman spectra into frequency components at different scales using higher-density discrete wavelet transform (HDDWT), yielding a larger number of subbands to benefit a good separation of spectral bands. Parallel member models constructed with these decomposed components are then fused into a final prediction through partial least square (PLS) reweighting strategy. With HDMR, the pre-treatment process and multivariate calibration are integrated into a unified calibration model for Raman spectra at hand, regardless of its structural characteristics. This would definitely avoid of information leakage in multivariate calibration, thus promoting the application of Raman spectroscopy technique as a general tool for analytical chemistry. To validate the performance of the HDMR, two industrial Raman spectral data sets were investigated to yield challenges representative of those encountered in Raman spectroscopy. This proposed strategy has improved calibration performance through the reweighting way. Satisfactory calibration results suggest that HDMR provides a universal tool for reliable Raman spectral analysis, which may well extend to other spectral data sets.

      PubDate: 2017-09-02T08:34:10Z
  • msktuple: An Integrated R library for alignment-free multiple sequence
           k-tuple analysis
    • Abstract: Publication date: Available online 3 August 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Saeid Amiri, Ivo D. Dinov
      Recently alignment-free sequence comparison methods based on promoter-frequency distance measures have gained popularity. This paper reports on the implementation and validation of several alignment-free sequence analysis methods for representing and quantifying between-sequence distances and sequence variability. The msktuple library includes the following sequence comparison techniques: locational k-tuple, naive k-tuple, CV-Tree, and their ensemble variants. These metrics are used to determine the dissimilarity between sequences using k-letter words. In support of open-science, we provide open-source software, R-scripts, and protocols implementing the new techniques. These tools will support collaboration, enable independent validation, promote result reproducibility and enable tool interoperability.

      PubDate: 2017-08-03T06:45:39Z
  • Slow feature analysis based on online feature reordering and feature
           selection for dynamic chemical process monitoring
    • Abstract: Publication date: Available online 1 August 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Jian Huang, Okan K. Ersoy, Xuefeng Yan
      This study considers the insufficiency of traditional monitoring methods to eliminate dynamics, and proposes a novel online feature reordering- and feature selection-based slow feature analysis (SFA) algorithm. The SFA algorithm explores the process dynamics from the view of inner variation of data to extract the slowly varying features. The extracted SFs are considered as the representations of steady- and dynamic-state processes. Online feature reordering and feature selection strategies maximize online fault information and can be used to perform fault detection operation. The proposed method is applied to two simulated processes. Monitoring results show that the proposed method has better monitoring results than those of traditional methods.

      PubDate: 2017-08-03T06:45:39Z
  • Stationarity test and Bayesian monitoring strategy for fault detection in
           nonlinear multimode processes
    • Abstract: Publication date: Available online 29 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Shumei Zhang, Chunhui Zhao
      Both of multimode and nonlinear characteristics have been common in modern industrial processes. In the present work, an improved monitoring strategy on the basis of a modified discriminant locality preserving projections (DLPP) algorithm and stationary test is proposed for nonlinear multimode process monitoring to analyze both within-mode and cross-mode information. First, the adjoined multiple local models are constructed in high-dimensional kernel space to extract within-mode information through kernel fuzzy c-means algorithm and kernel principal components. Afterwards, the weighted local representations are defined by integrating the local latent vectors within each mode and the corresponding posterior probabilities of data samples. Then, a modified DLPP algorithm is developed to preserve the local structure within each mode and separate different modes as far as possible by merging local models into a low-dimensional coordinated space. Augmented Dickey Fuller test (ADF test) strategy is utilized to decompose the final low-dimensional space into two subspaces including mode-specific subspace and mode-common subspace by checking the stability of the final global data. A Bayesian monitoring strategy is presented in mode-specific subspace and residual subspace to provide more reliable monitoring results. Finally, to illustrate the feasibility and effectiveness, the proposed algorithm is illustrated with multimode data generated from the Tennessee Eastman (TE) benchmark process and Three-phase flow process.

      PubDate: 2017-08-03T06:45:39Z
  • Leveraging multiple linear regression for wavelength selection
    • Abstract: Publication date: Available online 27 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Tony Lemos, John H. Kalivas
      Wavelength selection is often used for multivariate calibration methods to lower prediction error for the calibrated sample properties. As a result, there are a plethora of wavelength selection methods to select from; all with unique advantages and disadvantages. All wavelength selection methods involve a range of tuning parameters making the methods cumbersome or complex and hence, difficult to work with. The goal of this study is to provide a simple process to select wavelengths for multivariate calibration methods while trying to standardize values of the five adjustable algorithm tuning parameters across data sets. The proposed method uses multiple linear regression (MLR) as an indicator to which wavelengths should be used further to form a multivariate calibration model by some processes such as partial least squares (PLS). From a collection of MLR models formed from randomly selected wavelengths, those models within a thresholds of the bias indicator root mean square error of calibration (RMSEC) and variance indicator model vector L2 norm are evaluated to ascertain the most frequently selected wavelengths. Portions of the most frequent wavelengths are retained and used to produce a calibration model by PLS. This proposed wavelength selection method is compared to PLS models based on full spectra. Several near infrared data sets are evaluated showing that PLS models based on MLR selected wavelengths provide improved prediction errors. Of the five adjustable parameters for the wavelength selection method, three could be standardized across the data sets while the other two required minor tuning. Recommendations are provided as to alternate wavelength selection algorithms.

      PubDate: 2017-08-03T06:45:39Z
  • UPLC-MS/MS and multivariate analysis of inhibition of heterocyclic amine
           profiles by black pepper and piperine in roast beef patties
    • Abstract: Publication date: Available online 27 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Maomao Zeng, Mengru Zhang, Jing Chen, Zhiyong He, Fang Qin, Chundi Hu, Hongying Xu, Guanjun Tao, Shuang Zhang, Jie Chen
      In this study, UPLC-MS/MS combined with principal component analysis (PCA) with the help of scores, loadings, and contribution plots was used to investigate the inhibition of the profiles of carcinogenic and mutagenic heterocyclic amines (HAs) in roast beef patties by different levels of black pepper (0.05, 0.10, and 0.15%, g/g) or piperine (0.005, 0.010, and 0.015%, g/g). Six HAs from three categories, imidazopyridine (PhIP), imidazoquinoxaline (IQx, MeIQx, and 4,8-DiMeIQx), and β-carboline (harman and norharman), were detected and quantified in the roast beef patties. The PCA results demonstrated that both black pepper and piperine can significantly suppress the HA profiles and that lower levels of black pepper or piperine lead to better inhibition of HA formation, except the HA profiles of beef patties with 0.005% and 0.01% piperine were similar. In comparison, piperine inhibited HA formation better than did black pepper for all six HAs, as shown by the contribution plot. The total HAs were inhibited by all levels of black pepper (42%, 34%, 25%) and piperine (62%, 60%, 56%) in a dose-dependent manner, although black pepper suppressed only PhIP, IQx, MeIQx, and 4,8-DiMeIQx, whereas piperine inhibited all six HAs. The results show that UPLC-MS/MS in conjunction with PCA is a powerful tool for screening the inhibitory effects of spices and their components along with their relationship to HAs and can thus be used for safety control in food processing procedures.

      PubDate: 2017-08-03T06:45:39Z
  • An entropy based approach to estimation of analytical information. A
    • Abstract: Publication date: Available online 21 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Vladimir V. Apyari
      In this article, we propose a hypothesis to outline features of amount of analytical information to be obtained during chemical analysis. The information is considered in its connection with thermodynamic properties of a system under analyzing, especially with entropy as a measure of the lost information. Based on this supposition, a formal mathematical relation that connects the volume of analytical information in a qualitative chemical analysis with the concentration of an analyte, temperature, molecular weight and its thermodynamic properties, e.g. enthalpy of formation, has been derived. This relation should not be considered as a rigid mathematical connection but may be used as a guide to search correlations between the analytical information and thermodynamic properties of a system. The first examples of such correlations are given. In many cases, absolute correlation coefficients, R, are higher than 0.9. In our opinion, this hypothesis seems to be promising but requires further confirmation within different fields of analytical chemistry, remaining open to question.

      PubDate: 2017-07-24T06:16:15Z
  • Improved kernel PLS combined with wavelength variable importance for near
           infrared spectral analysis
    • Abstract: Publication date: Available online 21 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Xin Huang, Li Xia
      In this study, a new strategy called variable importance kernel PLS (VIKPLS) method is developed for near infrared spectral analysis. The wavelength variable importance is incorporated into KPLS by modifying the primary kernel matrix, and variables in the kernel matrix are given the different importance, which provides a feasible way to differentiate between the informative and uninformative variables. The importance of variables is determined by the frequency of variables appearing in the best performing sub-models based on the weighted bootstrap sampling. The performance of VIKPLS is investigated with three real near infrared(NIR) spectroscopic datasets. Examples are given specifically for modifying the linear kernel and Gaussian kernel. Compared with standard kernel PLS, the results show the proposed method can improve the training and prediction performance of KPLS by using variable importance kernel. VIKPLS could be considered as a general and promising mechanism to introduce extra information to improve the performance of KPLS.

      PubDate: 2017-07-24T06:16:15Z
  • Recognition of flooding and sinking conditions in flotation process using
           soft measurement of froth surface level and QTA
    • Abstract: Publication date: Available online 20 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Lin Zhao, Tao Peng, Yongfang Xie, Chunhua Yang, Weihua Gui
      Accurate recognition of abnormal conditions is crucial for control and optimization of the running of flotation process. In this paper, a novel method using soft measurement of froth surface level and modified qualitative trend analysis (QTA) is proposed for flooding and sinking conditions recognition. First, the soft measurement method based on defocus depth recovery is proposed to derive the froth surface level from the 2D froth image. Then, a modified interval-halving QTA is proposed to extract the reliable and stable trend information from the froth surface level. Finally, the flooding and sinking conditions can be recognized by the classification decision tree combining the froth surface level and its trend. Offline and online experiments indicate the proposed approach can accurately recognize the flooding and sinking conditions even at the early stage.

      PubDate: 2017-07-24T06:16:15Z
  • Multi-class classification for steel surface defects based on machine
           learning with quantile hyper-spheres
    • Abstract: Publication date: Available online 20 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Maoxiang Chu, Jie Zhao, Xiaoping Liu, Rongfen Gong
      Focusing on steel surface defects, a novel multi-class classification method is proposed. The method is termed as machine learning with quantile hyper-spheres (QH-ML). In order to obtain sparse set with boundary information from finite defect dataset, a new quantile hyper-sphere data description (QHDD) model is proposed. This model is used to generate a quantile hyper-sphere for each finite defect subset. And this quantile hyper-sphere is insensitive to noise. Then, in order to realize incremental learning for new samples, an incremental learning with quantile hyper-spheres (QHIL) method is proposed. The advantage of QHIL method is that the dataset is invariant in size during the process of incremental learning for new boundary information. In the meanwhile, a novel classifier with multiple quantile hyper-spheres (MQHC) is used to realize multi-class classification for steel surface defects. The target class of MQHC uses QHDD model, and negative class applies the margin maximization principle. MQHC has natural multi-class classification gene and perfect classification performance. In testing experiments, the proposed QH-ML is used to classify six types of defects with incremental learning. Experimental results show that QH-ML keeps high classification accuracy and efficiency.

      PubDate: 2017-07-24T06:16:15Z
  • Moving-window two-dimensional correlation spectroscopy and
           perturbation-correlation moving-window two-dimensional correlation
    • Abstract: Publication date: Available online 20 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Shigeaki Morita, Yukihiro Ozaki
      Numerical computations and practical applications of moving-window two-dimensional (MW2D) correlation spectroscopy and perturbation-correlation moving-window two-dimensional (PCMW2D) correlation spectroscopy are reviewed. A series of spectra obtained under a certain external perturbation, e.g., temperature-dependent infrared spectra, time-resolved near-infrared spectra, etc., is used for the analyses. Two-dimensional correlation map spread in a plane between spectral variable axis and perturbation variable axis is obtained by the computation. These methods therefore have become one of promising techniques to find informative bands in the spectral variable direction as well as informative perturbation points such as phase transition temperature in the perturbation variable direction.

      PubDate: 2017-07-24T06:16:15Z
  • Exploring the changes in a series of measurements – The comparison of
           the two-dimensional correlation analysis and the alteration analysis
    • Abstract: Publication date: Available online 20 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): József Simon, Attila Felinger
      Alteration analysis (ALA) has been recently introduced to expand the two-dimensional correlation analysis (2DCOR) into further dimensions. 2DCOR is unable to work with 3D data arrays composed from a series of 2D measurements, but ALA has the advantage that it does not increase (multiply) the dimensions of the original data sets. Thus, it can easily be applied to more complex systems. ALA, however, does not work only with 3D arrays, but with matrices as well. In this study we present a comparison of the two methods. ALA has a different mathematical background, indicating that it has different properties. Therefore, some drawbacks are inevitable, however, ALA has a number of advantages over 2DCOR. While 2DCOR emphasises the correlation between the changes, ALA focuses on individual changes and provides more detailed information about them. Furthermore, we demonstrate that the connection between these changes can also be described with ALA. Besides, ALA simplifies the visual representation, because instead of two 2D maps (2DCOR) the information is shown on a single linear graph. Therefore, ALA is not only an extension, but it can be an alternative to 2DCOR.

      PubDate: 2017-07-24T06:16:15Z
  • Ordered homogeneity pursuit lasso for group variable selection with
           applications to spectroscopic data
    • Abstract: Publication date: Available online 13 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): You-Wu Lin, Nan Xiao, Li-Li Wang, Chuan-Quan Li, Qing-Song Xu
      In high-dimensional data modeling, variable selection methods have been a popular choice to improve the prediction accuracy by effectively selecting the subset of informative variables, and such methods can enhance the model interpretability with sparse representation. In this study, we propose a novel group variable selection method named ordered homogeneity pursuit lasso (OHPL) that takes the homogeneity structure in high-dimensional data into account. OHPL is particularly useful in high-dimensional datasets with strongly correlated variables. We illustrate the approach using three real-world spectroscopic datasets and compare it with four state-of-the-art variable selection methods. The benchmark results on real-world data show that the proposed method is capable of identifying a small number of influential groups and has better prediction performance than its competitors. The OHPL method and the spectroscopic datasets are implemented and included in an R package OHPL available from

      PubDate: 2017-07-24T06:16:15Z
  • Reproducibility of nondominated solutions
    • Abstract: Publication date: 15 September 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 168
      Author(s): Nuno Costa, João Lourenço
      Natural process variability and model's uncertainty impact on nondominated solutions reproducibility cannot be ignored to assure that product or process will perform as expected when theoretical results are implemented in productive environments. To help the decision-maker in making more informed decisions when he/she selects a nondominated solution, two metrics are used to assess the predicted variability of nondominated solutions; one of them (the predicted standard error) quantifies the uncertainty in the estimated value for each response, the another one (the quality of predictions) quantifies the uncertainty associated to each generated solution. Supplementary material is provided to help the practitioners in calculating the metric values.

      PubDate: 2017-07-12T05:38:14Z
  • Editorial
    • Abstract: Publication date: Available online 8 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Roma Tauler, Philip Hopke

      PubDate: 2017-07-12T05:38:14Z
  • On the definition of mean, variance and covariance for periodic variables
           to avoid ambiguity in chemometric and bioinformatic data evaluation
    • Abstract: Publication date: Available online 3 July 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Gergely Tóth, Péter Király, Dóra Judit Kiss
      In the case of periodic variables, e.g. angular and temporal ones, the choice of the periodic section where experimental or calculated data are recorded is usually driven by convention. Unfortunately, basic statistics (mean, variance, covariance) and many multivariate statistical data evaluation methods (principal component analysis, clustering and classification methods, regressions …) provide different results for different data windows. We propose to change the selection of the periodic section to a data pattern based method, where that data window is selected which provides the smallest variance for the data. The use of smallest variance can be theoretically supported with the maximum likelihood principle. We show the advantages of the minimum-variance data window on two Ramachandran plots of simulated triglycine dihedral angles, where the unambiguously calculated means and clustering results are in agreement with the expectations of common sense. The second example concerns the enhanced effectivity in clustering of a temporal distribution of PM10 air pollutant, if the proposed data window is applied.
      Graphical abstract image

      PubDate: 2017-07-12T05:38:14Z
  • Semi-supervised fault classification based on dynamic Sparse Stacked
           auto-encoders model
    • Abstract: Publication date: Available online 27 June 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Li Jiang, Zhiqiang Ge, Zhihuan Song
      This paper proposes a hierarchical sparse artificial neural network for classifying the faults in dynamic processes base on limited labeled data. The Stacked auto-encoders (SAE) is developed to extract features from different faults. Each neural network in the proposed SAE is given a sparse constraint to learn a Sparse Stacked auto-encoders (SSAE). Then, the Dynamic time window is combined into SSAE to build Dynamic Sparse Stacked auto-encoders (DSSAE). DSSAE model based semi-supervised fault classification scheme is then formulated to classify the dynamic faulty data. Simulation studies on the Tennessee–Eastman (TE) benchmark process evaluate the performance of the developed method, which indicate that the DSSAE method performs better than both SAE and SSAE.

      PubDate: 2017-07-03T07:30:41Z
  • Incremental model learning for spectroscopy-based food analysis
    • Abstract: Publication date: 15 August 2017
      Source:Chemometrics and Intelligent Laboratory Systems, Volume 167
      Author(s): Katerine Diaz-Chito, Konstantia Georgouli, Anastasios Koidis, Jesus Martinez del Rincon
      In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps.

      PubDate: 2017-06-12T04:10:43Z
  • MCR-ALS applied to the quantification of the 5-Hydroxymethylfurfural using
           UV spectra: Study of catalytic process employing experimental design
    • Abstract: Publication date: Available online 3 June 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Larissa R. Terra, Mariana N. Catrinck, Reinaldo F. Teófilo
      Study of the production of 5-hydroxymethylfurfural (5-HMF) from glucose, applying niobic acid as catalyst, and the quantification of 5-HMF by selective analytical methods are the goals of this work. The high-performance liquid chromatography (HPLC) was used as reference method. The developed alternative method proposes the use of ultraviolet (UV) spectroscopy technique in combination with multivariate curve resolution alternating least squares (MCR-ALS). In addition, a partial least squares (PLS) regression model was built to compare MCR-ALS and HPLC results. Regression models using MCR-ALS and PLS were built for 5-HMF in the presence of levulinic acid in the range of 2.0–16.0 mg L−1. For HPLC the range used was of 10–800 mg L−1. The models were evaluated by analyzing statistical parameters of quality such as root mean square error (RMSE) and correlation coefficient (R). The calibration parameters obtained for MCR-ALS, PLS and HPLC were, respectively: RMSEC of 0.68, 0.27 and 4.92 mg L−1 and R equal to 0.988, 0.998 and 0.999. Central composite design (CCD) was used to optimize the two variables of reaction, i.e., time and mass of catalyst for glucose conversion into 5-HMF. The samples were analyzed by UV-MCR-ALS, UV-PLS and HPLC. The predicted concentrations of 5-HMF obtained by MCR-ALS and PLS versus HPLC predictions were evaluated. Both correlations were equals to 0.995. According to paired t-test results at a significance level of 0.05 both model predictions were statistically equals to the values predicted by HPLC. The results show that UV-MCR-ALS method can predict the concentration of 5-HMF in reaction mixtures with accuracy and obtain the relative concentration and pure spectra in a mixture without chromatographic separation.

      PubDate: 2017-06-07T03:50:59Z
  • Prediction subcellular localization of Gram-negative bacterial proteins by
           support vector machine using wavelet denoising and Chou's pseudo amino
           acid composition
    • Abstract: Publication date: Available online 1 June 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Bin Yu, Shan Li, Cheng Chen, Jiameng Xu, Wenying Qiu, Xue Wu, Ruixin Chen
      Information on the subcellular localization of Gram-negative bacterial proteins is of great significance to study the pathogenesis, drug design and discovery of certain diseases. Protein subcellular localization is an important part of proteomics, while providing new opportunities and challenges for chemometrics. Since the prediction of protein subcellular localization can help to understand their function and the role played by their metabolic processes, a number of protein subcellular localization prediction methods have been developed in recent years. In this paper, we propose a novel method by combining wavelet denoising with support vector machine to predict the subcellular localization of proteins for the first time. Firstly, the features of the protein sequence are extracted by Chou's pseudo amino acid composition (PseAAC), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of the Gram-negative bacterial proteins. Quite promising predictions are obtained using the jackknife test and compared with other predictive methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of protein subcellular localization, and it can be used to predict the other attributes of proteins.

      PubDate: 2017-06-02T03:32:04Z
  • A variable selection method for soft sensor development through mixed
           integer quadratic programming
    • Abstract: Publication date: Available online 25 May 2017
      Source:Chemometrics and Intelligent Laboratory Systems
      Author(s): Weiyu Jian, Lingyu Zhu, Zuhua Xu, Xi Chen
      Soft sensors are widely employed in industry to predict quality variables, which are difficult to measure online, by using secondary variables. To build an accurate soft sensor, a proper variable selection is critical. In this project, a method of selecting the optimal secondary variables for a soft sensor model is proposed. It is formulated as a nested optimization problem. In each iteration, a mixed integer quadratic programming (MIQP) is conducted with the Bayesian information criterion (BIC) to estimate the prediction error. A warm start (WS) technique is developed to speed up the convergence. The proposed method is evaluated using a number of instances from the UCI Machine Learning Repository. The computational results demonstrate that this method is well suited for finding the best variable subsets. The method is successfully applied to build soft sensors for an industrial distillation column. The results show that the proposed method can effectively select feature variables that will improve the model prediction performance and reduce the model complexity. Comparisons with other methods, including the traditional partial least square technique, are also presented.

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

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

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

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

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

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

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

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

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

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

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

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

      PubDate: 2017-05-23T05:49:06Z
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