Showing papers in "Chemometrics and Intelligent Laboratory Systems in 2017"

TL;DR: A systematic review on data-driven modeling and monitoring for plant-wide processes is presented in this paper, where the authors provide an overview of the state-of-the-art data processing and modeling procedures for the plantwide process monitoring.

TL;DR: In this study, different global measures of classification performances are compared by means of results achieved on an extended set of real multivariate datasets and a set of benchmark values based on different random classification scenarios are introduced.

TL;DR: In this paper, the authors discuss the importance of analysis of prediction errors to check for the presence of systematic error and/or violation of basic assumptions of the least-squares regression models under the BLUE framework with suitable examples using real QSAR model-derived quantitative predictions for test sets and simulated prediction data.

TL;DR: DD-SIMCA — a MATLAB GUI tool that extends the MATLAB environment to provide an easy way for establishment and employment of the data driven SIMCA technique.

TL;DR: The status quo of DP practice strategy is outlined and critically discussed on whether the contemporary practice has been malpractice or best practice, and rationales that could have possibly contributed to some of the malpractices are discussed.

TL;DR: 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.

TL;DR: An innovative chem-informatics tool, PyDescriptor, which 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.

TL;DR: A new analyst driven method to determine the maximum number of lags in DPCA with a foundation in multivariate time series analysis is proposed, based on the behavior of the eigenvalues of the lagged autocorrelation and partial autOCorrelation matrices.

TL;DR: The proposed methodology not only provides a recursive minimization strategy to deal with missing values but also offers Kernel Density Estimation (KDE) to alleviate the Gaussian assumption of derived data.

TL;DR: The models studied identify that secondary protein structure variations and DNA/RNA alterations are the main biomolecular ‘difference markers’ for prostate cancer grades.

TL;DR: In this article, a new adaptive thresholding scheme based on a modified exponentially weighted moving average (EWMA) control chart statistic was proposed to detect small changes and abrupt shifts in the process operation.

TL;DR: The results indicate that the new model is much more robust and reliable with less model parameters, which make it useful for industrial applications.

TL;DR: The experimental results show that the proposed multi-class classifier has perfect performance in efficiency and accuracy and multi-type statistical features are in favor of improving classification performance.

TL;DR: In this study, IQR was used to determine the best-suited mother wavelet for electronic nose signals in beef quality classification and the experimental results show that IQR based mother wavelets have better capability to keep essential information from original signals than SNR, MSE, and correlation coefficient basedmother wavelets.

TL;DR: This contribution introduces, discusses and evaluates a wide-ranging subset of transfer approaches available in chemometrics and the field of machine learning, where they focus on techniques applicable in situations where transfer standards cannot be provided and only few reference measurements are available for the new setting.

TL;DR: Fully robust versions of the elastic net estimator are introduced for linear and logistic regression in this article, where the algorithms used to compute the estimators are based on the idea of repeatedly applying the non-robust classical estimators to data subsets only.

TL;DR: 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.

TL;DR: The superiority of the proposed weighted DD PCA (WDDPCA) method over dynamic PCA, DLV, and DDPCA are explored by two industrial processes and apparently illustrate the salient monitoring performance that can be achieved by WDD PCA.

TL;DR: A Fault discriminant enhanced KPCA (FDKPCA) method is proposed, which simultaneously monitors two types of data features, nonlinear kernel principal components (KPCs) and fault discriminant components (FDCs).

TL;DR: Modified versions of two-dimensional principal component analysis with linear discriminant analysis (2D-PCA-LDA), quadratic discriminantAnalysis ( 2D- PCA-QDA), and support vector machines (2 D-PCa-SVM) have been proposed to classify three-way chemical data.

TL;DR: 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.

TL;DR: In this paper, a kernel-based extreme learning machine (K-ELM) model was built and applied to laser induced breakdown spectroscopy (LIBS) to improve the quantitative analysis accuracy of carbon and sulfur in coal.

TL;DR: A new distributed monitoring scheme that integrates minimal redundancy maximal relevance, Bayesian inference and principal component analysis (PCA) is proposed for plant-wide processes that considers not only the relevance between variables, but also their redundancy in block division.

TL;DR: A selective review on interval selection methods with partial least squares (PLS) as the calibration model and compared and discussed the performances of a subset of these methods on three real-world spectroscopic datasets.

TL;DR: In this paper, Fourier Transform Infrared Spectroscopy (FTIR) was selected as a reliable, fast and non-destructive technique to record spectroscopic fingerprints of Moroccan Protected Geographical Indication (PGI) Argan oils.

TL;DR: Results showed that the six regions of geographical origin can be identified based on the fatty acid fingerprints, and a high percentage for the prediction set shows the ability to indicate the origin of an unknown sample based on its fatty acid chromatographic data.

TL;DR: The work in this paper presents an approach that can be taken to perform non-negative matrix factorisation (NMF) of large ToF-SIMS datasets and shows that the fingerprint signal was successfully separated from the substrate signal.

TL;DR: An enhanced KECA method for fault detection and diagnosis is developed, by adding multi-scale principal component analysis for features extraction to improve the classification effect of KECA.

TL;DR: In this paper, three high-order chemometric algorithms, namely multiway principal component analysis (NPCA), parallel factor analysis (PARAFAC) and alternating trilinear decomposition (ATLD), were employed to extract the information from temperature dependent near infrared (NIR) spectra of alcohol aqueous solutions.

TL;DR: This paper presents a novel multi-block regression method that can handle multi-way data blocks and investigates the hypotheses that SO-N-PLS has better performances on small data sets and noisy data, and thatSO-N -PLS models are easier to interpret.