Conformational Sampling of Macrocyclic Alkenes Using a Kennard−Stone-Based Algorithm

Claeys, Diederica D.; Verstraelen, Toon; Pauwels, Ewald; Stevens, Christian V.; Waroquier, Michel; Speybroeck, Véronique Van

doi:10.1021/jp1022778

Cited by 20 publications

(6 citation statements)

References 69 publications

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“…To obtain a quantitative analysis model of stibnite content, the whole samples were divided into a calibration set and prediction set based on the Kennard–Stone method; these sets contained 84 and 36 samples, respectively . The statistical characteristics for the two data sets are summarized in Table .…”

Section: Methodsmentioning

confidence: 99%

Quantitative analysis of stibnite content in raw ore by Raman spectroscopy and chemometric tools

Cai

Yang

Gui

2018

J Raman Spectroscopy

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This paper presents a non-destructive and fast methodology to quantify the stibnite content in raw ore samples using Raman spectroscopy. A total of 120 raw ore reference samples were obtained from a froth flotation process, and multipoint collection and averaging were used to obtain Raman spectra from the raw ore samples. Our aim was to create the best multivariate calibration model for the quantitative analysis of stibnite contents in the raw ore samples.Several strategies were evaluated to generate a robust model; these strategies included preprocessing methods (de-nosing, baseline correction, and vector normalization), selecting the key wavenumbers for the quantitative analysis of stibnite, and building a multivariate calibration model. Synergy interval partial least squares (PLS), backward interval PLS, stability competitive adaptive reweighted sampling, and PLS with a genetic algorithm (GA) were investigated to build the linear multivariable models, whereas artificial neural network (ANN) and support vector regression (SVR) preceded by GAs (GA-ANN and GA-SVR) were implemented to build the non-linear multivariable models.Experimental results showed that the best multivariable calibration model for stibnite was obtained by a combination of an ANN and GA (GA-ANN). In contrast to the PLS model based on the full spectrum, the root mean square error of calibration and root mean square error of prediction of the GA-ANN method for the calibration and validation sets were reduced to 0.2038 from 0.3552 and to 0.2196 from 0.3927, respectively, and the squares of the correlation coefficients of the calibration and validation sets were increased to 0.9369 from 0.8053 and 0.9219 from 0.7561, respectively. The above results indicate that the multivariable calibration model for stibnite is stable. Furthermore, this method could be used in the froth flotation process to precisely determine the stibnite content in raw ore samples.

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Section: Methodsmentioning

confidence: 99%

Quantitative analysis of stibnite content in raw ore by Raman spectroscopy and chemometric tools

Cai

Yang

Gui

2018

J Raman Spectroscopy

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“…There is a specific relationship between the performance of the BVCDD model and the number of training samples chosen for training every submodel. The initial samples will affect the performance and give randomness to the final model, so this paper selects the initial m samples with the Kennard−Stone (K−S) 60 method, which can select the maximum range of the training samples. Initially increasing the training with weak samples will help the model get a better performance.…”

Section: Industrial and Engineering Chemistry Researchmentioning

confidence: 99%

Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning

Zhou

Xiong

2019

Ind. Eng. Chem. Res.

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This paper proposes a boosting vine copula-based dependence description (BVCDD) method for multivariate and multimode process monitoring. The BVCDD aims to improve the standard vine copula-based dependence description (VCDD) method by establishing an ensemble of submodels from sample directions based on a boosting strategy. The generalized Bayesian inference-based probability (GBIP) index is introduced to assess the degrees of a VCDD model (submodel) to depict different samples, which means how likely an observation is under the probabilistic model for the system. Every sample is weighted individually according to the depiction degree. The weights are then used to choose a certain number of samples for each succeeding submodel. In this way, the samples with large error in the preceding model can be selected for training the next submodel. Moreover, the number of submodels as well as the number of training samples chosen for every submodel are determined adaptively in the ensemble learning process. The proposed BVCDD method can not only solve weak sample problems but also remove redundant information in samples. To examine the performance, empirical evaluations have been conducted to compare the BVCDD method with some other state-of-the-art methods in a numerical example, the Tennessee Eastman (TE) process, and an acetic acid dehydration process. The results show that the developed BVCDD models are superior to those obtained by the counterparts on weak samples in both accuracy and stability.

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“…The Kennard-Stone (KS) algorithm 24,25 is an effective partition method for sample experiment. A \distance" between every two samples was¯rst de¯ned (e.g., Euclidean distance or Mahalanobis distance).…”

Section: Calibration Prediction and Validation Processesmentioning

confidence: 99%

Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

Yao

Pan

et al. 2018

J. Innov. Opt. Health Sci.

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The moving-window bis-correlation coe±cients (MW-BiCC) was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and -thalassemia with visible and nearinfrared (Vis-NIR) spectroscopy. The well-performed moving-window principal component analysis linear discriminant analysis (MW-PCA-LDA) was also conducted for comparison. A total of 306 transgenic (positive) and 150 nontransgenic (negative) leave samples of sugarcane were collected and divided to calibration, prediction, and validation. The di®use re°ection spectra were corrected using Savitzky-Golay (SG) smoothing with¯rst-order derivative (d ¼ 1), third-degree polynomial (p ¼ 3) and 25 smoothing points (m ¼ 25). The selected waveband was 736-1054 nm with MW-BiCC, and the positive and negative validation recognition rates (V REC þ , V REC À Þ were 100%, 98.0%, which achieved the same e®ect as MW-PCA-LDA. Another example, the 93 -thalassemia (positive) and 148 nonthalassemia (negative) of human hemolytic samples were collected. The transmission spectra were corrected using SG smoothing with d ¼ 1, p ¼ 3 and m ¼ 53. Using MW-BiCC, many best wavebands were selected (e.g., 1116-1146, 1794-1848 and 2284-2342 nm). The V REC þ and V REC À were both 100%, which achieved the same e®ect as MW-PCA-LDA. Importantly, the BiCC only required calculating correlation coe±cients between the spectrum of prediction sample and the average spectra of two types of calibration samples. Thus, BiCC was very simple in algorithm, and expected to obtain more applications. The results¯rst con¯rmed the feasibility of distinguishing -thalassemia and normal control samples by NIR spectroscopy, and provided a promising simple tool for large population thalassemia screening.

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Conformational Sampling of Macrocyclic Alkenes Using a Kennard−Stone-Based Algorithm

Cited by 20 publications

References 69 publications

Quantitative analysis of stibnite content in raw ore by Raman spectroscopy and chemometric tools

Quantitative analysis of stibnite content in raw ore by Raman spectroscopy and chemometric tools

Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning

Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

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