Hua Li

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Name: 李华

Organization: Northwest University , China

Department: Institute of Analytical Science

Title: NULL(PhD)

TOPICS

Inorganic Chemistry

hydrogenase models

Chemicals
References

Classification of wines according to their production regions with the contained trace elements using laser-induced breakdown spectroscopy

Co-reporter:Ye Tian, Chunhua Yan, Tianlong Zhang, Hongsheng Tang, Hua Li, Jialu Yu, Jérôme Bernard, Li Chen, Serge Martin, Nicole Delepine-Gilon, Jana Bocková, Pavel Veis, Yanping Chen, Jin Yu

Spectrochimica Acta Part B: Atomic Spectroscopy 2017 Volume 135(Volume 135) pp:

Publication Date(Web):1 September 2017

DOI:10.1016/j.sab.2017.07.003

•Classification of wines according to their production sites with LIBS.•Sensitive elemental detection in wines with surface-assisted LIBS liquid analyses.•Matrix effect assessment in classification using principal component analysis (PCA).•Classification of wines according to their production regions with random forest (RF).Laser-induced breakdown spectroscopy (LIBS) has been applied to classify French wines according to their production regions. The use of the surface-assisted (or surface-enhanced) sample preparation method enabled a sub-ppm limit of detection (LOD), which led to the detection and identification of at least 22 metal and nonmetal elements in a typical wine sample including majors, minors and traces. An ensemble of 29 bottles of French wines, either red or white wines, from five production regions, Alsace, Bourgogne, Beaujolais, Bordeaux and Languedoc, was analyzed together with a wine from California, considered as an outlier. A non-supervised classification model based on principal component analysis (PCA) was first developed for the classification. The results showed a limited separation power of the model, which however allowed, in a step by step approach, to understand the physical reasons behind each step of sample separation and especially to observe the influence of the matrix effect in the sample classification. A supervised classification model was then developed based on random forest (RF), which is in addition a nonlinear algorithm. The obtained classification results were satisfactory with, when the parameters of the model were optimized, a classification accuracy of 100% for the tested samples. We especially discuss in the paper, the effect of spectrum normalization with an internal reference, the choice of input variables for the classification models and the optimization of parameters for the developed classification models.Download high-res image (264KB)Download full-size image

Determination of carbon and sulfur content in coal by laser induced breakdown spectroscopy combined with kernel-based extreme learning machine

Co-reporter:Chunhua Yan, Juan Qi, Junxiu Ma, Hongsheng Tang, Tianlong Zhang, Hua Li

Chemometrics and Intelligent Laboratory Systems 2017 Volume 167(Volume 167) pp:

Publication Date(Web):15 August 2017

DOI:10.1016/j.chemolab.2017.06.006

•LIBS coupled with K-ELM was used for the determination of carbon and sulfur content in coal.•The results were compared with those obtained with SVM, LS-SVM and BP-ANN models.•LIBS coupled with K-ELM is a promising technique for real-time online, rapid analysis in coal industry.The carbon and sulfur content is an important index of coal property. In the present work, 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. The different preprocessing techniques, input variables and model parameters were optimized by 5-fold cross validation to find which combination can provide an appropriate calibration model. Then the optimized K-ELM model was applied to quantitative analysis of carbon and sulfur content in coal, and a comparison with support vector machine (SVM), least squares support vector machine (LS-SVM) and back propagating artificial neutral net (BP-ANN) was carried out. The three quantitative techniques were evaluated in terms of Root Mean Square Error (RMSE) and correlation coefficients (R2). The results show that K-ELM model has excellent performance compared to the others both in calibration and prediction set, and the optimum results of the K-ELM model were achieved with RMSE = 0.3762%, R2 = 0.9994 for C and RMSE = 0.7704%, R2 = 0.9832 for S in the prediction set. The overall results sufficiently demonstrate that LIBS coupled with K-ELM method has the potential to measure carbon and sulfur content in coal, and is a promising technique for real-time online, rapid analysis in coal industry.

Quantitative detection of harmful elements in alloy steel by LIBS technique and sequential backward selection-random forest (SBS-RF)

Co-reporter:Fangqi Ruan;Juan Qi;Chunhua Yan;Hongsheng Tang;Tianlong Zhang

Journal of Analytical Atomic Spectrometry 2017 vol. 32(Issue 11) pp:2194-2199

Publication Date(Web):2017/11/02

DOI:10.1039/C7JA00231A

In recent years, LIBS quantitative analysis based on multivariate regression has received considerable attention, and variable selection is critical for improving accuracy of multivariate regression analysis of LIBS. In the present study, sequential backward selection combined with random forest was proposed to improve detection accuracy of sulfur and phosphorus in steel. First, LIBS spectrum line of S and P was identified by the NIST database. Second, input variables for RF calibration model were selected and optimized by SBS, and RF model parameters (ntree and mtry) were optimized by out-of-bag (OOB) estimation. Finally, optimized input variables and model parameters were employed to build an SBS-RF calibration model for quantitative analysis of P and S in steel. Results showed that the SBS-RF model provided good predictions for S (R2 = 0.9991) and P (R2 = 0.9994) compared with those provided by the univariate method, PLS model and traditional RF model. Thus, LIBS coupled with SBS-RF is an effective method for quality supervision and control of steel products.

Quantitative detection of harmful elements in alloy steel by LIBS technique and sequential backward selection-random forest (SBS-RF)

Co-reporter:Fangqi Ruan;Juan Qi;Chunhua Yan;Hongsheng Tang;Tianlong Zhang

Journal of Analytical Atomic Spectrometry 2017 vol. 32(Issue 11) pp:2194-2199

Publication Date(Web):2017/11/02

DOI:10.1039/C7JA00231A

Classification and discrimination of coal ash by laser-induced breakdown spectroscopy (LIBS) coupled with advanced chemometric methods

Co-reporter:Tianlong Zhang;Chunhua Yan;Juan Qi;Hongsheng Tang

Journal of Analytical Atomic Spectrometry 2017 vol. 32(Issue 10) pp:1960-1965

Publication Date(Web):2017/10/04

DOI:10.1039/C7JA00218A

The classification and identification of coal ash contributes to recycling and reuse of metallurgical waste. This work explores the combination of the laser-induced breakdown spectroscopy (LIBS) technique and independent component analysis-wavelet neural network (ICA-WNN) for the classification analysis of coal ash. A series of coal ash samples were compressed into pellets and prepared for LIBS measurements. At first, principal component analysis (PCA) was used to identify and remove abnormal spectra in order to optimize the training set for the WNN model. And then, ICA was employed to select and optimize input variables for the WNN model. The classification of coal ash was carried out by using the WNN model with optimized model parameters (the number of hidden neurons (NHN), the number of iterations (NI), the learning rate (LR) and the momentum) and input variables optimized by ICA. Under the optimized WNN model parameters, the coal ash samples for test sets were identified and classified by using WNN and artificial neural network (ANN) models, and the WNN model shows a better classification performance. It was confirmed that the LIBS technique coupled with the WNN method is a promising approach to achieve the online analysis and process control of the coal industry.

On-line attenuated total reflection infrared (ATR-IR) spectroscopy combined with advanced chemometrics for investigating the synthesis process of 3,5-diamino-1,2,4-triazole

Co-reporter:Junxiu Ma;Xinyu Gao;Juan Qi;Chunhua Yan

Journal of the Iranian Chemical Society 2017 Volume 14( Issue 4) pp:915-923

Publication Date(Web):2017 April

DOI:10.1007/s13738-016-1040-6

The synthesis process of 3,5-diamino-1,2,4-triazole (DAT) was investigated by on-line attenuated total reflection infrared (ATR-IR) spectroscopy combined with advanced chemometrics method. The principal component analysis (PCA) was used to analyze the IR spectra matrix, which was in order to obtain orthonormal column and the number of principal components. Then the pure IR spectrum of every substance was obtained by mutual information least dependent component analysis (MILCA). The possible synthesis mechanism of DAT was deducted based on the changes of functional groups in the IR spectra. The geometric configurations of intermediates were optimized with the density functional theory (DFT) at B3LYP/6-311G*(d, p) level, and the vibrational frequencies were calculated simultaneously. The results by MILCA method agree well with quantum chemical calculation method, thus which demonstrated the reliability of MILCA. The present study proves that on-line ATR-IR spectroscopy combined with advanced chemometrics method can be applied to study the chemical synthesis mechanism and provide a strong technical support for the research and development of process analytical technology (PAT).

Preparation of Yellow-Green-Emissive Carbon Dots and Their Application in Constructing a Fluorescent Turn-On Nanoprobe for Imaging of Selenol in Living Cells

Co-reporter:Qin Wang, Shengrui Zhang, Yaogang Zhong, Xiao-Feng YangZheng Li, Hua Li

Analytical Chemistry 2017 Volume 89(Issue 3) pp:

Publication Date(Web):December 23, 2016

DOI:10.1021/acs.analchem.6b03983

Selenocysteine (Sec) carries out the majority of the functions of the various Se-containing species in vivo. Thus, it is of great importance to develop sensitive and selective assays to detect Sec. Herein, a carbon-dot-based fluorescent turn-on probe for highly selective detection of selenol in living cells is presented. The highly photoluminescent carbon dots that emit yellow-green fluorescence (Y-G-CDs; λmax = 520 nm in water) were prepared by using m-aminophenol as carbon precursor through a facile solvothermal method. The surface of Y-G-CDs was then covalently functionalized with 2,4-dinitrobenzenesulfonyl chloride (DNS-Cl) to afford the 2,4-dinitrobenzene-functionalized CDs (CD-DNS) as a nanoprobe for selenol. CD-DNS is almost nonfluorescent. However, upon treating with Sec, the DNS moiety of CD-DNS can be readily cleaved by selenolate through a nucleophilic substitution process, resulting in the formation of highly fluorescent Y-G-CDs and hence leads to a dramatic increase in fluorescence intensity. The proposed nanoprobe exhibits high sensitivity and selectivity toward Sec over biothiols and other biological species. A preliminary study shows that CD-DNS can function as a useful tool for fluorescence imaging of exogenous and endogenous selenol in living cells.

Development of a red fluorescent light-up probe for highly selective and sensitive detection of vicinal dithiol-containing proteins in living cells

Co-reporter:Yuanyuan Wang, Xiao-Feng Yang, Yaogang Zhong, Xueyun Gong, Zheng Li and Hua Li

Chemical Science 2016 vol. 7(Issue 1) pp:518-524

Publication Date(Web):23 Oct 2015

DOI:10.1039/C5SC02824H

Vicinal dithiol-containing proteins (VDPs) play a key role in cellular redox homeostasis and are responsible for many diseases. Here, we develop a red fluorescent light-up probe FAsH for the highly selective and sensitive detection of VDPs using the environment-sensitive 2-(4-dimethylaminophenyl)-4-(2-carboxyphenyl)-7-diethylamino-1-benzopyrylium (F1) as the fluorescent reporter and cyclic dithiaarsane as the targeting unit. FAsH is almost nonfluorescent in aqueous solution. However, it exhibits intense fluorescence emission upon binding to reduced bovine serum albumin (rBSA, selected as the model protein). The fluorescence intensity of FAsH is directly proportional to the concentration of rBSA over the range of 0.06–0.9 μM, with a detection limit (3δ) of 0.015 μM. Importantly, the fast kinetics of binding between FAsH and VDPs (∼2.5 min) enables the dynamic tracing of VDPs in biological systems. Preliminary experiments show that FAsH can be used for the no-wash imaging of endogenous VDPs in living cells. In addition, our study shows that F1 presents both high environment-sensitivity and good fluorescence properties, and is promising for the development of no-wash fluorescent light-up probes for target-specific proteins in living cells.

Ratiometric Fluorescent Probe for Vicinal Dithiol-Containing Proteins in Living Cells Designed via Modulating the Intramolecular Charge Transfer–Twisted Intramolecular Charge Transfer Conversion Process

Co-reporter:Yuanyuan Wang, Yaogang Zhong, Qin Wang, Xiao-Feng Yang, Zheng Li, and Hua Li

Analytical Chemistry 2016 Volume 88(Issue 20) pp:10237

Publication Date(Web):September 20, 2016

DOI:10.1021/acs.analchem.6b02923

Vicinal dithiol-containing proteins (VDPs) play a significant role in maintaining the cellular redox homeostasis and are implicated in many diseases. To provide new chemical tools for VDPs imaging, we report here a ratiometric fluorescent probe CAsH2 for VDPs using 7-diethylaminiocoumarin as the fluorescent reporter and cyclic 1,3,2-dithiarsenolane as the specific ligand. CAsH2 shows peculiar dual fluorescence emission from the excited intramolecular charge transfer (ICT) and twisted intramolecular charge transfer (TICT) states in aqueous media. However, upon selective binding of protein vicinal dithiols to the trivalent arsenical of CAsH2, the probe was brought from the polar water media into the hydrophobic protein domain, causing the excited state ICT to TICT conversion to be restricted; as a result, an increase from the ICT emission band and a decrease from the TICT emission band were observed simultaneously. The designed probe shows high selectivity toward VDPs over other proteins and biological thiols. Preliminary experiments show that CAsH2 can be used for the ratiometric imaging of endogenous VDPs in living cells. So far as we know, this is a rare example of the ratiometric fluorescent probe designed via modulating the ICT–TICT conversion process, which provides a new way to construct various protein-specific ratiometric fluorescent probes.

Acidity analysis of iron ore based on calibration-free laser-induced breakdown spectroscopy (CF-LIBS) combined with a binary search algorithm (BSA)

Co-reporter:Zhanmei Wang, Chunhua Yan, Juan Dong, Tianlong Zhang, Jiao Wei and Hua Li

RSC Advances 2016 vol. 6(Issue 80) pp:76813-76823

Publication Date(Web):08 Aug 2016

DOI:10.1039/C6RA13038K

Calibration-free laser-induced breakdown spectroscopy (CF-LIBS) combined with a binary search algorithm (BSA) is proposed to determine the acidity (CaO/SiO2 mass ratios) of iron ore. It is based on the idea that different samples with a similar matrix ablated in the same conditions have the same plasma temperature. Ca I/Si I molar ratios are obtained by the intercepts on the Boltzmann plots drawn by using corrected spectral lines without self-absorption, and the number concentrations of primary ionization ions of the elements are evaluated by the Saha equation. Furthermore, one standard sample matrix-matched with unknown samples along with BSA is employed to obtain a more accurate plasma temperature. Noteworthily, BSA is a classical search method and utilized to search the optimal plasma temperature for the first time in CF-LIBS. The acidity of the iron ores can be calculated according to the obtained value of Ca/Si molar ratios. The calculated acidity of the unknown samples were close to the certified acidity based on the root mean square error (RMSE) and mean relative error (MRE) which were 0.0145 and 4.01%, respectively. The proposed CF-LIBS method can be used to determine the acidity of iron ores, and it provides a new method and technology for selection and quality control of iron ore.

Classification of iron ore based on acidity and alkalinity by laser induced breakdown spectroscopy coupled with N-nearest neighbours (N3)

Co-reporter:Chunhua Yan, Zhanmei Wang, Fangqi Ruan, Junxiu Ma, Tianlong Zhang, Hongsheng Tang and Hua Li

Analytical Methods 2016 vol. 8(Issue 32) pp:6216-6221

Publication Date(Web):19 Jul 2016

DOI:10.1039/C6AY01396A

Laser induced breakdown spectroscopy (LIBS) coupled with N-nearest neighbours (N3) method was developed for classification and identification of four types of iron ore (acid iron ore, seiili-self fluxing iron ore, self-fluxing iron ore and alkaline iron ore). The parameters included spectral pretreatment methods and spectral range selection and the model parameter α was optimized at the same time by 5-fold cross validation and evaluated by average classification error rate. The region of 400–600 nm was normalized by maximum integrated intensity and used to construct the N3 and KNN (K nearest neighbor) models. The N3 and KNN models were evaluated and applied to discriminate iron ore. The classification accuracy is 100% for the N3 model, which shows better predictive capabilities than the KNN model for the classification of iron ore. Therefore, LIBS technique combined with N3 could be a promising method for real-time online, rapid analysis in mining and mineral processing industries.

Quantitative analysis of the major components of coal ash using laser induced breakdown spectroscopy coupled with a wavelet neural network (WNN)

Co-reporter:Jiao Wei, Juan Dong, Tianlong Zhang, Zhanmei Wang and Hua Li

Analytical Methods 2016 vol. 8(Issue 7) pp:1674-1680

Publication Date(Web):25 Jan 2016

DOI:10.1039/C5AY02994E

A laser induced breakdown spectroscopy (LIBS) technique was applied to detect the major components of coal ash based on a wavelet neural network (WNN). Prior to constructing the WNN model, the spectra were preprocessed using wavelet threshold de-noising and Kalman filtering, and the principle components (PC), extracted using principle component analysis (PCA), were used as the input variables. Afterwards, the quantitative analysis of the major components in coal ash samples was completed using the WNN with the optimized WNN model parameters consisting of the number of hidden neurons (NHN), the number of iterations (NI), the learning rate (LR) and the momentum based on the root mean square error (RMSE). Finally, an artificial neural network (ANN) and the WNN were evaluated comparatively on their ability to predict the content of major components of test coal ash samples in terms of correlation coefficient (R) and RMSE, demonstrating that LIBS combined with a WNN model exhibited better prediction for coal ash, and is a promising technique for combustion process control even in the online mode.

In situ ATR-FTIR combined with SIMPLISMA algorithm to investigate the synthesis mechanism of 4-amino-3,5-dimethyl pyrazole

Co-reporter:Xinyu Gao;Junxiu Ma;Fangqi Ruan

Chemical Research in Chinese Universities 2016 Volume 32( Issue 6) pp:985-991

Publication Date(Web):2016 December

DOI:10.1007/s40242-016-6189-0

In situ attenuated total refletion-Fourier transform infrared spectroscopy(ATR-FTIR) was used to monitor and acquire spectral information on the synthesis of 4-amino-3,5-dimethyl pyrazole. Principal component analysis(PCA) was used to determine the number of principle components(PCs). The score vectors of the PCs were analysed using the simple-to-use interactive self-modelling mixture analysis(SIMPLISMA) algorithm to obtain spectral and concentration profiles for the reactants, intermediates and product. The vibrational frequencies of the intermediates were calculated via density functional theory(DFT) at the level of the B3LYP/6-311++G(d,p) basis set, and the geometrical configurations of the intermediates were simultaneously optimized. Finally, a reasonable synthesis mechanism for 4-amino-3,5-dimethyl pyrazole was determined based on the changes observed in the feature peaks. The results from the SIMPLISMA algorithm correlated well with the quantum chemistry calculations. This proved that the SIMPLISMA algorithm combined with ATR-FTIR can be used to determine the synthesis mechanism for 4-amino-3,5-dimethyl pyrazole and can even provide a new, useful method to explore dynamic synthesis reaction mechanisms.

Quantitative and classification analysis of slag samples by laser induced breakdown spectroscopy (LIBS) coupled with support vector machine (SVM) and partial least square (PLS) methods

Co-reporter:Tianlong Zhang, Shan Wu, Juan Dong, Jiao Wei, Kang Wang, Hongsheng Tang, Xiaofeng Yang and Hua Li

Journal of Analytical Atomic Spectrometry 2015 vol. 30(Issue 2) pp:368-374

Publication Date(Web):09 Dec 2014

DOI:10.1039/C4JA00421C

The laser induced breakdown spectroscopy (LIBS) technique coupled with a support vector machine (SVM) and partial least square (PLS) methods was proposed to perform quantitative and classification analysis of 20 slag samples. The characteristic lines (Ca, Si, Al, Mg and Ti) of LIBS spectra for slag samples can be identified based on the NIST database. At first, quantitative analysis of the major components (Fe2O3, CaO, SiO2, Al2O3, MgO and TiO2) in slag samples was completed by SVM with the full spectra as the input variable, and two parameters (kernel parameter of RBF-γ and σ2) of SVM were optimized by a grid search (GS) approach based on 5-fold cross-validation (CV). The performance of the SVM calibration model was investigated by 5-fold CV, and the prediction accuracy and root mean square error (RMSE) of SVM and PLS were employed to validate the predictive ability of the multivariate SVM calibration model in slag. The SVM model can eliminate the influence of nonlinear factors due to self-absorption in the plasma and provide a better predictive result. And then, two type of slag samples (open-hearth furnace slag and high titanium slag) were identified and classified by a partial least squares-discrimination analysis (PLS-DA) method with different input variables. Sensitivity, specificity and accuracy were calculated to evaluate the classification performance of the PLS-DA model for slag samples. It has been confirmed that the LIBS technique coupled with SVM and PLS methods is a promising approach to achieve the online analysis and process control of slag and even in the metallurgy field.

Classification of different types of slag samples by laser-induced breakdown spectroscopy (LIBS) coupled with random forest based on variable importance (VIRF)

Co-reporter:Hongsheng Tang, Tianlong Zhang, Xiaofeng Yang and Hua Li

Analytical Methods 2015 vol. 7(Issue 21) pp:9171-9176

Publication Date(Web):21 Sep 2015

DOI:10.1039/C5AY02208H

A laser induced breakdown spectroscopy (LIBS) technique coupled with random forest based on variable importance (VIRF) was proposed to perform the classification analysis of slag samples. Three types of slag samples (open-hearth furnace slag, converter slag and high titanium slag) were identified and classified by a random forest (RF) method with different pre-processing methods (normalized with maximum integrated intensity, first-order derivative and second-order derivative) and different input variables (200–300, 200–400, 200–500, 200–600, 200–700 and 200–800 nm), and the importance of the input variable was employed to improve the classification performance of the RF model for slag samples. Averaged OOB (out-of-bag) error, sensitivity, specificity and accuracy were calculated to evaluate the classification performance of the RF model for slags. Normalized by maximum integrated intensity LIBS spectra (200–500 nm) of slag samples were used as the input variable to construct the PLS-DA, SVM, RF and VIRF models for the classification analysis of slags. The VIRF model shows a better classification performance than the other three models. The LIBS technique coupled with RF perhaps is a promising approach to achieve the online analysis and process control of slag and even industrial waste recycling.

Determination of enantiomeric composition of tryptophan by using fluorescence spectroscopy combined with backward interval partial least squares

Co-reporter:Long Jiao, Shan Bing, Xiaofeng Zhang, Yunxia Wang and Hua Li

Analytical Methods 2015 vol. 7(Issue 11) pp:4535-4540

Publication Date(Web):24 Apr 2015

DOI:10.1039/C5AY00190K

The application of backward interval partial least squares (BiPLS) method to fluorescence spectroscopy analysis was studied. A method which combines BiPLS and fluorescence spectroscopy was developed for determining the enantiomeric composition of tryptophan (Trp). Fluorescence spectroscopy was utilized to measure the interaction between Trp enantiomers and bovine serum albumin, which is the chiral selector of the two enantiomers. BiPLS was used to select spectral regions and build the calibration model. In terms of BiPLS, five spectral regions were selected and used to develop the calibration model between the spectral data and enantiomeric composition of Trp. In addition, a full-spectrum PLS model and two local-spectrum PLS models were developed in order to make a comparison to the BiPLS model. The prediction performance of the established models was assessed by external test validation and leave-one-out cross-validation. The BiPLS model shows the highest prediction accuracy among these models. For the BiPLS model, the root mean square relative error of external test validation and leave-one-out validation was 6.59% and 5.67%, respectively. It is demonstrated that fluorescence spectroscopy combined with BiPLS is a practicable method for determining the enantiomeric composition of Trp at trace levels. When there is 2.50 μmol L−1 Trp in the samples, the enantiomeric composition of Trp can be accurately determined. Furthermore, the result demonstrates that spectral region selection can significantly influence the fluorescence spectroscopy analysis and BiPLS is a practicable method for the spectral region selection in fluorescence spectroscopy analysis.

Quantitative analysis of nonmetal elements in steel using laser-induced breakdown spectroscopy combined with random forest

Co-reporter:Shan Wu, Tianlong Zhang, Hongsheng Tang, Kang Wang, Xiaofeng Yang and Hua Li

Analytical Methods 2015 vol. 7(Issue 6) pp:2425-2432

Publication Date(Web):02 Feb 2015

DOI:10.1039/C4AY02601B

Laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF) was proposed for the quantitative analysis of sulfur (S) and phosphorus (P) in steel samples. The interference from the characteristic spectral lines of S and P in steel is difficult to accurately quantitatively analyse due to the influence of the multi-matrix. A RF model was utilized to compensate for the negative influence of the matrix effect. The influences of laser pulse energy and delay time on the spectral intensity were studied to improve the signal-to-noise ratio (SNR) of the analytical line for a certain element. Furthermore, the parameters (ntree and mtry) of the RF model were optimized by out-of-bag (OOB) estimation. The final RF calibration model for the quantitative analysis of S and P in steel was constructed using the spectral range (520–620 nm) as an input variable under the optimized experimental conditions. Results showed that the RF calibration model made good predictions of S (R2 = 0.9974) and P (R2 = 0.9981) compared with partial least square regression (PLSR), using the peak signals of S II 545.3 nm and P II 602.4 nm, respectively. The averaged relative errors (ARE) of S in steel were 2.69% and 3.47% for samples #8 and #9, respectively, and of P were 1.77% and 0.83% for samples #8 and #9, respectively. This confirms that the RF model is a promising approach for the quantitative detection of the nonmetal elements with LIBS technology in the field of metallurgy.

A new model for the phase correction of interferograms

Co-reporter:Xuemei Wu, Zhiqiang Liu and Hua Li

Analytical Methods 2015 vol. 7(Issue 6) pp:2399-2405

Publication Date(Web):02 Feb 2015

DOI:10.1039/C4AY02433H

A new model for the phase correction is proposed for Fourier transform spectrometer interferograms. The new model consists of three sections including a digital all-pass filter, delayer and phaser. Therefore, the new model is named the ADP model. When the ADP model is applied to phase correction, the particle swarm optimization (PSO) algorithm is applied to resolve the ADP model. Simulation experiments for the phase correction of double-sided interferograms using the ADP model based on the PSO algorithm are given. Simulation results show that it is easy to implement higher phase correction precision by using the ADP model based on the PSO algorithm with lower iteration number. In the application experiment, firstly, Fourier-transform near infrared (FT-NIR) interferograms are corrected by the Mertz method, all-pass filter method and ADP model, respectively. Secondly, the spectra calculated from the corrected interferograms are applied to predict the concentration of an ethanol solution by the PLS algorithm. The results show that the predicted error of the spectrum corrected by the ADP model is less than those of the spectra corrected by the all-pass filter method and Mertz method.

A new method for abnormal spectrum detection based on the mixed model of samples

Co-reporter:Xuemei Wu, Zhiqiang Liu, Zhang Tianlong, Li Hua

Chemometrics and Intelligent Laboratory Systems 2015 Volume 145() pp:17-21

Publication Date(Web):15 July 2015

DOI:10.1016/j.chemolab.2015.04.011

•A method for abnormal spectrum detection is proposed.•The method shows good detection performances for abnormal spectrum.•The method provides a new approach to detect the spectrometer performance.A new method for abnormal spectrum detection based on the mixed model of samples is proposed. The method can detect abnormal spectra on the condition that the content values are unknown. The method consists of four steps. Firstly, mixed vector of the prediction sample is calculated according to the mixed model of samples. Secondly, estimated spectrum of the prediction sample is calculated according to the mixed ratio and the spectrum of calibration samples. Thirdly, the difference between the estimated spectrum and the measuring spectrum is calculated. Lastly F-statistical test is carried out to detect the abnormal spectrum according to the variance. The method is compared with the MMS and PLS algorithms. In the experiment, it is assumed that the contents of the prediction samples are unknown for the new method. For MMS and PLS, the contents of the prediction samples are known, and when the prediction error is bigger than three times the root mean square error of prediction (RMSEP), the spectrum is identified as abnormal spectrum. Results from calculations show that the new method has better detection performances for abnormal spectrum caused by measurement background changes, instrumental noise increase, and the condition of detection samples containing non-calibration content than MMS and PLS algorithms. The new method provides a new approach to detect the spectrometer performance including the background changes and noise increase in advance.

Progress of Chemometrics in Laser-induced Breakdown Spectroscopy Analysis

Co-reporter:Tian-Long ZHANG, Shan WU, Hong-Sheng TANG, Kang WANG, Yi-Xiang DUAN, Hua LI

Chinese Journal of Analytical Chemistry 2015 Volume 43(Issue 6) pp:939-948

Publication Date(Web):June 2015

DOI:10.1016/S1872-2040(15)60832-5

Laser-induced breakdown spectroscopy (LIBS), a new type of element analytical technique with the advantages such as real-time, online, non-contact and multiple elements simultaneous analysis, is a frontier analytical technique in spectral analysis. However, it is still the main problem for LIBS technique to improve the accuracy of qualitative and quantitative analysis by extracting the useful information from a large number of complex LIBS data. Chemometrics is a chemical sub-discipline of multi-interdisciplinary, which has the advantages in date processing, signal analysis and pattern recognition. It can solve some complicated problems which are difficult for traditional chemical methods. In the paper, we reviewed the research progress of chemometrics methods in LIBS from the spectral data pre-processing, qualitative and quantitative analysis in recent years.Laser induced breakdown spectroscopy(LIBS) coupled with advanced chemometrics methods has been successfully applied for the quantitative and qualitative analysis of some specific samples in various fields, and chemometrics shows a great development potential in LIBS fields.

Quantitative analysis of Fe content in iron ore via external calibration in conjunction with internal standardization method coupled with LIBS

Co-reporter:Liwen Sheng;Tianlong Zhang;Kang Wang

Chemical Research in Chinese Universities 2015 Volume 31( Issue 1) pp:107-111

Publication Date(Web):2015 February

DOI:10.1007/s40242-014-4318-1

The external calibration in conjunction with internal standardization(ECIS) coupled with laser-induced breakdown spectroscopic(LIBS) technique was proposed to perform the quantitative analysis of Fe content in iron ore. The plasma temperature and the electron number density were calculated to prove that the plasma was under local thermodynamic equilibrium(LTE) conditions and to ensure that the integral intensities of Fe I lines were reasonable. In addition, the result of the quantitative analysis shows a content of (20.26±0.59)% by mass of Fe in the iron ore. It was determined by four calibration curves, drawn for four emission lines of Fe I(373.48, 373.71, 404.58 and 438.35 nm) normalized by Mn I line, base on the ECIS method which can eliminate the influence of matrix effect and improve the accuracy of quantitative analysis, compared with the standard addition method. Both the results of these two analytical methods were compared with that listed in the Standard Substance Certificate. The percentage content of Fe in the same sample of iron ore by the ECIS method was (20.17±0.08)% by mass, which shows a good performance to analyze the Fe content of iron ore in combination with LIBS.

Quantitative determination of Cr in ink by laser-induced breakdown spectroscopy(LIBS) using ZnO as adsorbent

Co-reporter:Jiao Wei;Tianlong Zhang;Juan Dong

Chemical Research in Chinese Universities 2015 Volume 31( Issue 6) pp:909-913

Publication Date(Web):2015 December

DOI:10.1007/s40242-015-5210-3

Laser-induced breakdown spectroscopy(LIBS) technique was applied to detecting chromium in ink with ZnO as adsorbent, and the LIBS spectra were preprocessed by wavelet denoising. The laser energy and delay time were optimized depending on the signal-to-noise ratio(SNR) and intensity of three analysis atomic lines(Cr 425.43 nm, Cr 427.48 nm and Cr 428.97 nm). Compared with other analysis lines, atomic line of Cr 427.48 nm was selected as the analysis line for the quantitative analysis of Cr in ink as the calibration curve of it showed a better linear relationship (correlation coefficient R2=0.9778), and the relative error of Cr in the measured ink was 52.96%. Since the single spectral line used for calibration curve method is often influenced by matrix effect and other factors, partial least squares regression(PLS) as multivariate calibration method has been applied to predicting the concentration of Cr in ink, and the relative error of Cr in the measured ink was 10.48%. The result obtained from the PLS method was better than that from the calibration curve when comparing the relative error, demonstrating that, based on adsorbent, LIBS combined with PLS provides an effective, practical and convenient technique for the determination of trace element in aqueous solution.

QSPR study on the relative retention time of polybrominated diphenyl ethers (PBDEs) by using molecular distance-edge vector index

Co-reporter:Long Jiao, Zhiwei Xue, Gangfeng Wang, Xiaofei Wang, Hua Li

Chemometrics and Intelligent Laboratory Systems 2014 Volume 137() pp:91-96

Publication Date(Web):15 October 2014

DOI:10.1016/j.chemolab.2014.06.015

•The QSPR model for the relative retention time of PBDEs is established.•The obtained model shows higher prediction accuracy than the literature models.•It is demonstrated that the MDEV index is quantitatively related to the RRT of PBDEs.The quantitative structure property relationship (QSPR) for the relative retention time (RRT) of polybrominated diphenyl ethers (PBDEs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PBDEs. The quantitative relationship between the MDEV index and RRT of PBDEs was modeled by using multivariate linear regression (MLR), partial least square (PLS) and radial basis function artificial neural network (RBF-ANN) respectively. Three QSPR models, MLR model, PLS model and RBF-ANN model, were established. External validation was carried out to assess the predictive ability of the developed models. The investigated 126 PBDEs were randomly divided into two groups: a calibration set, which comprises 88 PBDEs, and a test set, which comprises 38 PBDEs. For the MLR model, the prediction RMSRE of test set is 12.58. For the PLS model, the prediction RMSRE of test set is 12.58. For the RBF-ANN model, the prediction RMSRE of test set is 8.21. It is demonstrated that the MDEV index of PBDEs is quantitatively related to the RRT of PBDEs. The developed three models are all practicable for predicting the RRT of PBDEs. Compared with the MLR and PLS models, the RBF-ANN model shows higher prediction accuracy.

A simple method for multivariate calibration with minimization of the prediction relative error

Co-reporter:Xuemei Wu, Zhiqiang Liu and Hua Li

Analytical Methods 2014 vol. 6(Issue 12) pp:4056-4060

Publication Date(Web):31 Mar 2014

DOI:10.1039/C4AY00620H

A simple method addressing the problem of minimizing the prediction relative error is proposed for multivariate calibration. The method is based on the use of back-propagation artificial neural network (BP-ANN). The regression objective of the simple method is to minimize the prediction relative error by changing the output values of BP-ANN. With both theoretical support and analysis of near infrared spectroscopic data and ultraviolet spectroscopic data, it is demonstrated that the simple method produced lower prediction relative error than partial least squares (PLS), principal component regression (PCR), and BP-ANN methods for the system with a wide content range. In addition, when we consider the value of the root mean square error of prediction (RMSEP), four methods were found to have a similar prediction performance. The simple method can predict low content more accurately for the system with a wide content range.

A novel algorithm for linear multivariate calibration based on the mixed model of samples

Co-reporter:Xuemei Wu, Zhiqiang Liu, Hua Li

Analytica Chimica Acta 2013 Volume 801() pp:43-47

Publication Date(Web):1 November 2013

DOI:10.1016/j.aca.2013.09.043

•A novel algorithm is proposed for linear multivariate calibration.•The algorithm shows good performance of anti-background interference.•The algorithm shows good robustness.We present a novel algorithm for linear multivariate calibration that can generate good prediction results. This is accomplished by the idea of that testing samples are mixed by the calibration samples in proper proportion. The algorithm is based on the mixed model of samples and is therefore called MMS algorithm. With both theoretical support and analysis of two data sets, it is demonstrated that MMS algorithm produces lower prediction errors than partial least squares (PLS2) model, has similar prediction performance to PLS1. In the anti-interference test of background, MMS algorithm performs better than PLS2. At the condition of the lack of some component information, MMS algorithm shows better robustness than PLS2.

QSPR studies on the aqueous solubility of PCDD/Fs by using artificial neural network combined with stepwise regression

Co-reporter:Long Jiao, Hua Li

Chemometrics and Intelligent Laboratory Systems 2010 Volume 103(Issue 2) pp:90-95

Publication Date(Web):15 October 2010

DOI:10.1016/j.chemolab.2010.05.019

A practicable quantitative structure property relationship (QSPR) model for predicting aqueous solubility, Sw, of 23 polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was developed. Linear artificial neural network (L-ANN) was used to develop the calibration model of Sw. The input variables of L-ANN were selected from 11 structural descriptors of the investigated PCDD/Fs by using stepwise regression. Leave one out cross validation and split-sample validation were carried out to assess the predictive performance of the developed model. The results of leave one out cross validation and split-sample validation are both satisfactory, which verify the reliability and practicability of the developed model. It is demonstrated that L-ANN combined with stepwise regression is a practicable method for developing QSPR model for Sw of PCDD/Fs. Additionally, stepwise regression is shown to be a practicable approach for the selection of input variables when developing a QSPR model with L-ANN.

Quantification of components in overlapping peaks from capillary electrophoresis by using continues wavelet transform method

Co-reporter:Long Jiao, Suya Gao, Fang Zhang, Hua Li

Talanta 2008 Volume 75(Issue 4) pp:1061-1067

Publication Date(Web):30 May 2008

DOI:10.1016/j.talanta.2008.01.016

Artificial neural networks based on principal component analysis input selection for quantification in overlapped capillary electrophoresis peaks

Co-reporter:Yaxiong Zhang, Hua Li, Aixia Hou, Josef Havel

Chemometrics and Intelligent Laboratory Systems 2006 Volume 82(1–2) pp:165-175

Publication Date(Web):26 May 2006

DOI:10.1016/j.chemolab.2005.08.012

The application of three different kinds of artificial neural networks (ANN) based on principal component analysis (PCA) input selection for quantification of overlapped peaks in micellar electrokinetic capillary chromatography (MECC) is investigated. In the case of overlapped peaks, ANN based on PCA input selection were proved to be a promising approach for quantification of the corresponding components. Both the spectra and the electrophoretograms of the unseparated analytes were used as the multivariate input data. The two kinds of data were both suitable for quantification of overlapped peaks by ANN based on PCA input selection. In the study, it was also shown that the input selection based on PCA for the three kinds of ANN could improve the precision of quantification of the corresponding components in both completely and partially overlapped peaks to some extent.

Determination of tetracycline, chlortetracycline and oxytetracycline by flow injection with inhibitory chemiluminescence detection using copper(II) as a probe ion

Co-reporter:Suqin Han;Erbao Liu

Luminescence 2006 Volume 21(Issue 2) pp:106-111

Publication Date(Web):17 JAN 2006

DOI:10.1002/bio.893

This paper reports an indirect flow-injection (FI) method for the determination of the tetracycline drugs (TCs), tetracycline (TC), chlortetracycline (CTC) and oxytetracycline (OTC), using copper(II) as a probe ion. The method was based on the inhibition caused by these TCs to the copper(II)-catalysed chemiluminescence (CL) reaction between luminol and H₂O₂. The CL reaction was induced on-line and injection of the sample produced negative peaks as a result of the copper(II) complexation or displacement by the analytes. The height of the peaks was proportional to the drug concentration in the sample. The choice of the catalyst ion, the concentration of luminol, H₂O₂ and copper(II) are discussed. The linear range was 3.6 × 10⁻⁸–1.0 × 10⁻⁵, 1.1 × 10⁻⁷–1.0 × 10⁻⁵ and 1.9 × 10⁻⁷–1.0 × 10⁻⁵ mol/L for TC, CTC and OTC, respectively. The detection limit was 5.0 × 10⁻⁹ mol/L for TC, 1.0 × 10⁻⁸ mol/L for CTC and 2.0 × 10⁻⁸ mol/L for OTC (3σ), respectively. The method was applied to the determination of TCs in pharmaceutical preparations and human urine with recoveries in the range 95–105%. Copyright © 2006 John Wiley & Sons, Ltd.

The study of the relationship between the new topological index Am and the gas chromatographic retention indices of hydrocarbons by artificial neural networks

Co-reporter:Hua Li, Ya Xiong Zhang, Lu Xu

Talanta 2005 Volume 67(Issue 4) pp:741-748

Publication Date(Web):15 October 2005

DOI:10.1016/j.talanta.2005.03.031

The newly developed topological indices Am1–Am3 and the molecular connectivity indices mX were applied to multivariate analysis in structure–property correlation studies. The topological indices calculated from the chemical structures of some hydrocarbons were used to represent the molecular structures. The prediction of the retention indices of the hydrocarbons on three different kinds of stationary phase in gas chromatography can be achieved applying artificial neural networks and multiple linear regression models. The results from the artificial neural networks approach were compared with those of multiple linear regression models. It is shown that the predictive ability of artificial neural networks is superior to that of multiple linear regression method under the experimental conditions in this paper. Both the topological indices 2X and Am1 can improve the predicted results of the retention indices of the hydrocarbons on the stationary phase studied.

Artificial neural networks based on genetic input selection for quantification in overlapped capillary electrophoresis peaks

Co-reporter:Yaxiong Zhang, Hua Li, Aixia Hou, Josef Havel

Talanta 2005 Volume 65(Issue 1) pp:118-128

Publication Date(Web):15 January 2005

DOI:10.1016/j.talanta.2004.05.050

Modeling of the relationship between electroosmotic flow and separation parameters in capillary zone electrophoresis using artificial neural networks and experimental design

Co-reporter:Ya Xiong Zhang, Hua Li, Josef Havel

Talanta 2005 Volume 65(Issue 4) pp:853-860

Publication Date(Web):28 February 2005

DOI:10.1016/j.talanta.2004.08.016

The prediction of migration time of electroosmotic flow (EOF) marker was achieved by applying artificial neural networks (ANN) model based on principal component analysis (PCA) and standard normal distribution simulation to the input variables. The voltage of performance, the temperature in the capillary, the pH and the ionic strength of background electrolytes (BGE) were applied as the input variables to ANN. The range of the performance voltage studied was from 15 to 27 kV, and that of the temperature in the capillary was from 20 to 30 °C. For the pH values studied, the range was from 5.15 to 8.04. The range of the ionic strength investigated in this paper was from 0.040 to 0.097. The prediction abilities of ANN with different pre-processing procedure to the input variables were compared. Under the same performance conditions, the average prediction error of the migration time of the EOF marker was 5.46% with RSD = 1.76% according to 10 parallel runs of the optimized ANN structure by the proposed approach, and that of the 10 parallel predictions of the optimal ANN structure for the different performance conditions was 12.95% with RSD = 2.29% according to the proposed approach. The study showed that the proposed method could give better predicted results than other approaches discussed.

Chemiluminescence of cobalt(II)-catalysed auto-oxidation of sulphite and its application to cobalt analysis

Co-reporter:Xiao-Feng Yang

Luminescence 2004 Volume 19(Issue 5) pp:253-258

Publication Date(Web):2 AUG 2004

DOI:10.1002/bio.762

The oxidation of sulphite by dissolved oxygen in aqueous solution catalysed by cobalt(II) was investigated. A weak chemiluminescence (CL) emission was observed when the reaction took place in a strong alkaline solution without any special CL reagent. Further studies showed that in the presence of ﬂuorescein sodium the CL signal was enhanced signiﬁcantly. The CL emission is linear with Co(II) concentration in the range 0.6–80 nmol/L and the detection limit is 0.3 nmol/L. In addition to Co(II), other transition metal ions were also tested, and the results showed that the proposed system was highly selective for Co(II). The method was successfully applied to the determination of Co(II) in pharmaceutical preparations. The possible CL mechanism was also discussed. Copyright © 2004 John Wiley & Sons, Ltd.

Flow injection chemiluminescence determination of dihydralazine sulphate based on permanganate oxidation sensitized by rhodamine B

Co-reporter:Xiao-Feng Yang;Dong-Bing Wu

Luminescence 2004 Volume 19(Issue 6) pp:322-327

Publication Date(Web):26 OCT 2004

DOI:10.1002/bio.781

A novel ﬂow injection chemiluminescence (CL) method for the determination of dihydralazine sulphate (DHZS) is described. The method is based on the CL produced during the oxidation of DHZS by acidic permanganate solution in the presence of rhodamine B. Rhodamine B is suggested as a ﬂuorescing compound for the energy-transferred excitation. The CL emission allows quantitation of DHZS concentration in the range 5–800 ng/mL, with a detection limit of 1.9 ng/mL (3σ). The experimental conditions for the CL reaction are optimized and the possible reaction mechanism is discussed. The method has been applied to the determination of DHZS in pharmaceutical preparations and compares well with the high performance liquid chromatography (HPLC) method. Copyright © 2004 John Wiley & Sons, Ltd.

Flow-injection chemiluminescence determination of dihydralazine sulfate based on hexacyanoferrate(III) oxidation sensitized by eosin Y

Co-reporter:Xiao-Feng Yang, Hua Li

Talanta 2004 Volume 64(Issue 2) pp:478-483

Publication Date(Web):8 October 2004

DOI:10.1016/j.talanta.2004.03.013

A novel flow-injection chemiluminescence (CL) method for the determination of dihydralazine sulfate (DHZS) is described. The method is based on the reaction between DHZS and hexacyanoferrate(III) in alkaline solution to give weak CL signal, which is dramatically enhanced by eosin Y. The CL emission allows quantitation of DHZS concentration in the range 0.02–2.8 μg ml−1 with a detection limit (3σ) of 0.012 μg ml−1. The experimental conditions for the CL reaction are optimized and the possible reaction mechanism is discussed. The method has been applied to the determination of DHZS in pharmaceutical preparations and compared well with the high performance liquid chromatography (HPLC) method.

Fluorimetric determination of hemoglobin using spiro form rhodamine B hydrazide in a micellar medium

Co-reporter:Xiao-Feng Yang, Xiang-Qun Guo, Hua Li

Talanta 2003 Volume 61(Issue 4) pp:439-445

Publication Date(Web):12 November 2003

DOI:10.1016/S0039-9140(03)00306-0

A novel fluorimetric method for the determination of hemoglobin (Hb) using spiro form rhodamine B hydrazide (RBH) as fluorogenic reagent in the presence of sodium dodecylbenzene sulfonate (SDBS) surfactant micelles is described. The method employs the reaction of Hb with RBH, a colorless, non-fluorescent spirolactam hydrazide in SDBS micelles to generate highly fluorescent product rhodamine B and hence leads to a large increase in fluorescence intensity. The fluorescence increase is linearly related to the concentration of Hb in the range 0.2–12.0 nmol l−1 with a detection limit of 0.086 nmol l−1 (3σ). The optimal conditions for the detection of Hb are evaluated and the possible detection mechanism is also discussed. The proposed method has been applied to the determination of Hb in human blood.

Simultaneous determination of captopril and hydrochlorothiazide by time-resolved chemiluminescence with artificial neural network calibration

Co-reporter:Han-Chun Yao, Min Sun, Xiao-Feng Yang, Zhen-Zhong Zhang, Hua Li

Journal of Pharmaceutical Analysis (February 2011) Volume 1(Issue 1) pp:32-38

Publication Date(Web):1 February 2011

DOI:10.1016/S2095-1779(11)70006-5

The combined use of chemometrics and chemiluminescence (CL) measurements, with the aid of the stopped-flow mixing technique, developed a simple time-resolved CL method for the simultaneous determination of captopril (CPL) and hydrochlorothiazide (HCT). The stopped-flow technique in a continuous-flow system was employed in this work in order to emphasize the kinetic differences between the two analytes in cerium (IV)-rhodamine 6G CL system. After the flow was stopped, an initial rise of CL signal was observed for HCT standards, while a direct decay of CL signal was obtained for CPL standards. The mixed CL signal was monitored and recorded on the whole process of continuousflow/stopped-flow, and the obtained data were processed by the chemometric approach of artificial neural network. The relative prediction error (RPE) of CPL and HCT was 5.9% and 8.7%, respectively. The recoveries of CPL and HCT in tablets were found to fall in the range between 95% and 106%. The proposed method was successfully applied to the simultaneous determination of CPL and HCT in a compound pharmaceutical formulation.

Development of a red fluorescent light-up probe for highly selective and sensitive detection of vicinal dithiol-containing proteins in living cells

Co-reporter:Yuanyuan Wang, Xiao-Feng Yang, Yaogang Zhong, Xueyun Gong, Zheng Li and Hua Li

Chemical Science (2010-Present) 2016 - vol. 7(Issue 1) pp:

Publication Date(Web):

DOI:10.1039/C5SC02824H

Quantitative and classification analysis of slag samples by laser induced breakdown spectroscopy (LIBS) coupled with support vector machine (SVM) and partial least square (PLS) methods

Co-reporter:Tianlong Zhang, Shan Wu, Juan Dong, Jiao Wei, Kang Wang, Hongsheng Tang, Xiaofeng Yang and Hua Li

Journal of Analytical Atomic Spectrometry 2015 - vol. 30(Issue 2) pp:NaN374-374

Publication Date(Web):2014/12/09

DOI:10.1039/C4JA00421C

A simple method for multivariate calibration with minimization of the prediction relative error

Co-reporter: