Bin Wang

Jilin University, Yung-chi, Jilin Sheng, China

Are you Bin Wang?

Claim your profile

Publications (7)15.56 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, a novel chemometric method was developed for rapid, accurate, and quantitative analysis of cefalexin in samples. The experiments were carried out by using the short near-infrared spectroscopy coupled with artificial neural networks. In order to enhancing the predictive ability of artificial neural networks model, a modified genetic algorithm was used to select fixed number of wavelength.
    Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy 03/2013; 109C:308-312. DOI:10.1016/j.saa.2013.02.047 · 2.13 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: A method for quantitative analysis of phenoxymethylpenicillin potassium powder on the basis of near-infrared (NIR) spectroscopy is investigated by using orthogonal projection to latent structures (O-PLS) combined with artificial neural network (ANN). Being a preprocessing method, O-PLS can remove systematic orthogonal variation from a given data set X without disturbing the correlation between X and the response set y. In this paper, O-PLS method was applied to preprocess the original spectral data of phenoxymethylpenicillin potassium powder, and the filtered data was used to establish the ANN model. In this model, the concentration of phenoxymethylpenicillin potassium as the active component was determined. The degree of approximation was employed as the selective criterion of the optimum network parameters. In order to compare with O-PLS-ANN model, the calibration models that use the original spectra and different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) of the spectra were also designed. Experimental results show that O-PLS-ANN model is the best.
    Vibrational Spectroscopy 11/2009; 51(2):199-204. DOI:10.1016/j.vibspec.2009.04.007 · 1.55 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, a novel chemometric method was developed for rapid, accurate, and quantitative analysis of cefalexin. The experiment was carried out by using the near-infrared spectrometry coupled to multivariate calibration (partial least squares and artificial neural nets). The wavelength selection through a modified genetic algorithm with fixed number of select variables would enhance the predictive ability when applying artificial neural networks model. (c) 2009 Elsevier B.V. All rights reserved.
    Chemometrics and Intelligent Laboratory Systems 07/2009; 97(2):127-131. DOI:10.1016/j.chemolab.2009.03.003 · 2.38 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: A method for quantitative analysis of diclofenac sodium powder on the basis of near-infrared (NIR) spectroscopy is investigated by using of orthogonal projection to latent structures (O-PLS) combined with artificial neural network (ANN). 148 batches of different concentrations diclofenac sodium samples were divided into three groups: 80 training samples, 46 validation samples and 22 test samples. The average concentration of diclofenac sodium was 27.80%, and the concentration range of all the samples was 15.01-40.55%. O-PLS method was applied to remove systematic orthogonal variation from original NIR spectra of diclofenac sodium samples, and the filtered signal was used to establish ANN model. In this model, the concentration of diclofenac sodium was determined. The degree of approximation was employed as selective criterion of the optimum network parameters. In order to compare with O-PLS-ANN model, principal component artificial neural network (PC-ANN) model and calibration models that use different preprocessing methods (first derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) of the original spectra were also designed. In addition, partial least squares regression (PLS) models were also established to compare with ANN models. Experimental results show that O-PLS-ANN model is the best.
    Journal of pharmaceutical and biomedical analysis 05/2009; 50(2):158-63. DOI:10.1016/j.jpba.2009.04.014 · 2.83 Impact Factor
  • Nan Qu, Hong Mi, Bin Wang, Yulin Ren
    [Show abstract] [Hide abstract]
    ABSTRACT: A new method was applied to nondestructive quantitative analysis of pharmaceutical samples with different concentrations on the basis of the near-infrared spectral data. By the proposed method powerful radial basis function (RBF) networks can be produced based on a genetic algorithm (GA), which is applied to auto-configuring the structure of the networks to optimize the near-infrared wavelength regions used and variables employed in building radial basis function networks. Estimation and calibration of the sample concentration via NIR spectroscopy were made with the aid of genetic algorithm-radial basis function (GA-RBF) network models based on conventional spectra, standard normal variate (SNV), multiplicative scatter correction (MSC) and the first-derivative spectra, various optimum models of which were established and compared. Experiment results show that GA-RBF networks can give robust and satisfactory prediction and the GA-RBF model based on SNV preprocessing spectra was found to provide the best performance. Therefore, the proposed method may have significant potential for use in nondestructive quantitative analysis of pharmaceutical samples.
    Journal of the Taiwan Institute of Chemical Engineers 03/2009; DOI:10.1016/j.jtice.2008.08.002 · 3.00 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper is concerned with the rapid and non-destructive quantitative analysis of cimetidine in single intact tablets by diffuse reflectance spectroscopy. Support vector machines (SVM) are introduced to model multivariate, non-linear systems of calibration samples by radical basis functions. Short-wave near-infrared spectra ranging 760–1100nm are processed by SVM. Wavelet method has been employed to minimize the influence of noise. Measurement errors of independent testing set by SVM compared to partial least squares (PLS) give relatively reasonable results. Experiments show that SVM with wavelet denoising pretreatment is an effective method and requires less number of calibration samples.
    Vibrational Spectroscopy 03/2009; 49(2):274-277. DOI:10.1016/j.vibspec.2008.10.008 · 1.55 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: A new method orthogonal projection to latent structures (O-PLS) combined with artificial neural networks is investigated for non-destructive determination of Ampicillin powder via near-infrared (NIR) spectroscopy. The modern NIR spectroscopy analysis technique is efficient, simple and non-destructive, which has been used in chemical analysis in diverse fields. Be a preprocessing method, O-PLS provides a way to remove systematic variation from an input data set X not correlated to the response set Y, and does not disturb the correlation between X and Y. In this paper, O-PLS pretreated spectral data was applied to establish the ANN model of Ampicillin powder, in this model, the concentration of Ampicillin as the active component was determined. The degree of approximation was employed as the selective criterion of the optimum network parameters. In order to compare the OPLS-ANN model, the calibration models that using first-derivative and second-derivative preprocessing spectra were also designed. Experimental results showed that the OPLS-ANN model was the best.
    Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy 08/2008; 71(5):1695-700. DOI:10.1016/j.saa.2008.06.021 · 2.13 Impact Factor