共查询到20条相似文献,搜索用时 15 毫秒
1.
Wang B Liu G Fei Q Zuo Y Ren Y 《Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy》2009,71(5):1695-1700
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. 相似文献
2.
Diffuse reflectance near-infrared (NIR) spectroscopy is a technique widely used for rapid and non-destructive analysis of solid samples. A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drug has been developed by using artificial neural network (ANN) on near-infrared (NIR) spectroscopy. An ANN containing three layers of nodes was trained. Various ANN models based on pretreated spectra (first-derivative, second-derivative and standard normal variate; SNV) were tested and compared, respectively. In the models the concentration of paracetamol and caffeine as active principles of compound paracetamol and diphenhydramine hydrochloride powder was determined simultaneously. Partial least squares regression (PLS) multivariate calibrations were also used, which were compared with ANN. The best model was obtained at first-derivative spectra. We have also discussed the parameters that affected the networks and predicted the test set (unknown) specimens. The degree of approximation, a new evaluation criteria of the network were employed, which proved the accuracy of the predicted results. 相似文献
3.
4.
Okumura T Nakazono M Otsuka M Takayama K 《Colloids and surfaces. B, Biointerfaces》2006,49(2):153-157
Measurement precision based on homogeneous and accurate standard samples has been reported to result in significant improvement in the sensitivity and accuracy of the quantitative analysis of polymorphic mixtures. The purpose of this study was to further improve the accuracy of the quantitation based on data processing by artificial neural networks (ANNs), using such high quality standard samples. Homogeneous powder mixtures of - and γ-forms of indomethacin (IMC) at various ratios (0–50% -form content) were subjected to X-ray powder diffractometry. The two diffraction peaks selected as the best combination in multiple linear regression (MLR) were used in the ANN with an extended Kalman filter as a training algorithm. The results obtained by ANN had better predictive accuracy at lower contents (0–5%) compared to those of MLR. ANNs for the diffraction data based on high quality standard samples provide an extremely precise and accurate quantification for polymorphic mixtures. 相似文献
5.
Zhimei Wang 《Microchemical Journal》2008,89(1):52-57
In the construction of a neural network, most attentions have been paid to the selection of the architecture, the selection of the learning parameters and the network validation while the selection of input variables shared little. This study focused on the selection of input variables by various data pre-treatment for constructing ANN models. The results showed that the validation results differed from each other when different data-pretreatment methods combined with near-infrared spectroscopy (NIRS) to build a model using artificial neural network (ANN) for quality control of paracetamol in coldrex. And wavelet coefficients after orthogonal signal correction (OSC) in the ANN models reduced RMSEP by up to 77% compared to ANN models using derivatives combined with PCA pretreatment. The selection of input variables has potent to improve the calibration ability of ANN, and the model can be used for pressure reduction of quality control in the pharmaceutical industry. 相似文献
6.
Classical multivariate analysis techniques such as factor analysis and stepwise linear discriminant analysis and artificial neural networks method (ANN) have been applied to the classification of Spanish denomination of origin (DO) rose wines according to their geographical origin. Seventy commercial rose wines from four different Spanish DO (Ribera del Duero, Rioja, Valdepeñas and La Mancha) and two successive vintages were studied. Nineteen different variables were measured in these wines. The stepwise linear discriminant analyses (SLDA) model selected 10 variables obtaining a global percentage of correct classification of 98.8% and of global prediction of 97.3%. The ANN model selected seven variables, five of which were also selected by the SLDA model, and it gave a 100% of correct classification for training and prediction. So, both models can be considered satisfactory and acceptable, being the selected variables useful to classify and differentiate these wines by their origin. Furthermore, the casual index analysis gave information that can be easily explained from an enological point of view. 相似文献
7.
In this paper a continuous-flow chemiluminescence (CL) system with artificial neural network calibration is proposed for simultaneous
determination of rifampicin and isoniazid. This method is based on the different kinetic spectra of the analytes in their
CL reaction with alkaline N-bromosuccinimide as oxidant. The CL intensity was measured and recorded every second from 1 to 300 s. The data obtained were
processed chemometrically by use of an artificial neural network. The experimental calibration set was 20 sample solutions.
The relative standard errors of prediction for both analytes were approximately 5%. The proposed method was successfully applied
to the simultaneous determination of rifampicin and isoniazid in a combined pharmaceutical formulation. 相似文献
8.
A simple and sensitive kinetic method for the determination of traces of mercury (70-760 ng ml−1) based on its inhibitory effect on the addition reaction between methyl green and sulfite ion is proposed. The reaction was monitored spectrophotometrically by measuring the decrease in absorbance of methyl green at 596 nm between 2 and 4 min using a fixed time method. Artificial neural networks with back propagation algorithm coupled with an orthogonal array design were applied to the modeling of the proposed kinetic system and optimization of experimental conditions. An orthogonal design was utilized to design the experimental protocol, in which pH, concentration of sulfite, temperature, and concentration of methyl green were varied simultaneously. Optimum experimental conditions in term of sensitivity were generated by using ANNs. The rate of decrease in absorbance is inversely proportional to the concentration of Hg(II) over entire concentration range tested (100-550 ng ml−1) with a detection limit of 45 ng ml−1 and a relative standard deviation at 200-400 ng ml−1 Hg(II) of 3.2% (n=5). A simple preconcentration step improved the limit of detection and linear dynamic range of the method to about 8 and 12-760 ng ml−1, respectively, by about 10 times enrichment of mercury between 12 and 75 ng ml−1. The method was based on enrichment of Hg(II) from dilute samples on an anionic ion exchanger fixed on a plastic strip and was applied to the determination of Hg(II) in environmental samples with satisfactory results. 相似文献
9.
A piezoelectric chemical sensor array was developed using four quartz crystals. Gas chromatographic stationary phases were used as sensing materials and the array was connected to an artificial neural network (ANN). The application of the ANN method proved to be particularly advantageous if the measured property (mass, concentration, etc.) should not be connected exactly to the signal of the transducers of the piezoelectric sensor. The optimum structure of neural network was determined by a trial and error method. Different structures were tried with several neurons in the hidden layer and the total error was calculated. The optimum values of primary weight factors, learning rate (η=0.15), momentum term (μ=0.9), and the sigmoid parameter (β=1) were determined. Finally, three hidden neurons and 900 training cycles were applied. After the teaching process the network was used for identification of taught analytes (acetone, benzene, chloroform, pentane). Mixtures of organic compounds were also analysed and the ANN method proved to be a reliable way of differentiating the sensing materials and identifying the volatile compounds. 相似文献
10.
Fourier transform infrared (FTIR) spectroscopy has being emphasised as a widespread technique in the quick assess of food components. In this work, procyanidins were extracted with methanol and acetone/water from the seeds of white and red grape varieties. A fractionation by graded methanol/chloroform precipitations allowed to obtain 26 samples that were characterised using thiolysis as pre-treatment followed by HPLC-UV and MS detection. The average degree of polymerisation (DPn) of the procyanidins in the samples ranged from 2 to 11 flavan-3-ol residues. FTIR spectroscopy within the wavenumbers region of 1800-700 cm−1 allowed to build a partial least squares (PLS1) regression model with 8 latent variables (LVs) for the estimation of the DPn, giving a RMSECV of 11.7%, with a R2 of 0.91 and a RMSEP of 2.58. The application of orthogonal projection to latent structures (O-PLS1) clarifies the interpretation of the regression model vectors. Moreover, the O-PLS procedure has removed 88% of non-correlated variations with the DPn, allowing to relate the increase of the absorbance peaks at 1203 and 1099 cm−1 with the increase of the DPn due to the higher proportion of substitutions in the aromatic ring of the polymerised procyanidin molecules. 相似文献
11.
Vianney O. Santos Jr. 《Analytica chimica acta》2005,547(2):188-196
Diesel properties determined by ASTM reference methods as cetane index, density, viscosity, distillation temperatures at 50% (T50) and 85% (T85) recovery, and the total sulfur content (%, w/w) were modeled by FTIR-ATR, FTNIR, and FT-Raman spectroscopy using partial last square regression (PLS) and artificial neural network (ANN) spectral analysis. In the PLS models, 45 diesel samples were used in the training group and the other 45 samples were used in the validation. In the ANN analysis a modular feedforward network was used. Sixty diesel samples were used in the neural network training and other 30 samples were used in the validation. Two different ATR configurations were compared in the FTIR, a conventional (ATR1) and an immersion (ATR2) cell. The ATR1 cell presented the best results, with smaller prediction errors (root mean square error of prediction, RMSEP). The comparison of the three PLS models (FTIR-ATR1, FTNIR, and FT-Raman) shows that reasonable values of R2 and RMSEP were obtained by the FTIR-ATR1 and FTNIR models in the evaluation of density, viscosity, and T50. The PLS/FT-Raman models presented reasonable results only for the T50 property. None of the techniques was able to generate suitable PLS calibration models for the determination of sulfur content. The ANN/FT-Raman models presented the best performances, with all models presenting R2-values above 85% some of them with RMSEP values significantly smaller than those obtained with FTIR-ATR and FTNIR. The ANN/FT-Raman and ANN/FTIR-ATR1 models were able to estimate the total sulfur content of diesel with 0.01% (w/w) accuracy. 相似文献
12.
In this article, a continuous-flow chemiluminescence (CL) system with artificial neural network calibration is proposed for simultaneous determination of three organophosphorus pesiticides residues. This method is based on the fact that organophosphorus pesticides can be decomposed into orthophosphate with potassium peroxodisulphate as oxidant under ultraviolet radiation and that the decomposing kinetic characteristics of the organophosphorus pesticides with different molecular structure are significantly different. The produced orthophosphate can react with molybdate and vanadate to form the vanadomolybdophosphoric heteropoly acid, which can oxidize luminol to produce intense CL emission. The CL intensity of the solution was measured and recorded every 2 s in the range of 0-250 s. The obtained data were processed chemometrically by use of a three-layered feed-forward artificial neural network trained by back-propagation learning algorithm, in which input node, hidden node and output nodes were 65, 21 and 3, respectively. The proposed multi-residue analysis method was successfully applied to the simultaneous determination of the three organophosphorus pesticides residue in some vegetables samples. 相似文献
13.
Monika Michel Luke Chimuka Tomasz Kowalkowski Ewa M. Cukrowska Bogusław Buszewski 《Journal of separation science》2013,36(5):986-991
An artificial neural network model of supported liquid membrane extraction process with a stagnant acceptor phase is proposed. Triazine herbicides and phenolic compounds were used as model compounds. The model is able to predict the compound extraction efficiency within the same family based on the octanol–water partition coefficient, water solubility, molecular mass and ionisation constant of the compound. The network uses the back‐propagation algorithm for evaluating the connection strengths representing the correlations between inputs (octanol–water partition coefficients logP, acid dissociation constant pKa, water solubility and molecular weight) and outputs (extraction efficiency in dihexyl ether and undecane as organic solvents). The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be smaller than ±3%. Moreover, standard statistical methods were applied for exploration of relationships between studied parameters. 相似文献
14.
The present study has aimed at providing new insight into short-wavelength near-infrared (SW-NIR) spectroscopy (780–1100 nm) for non-destructive quantitative analysis of acetylspiramycin (macrolide antibiotics) powder by using artificial neural networks (ANNs). Presently, it was shown the third vibrational overtone of the CH stretching band can be used to quantitatively determine constituents in pharmaceutical. The third overtone referred to as the SW-NIR region ranges from 780 nm to 1100 nm. In this paper, 156 experimental samples of acetylspiramycin powder were analyzed using ANNs in the 780–1100 nm region of SW-NIR spectra. Four different pretreated methods (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to three sets of SW-NIR spectra of powder samples. The results presented here demonstrate that the SW-NIR region is promising for the fast and reliable determination of major component in pharmaceutical analysis. Degree of approximation as an evaluation criterion of the network was employed, which proved the accuracy of the predicted results. 相似文献
15.
Mohammadreza Khanmohammadi Amir Bagheri Garmarudi Keyvan Ghasemi Miguel de la Guardia 《Microchemical Journal》2009,91(1):47-52
A method has been introduced for quantitative determination of protein content in yogurt samples based on the characteristic absorbance of protein in 1800-1500 cm− 1 spectral region by mid-FTIR spectroscopy and chemometrics. Successive Projection Algorithm (SPA) wavelength selection procedure, coupled with feed forward Back-Propagation Artificial Neural Network (BP-ANN) model was the benefited chemometric technique. Relative Error of Prediction (REP) in BP-ANN and SPA-BP-ANN methods for training set was 7.25 and 3.70 respectively. Considering the complexity of the sample, the ANN model was found to be reliable, while the proposed method is rapid and simple, without any sample preparation step. 相似文献
16.
Chen-Bo Cai Hong-Wei YangBo Wang Yong-Yuan TaoMei-Qiong Wen Lu Xu 《Vibrational Spectroscopy》2011,56(2):202-209
This paper demonstrates the application of near-infrared (NIR) process analysis to study gas-solid adsorption process non-invasively: its experimental setup, data treatment, and potentials as a convenient tool to investigate the gas-solid adsorption process. The experimental setup includes a differential adsorption bed (DAB) monitored by a NIR spectrometer via an optical fiber probe, which makes it convenient and reliable to construct adsorption mass-transfer models. A chemometrics strategy based on back propagation-artificial neural network (BP-ANN) and partial least squares (PLS) has been developed to treat NIR spectra collected during the adsorption process because of the obvious nonlinearity in concentration prediction. This nonlinear problem results from the great concentration variation of the adsorbate adsorbed by the adsorbent during the whole adsorption process, the extraordinarily low concentration of the adsorbed adsorbate at the beginning of the process, and probably NIR distinction between the adsorbate on the first adsorption layer at the beginning of the process and that on the other layers afterward. With the strategy, NIR spectra are pretreated with PLS for data compression and noise reduction, and then a BP-ANN is built as the nonlinear calibration model. As compared with linear calibration algorithm, our strategy has the higher predication ability for the whole adsorption process, even with less calibration samples. Finally, as an example the kinetics of aniline-silica gel adsorption process has been studied through the experimental setup and chemometrics strategy. 相似文献
17.
A piezoelectric chemical sensor array was developed using four quartz crystals. Gas chromatographic stationary phases were used as sensing materials and the array was connected to an artificial neural network (ANN). The application of the ANN method proved to be particularly advantageous if the measured property (mass, concentration, etc.) should not be connected exactly to the signal of the transducers of the piezoelectric sensor. The optimum structure of neural network was determined by a trial and error method. Different structures were tried with several neurons in the hidden layer and the total error was calculated. The optimum values of primary weight factors, learning rate (η=0.15), momentum term (μ=0.9), and the sigmoid parameter (β=1) were determined. Finally, three hidden neurons and 900 training cycles were applied. After the teaching process the network was used for identification of taught analytes (acetone, benzene, chloroform, pentane). Mixtures of organic compounds were also analysed and the ANN method proved to be a reliable way of differentiating the sensing materials and identifying the volatile compounds. 相似文献
18.
Summary The rate constants of the oxidation of CO on a number of pure and La2O3 doped NiO/Al2O3 solid catalysts were correlated with the mole percent of dopant, calcinations temperature, surface area, pore volume and pore mouth diameter by an artificial neural network simulator. The cross validation method had to be used due to the scarcity of the data. A three-layer network with 3 nodes in the hidden layer was found to simulate the system well. 相似文献
19.
概率神经网络和FTIR光谱用于食道癌的辅助分析 总被引:1,自引:1,他引:0
利用正常与相应癌化食道组织的主要FTIR特征峰aυs,CH3、sυ,CH2、σCH2、aυs,po4-、υc-o、sυ,po2-及sυ,磷酸化蛋白作为概率神经网络的输入向量,对网络的主要参数(网络径向基函数分布spread(0~5))、输入向量和网络表现(m ean accurate rate of recogn ition)之间的关系进行了研究。主要结论如下:i)无论输入向量是哪种特征频率的组合,其平均识别正确率都高于71.40%;ii)当输入向量为特征频率sυ,po2、sυ,磷酸化蛋白或υc-0、sυ,po2、sυ,磷酸化蛋白时,网络表现较佳,平均识别正确率较好。当spread介于1.4~2.3时,两者均达到网络具有的最高平均识别正确率(85.71%);iii)大多数情况下,网络的平均识别正确率与spread之间呈现二个高峰的特征,即spread介于0.1~0.3和1.5~5.0之间时,网络均具有较高的平均识别正确率。研究表明,以傅里叶变换红外光谱的主要特征峰为概率神经网络的输入向量,用于食道组织样品的癌化识别分析是完全可能的,其平均识别正确率可达85.71%。 相似文献