学习向量量化神经网络用于胃癌组织样品分类识别的研究

将lvq神经网络(Learn ing Vector Quantization Neural Networks)用于胃癌组织样品的分类识别,根据胃癌组织及相应正常组织的FTIR光谱的主要特征吸收峰值(包括vas(CH3)、vs(CH2)、δ(CH2)、v(C-O)、vs(PO2-)、vas(PO2-)和vs(核酸,细胞蛋白及膜脂))全部或部分作为网络输入向量,对未知的胃组织样品进行分类识别,结果显示:i)以上述全部七个谱峰为输入向量时,网络经训练学习后,其平均识别正确率最高(达89.3%),表明该网络对胃癌组织样品的分类识别是满意的,完全可作为临床医学的辅助诊断手段;ii)总体上,当作为输入向量的FTIR特征谱峰越多时,则网络的平均分类识别正确率越高;iii)作为输入的FTIR特征谱峰不同时,则网络的平均分类识别正确率也不同。     

概率神经网络和FTIR光谱用于食道癌的辅助分析

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%。     

Resolution of overlapping signals in spectrometry using a wavelet packet transform and an Elman recurrent neural network

A novel method named a wavelet packet transform based Elman recurrent neural network (WPTERNN) was proposed for the simultaneous UV–visible spectrometric determination of Cu(II), Cd(II) and Zn(II). This method combined wavelet packet denoising with an Elman recurrent neural network. A wavelet packet transform was applied to perform data compression, to extract relevant information, and to eliminate noise and collinearity. An Elman recurrent network was applied for nonlinear multivariate calibration. In this case, using trials, the kind of wavelet function, the decomposition level, and the number of hidden nodes for the WPTERNN method were selected as Daubechies 14, 3, and 8, respectively. A program (PWPTERNN) was designed that could perform the simultaneous determination of Cu(II), Cd(II) and Zn(II). The relative standard errors of prediction (RSEP) obtained for all components using WPTERNN, a Elman recurrent neural network (ERNN), partial least squares (PLS), principal component regression (PCR), Fourier transform based PCR (FTPCR), and multivariate linear regression (MLR) were compared. Experimental results demonstrated that the WPTERRN method was successful even where there was severe overlap of spectra. The results obtained from an additional test case also demonstrated that the WPTERNN method performed very well. Figure The part of WP coefficients obtained by wavelet packet transforms     

Evaluation of chemometric techniques and artificial neural networks for cancer screening using Cu, Fe, Se and Zn concentrations in blood serum

It is known that variations in the concentrations of certain trace elements in bodily fluids may be an indication of an alteration of the organism's normal functioning. Multivariate analysis of such data, and its comparison against proper reference values, can thus be employed as possible screening tests. Such analysis has usually been conducted by means of chemometric techniques and, to a lower extent, backpropagation artificial neural networks. Despite the excellent classification capacities of the latter, its development can be time-consuming and computer-intensive. Probabilistic artificial neural networks represent another attractive, yet scarcely evaluated classification technique which could be employed for the same purpose. The present work was aimed at comparing the performance of two chemometric techniques (principal component analysis and logistic regression) and two artificial neural networks (a backpropagation and a probabilistic neural network) as screening tools for cancer. The concentrations of copper, iron, selenium and zinc, which are known to play an important role in body chemistry, were used as indicators of physical status. Such elements were determined by total reflection X-ray fluorescence spectrometry in a sample of blood serum taken from individuals who had been given a positive (N = 27) or a negative (N = 32) diagnostic on various forms of cancer. The principal components analysis served two purposes: (i) initial screening of the data; and, (ii) reducing the dimension of the data space to be input to the networks. The logistic regression, as well as both artificial neural networks showed an outstanding performance in terms of distinguishing healthy (specificity: 90-100%) or unhealthy individuals (sensitivity: 100%). The probabilistic neural network offered additional advantages when compared to the more popular backpropagation counterpart. Effectively, the former is easier and faster to develop, for a smaller number of variables has to be optimized, and there are no risk of overtraining.     

改进的广义回归神经网络用于基于气相色谱法的原油密度定量分析

在油气勘探开发领域,快速识别储集层原油性质对于工程技术人员有非常重要的指导意义.地球化学录井技术是用于判断储集层原油性质的常规手段,能为储集层综合评价提供专业认识.该文研究了地化录井中的岩石热解分析和气相色谱分析的原理,提出了一种利用色谱谱图对原油密度进行定量分析的新方法,再结合原油性质密度划分标准,可快速判断储集层原...     

QSPR study for the prediction of half-wave potentials of benzoxazines by heuristic method and radial basis function neural network

The half-wave potential (E_1/2) is an important electrochemical property of organic compounds. In this work, a quantitative structure-property relationship (QSPR) analysis has been conducted on the half-wave reduction potential (E_1/2) of 40 substituted benzoxazines by means of both a heuristic method (HM) and a non-linear radial basis function neural network (RBFNN) modeling method. The statistical parameters provided by the HM model (R² =0.946; F=152.576; RMSCV=0.0141) and the RBFNN model (R²=0.982; F=1034.171 and RMS =0.0209) indicated satisfactory stability and predictive ability. The obtained models showed that benzoxazines with larger Min valency of a S atom (MVSA), lower Relative number of H atom (RNHA) and Min n-n repulsion for a C-H bond (MnnRCHB) and Minimal Electrophilic Reactivity Index for a C atom (MERICA) can be more easily reduced. This QSPR approach can contribute to a better understanding of structural factors of the organic compounds that contribute to the E_1/2, and can be useful in predicting the E_1/2 of other compounds.      

Application of artificial neural network for comparison and modeling of the ultrasonic and stirrer assisted removal of anionic dye using activated carbon supported with nanostructure material

In this study, a green approach has been described for the synthesis of copper sulfide nanoparticles loaded on activated carbon (CuS‐NP‐AC) and usability of it for the removal of sunset yellow (SY) dye by ultrasound‐assisted and stirrer has been compared. In addition, the artificial neural network (ANN) model has been employed for a forecasting removal percentage of SY dye using the results obtained. This material was characterized using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The impact of variables, including initial dye concentration (mg/L), pH, adsorbent dosage (g), sonication time (min) and temperature (°C) on SY removal was studied. Fitting the experimental equilibrium data of different isotherm models such as Langmuir, Freundlich, Temkin and Dubinin–Radushkevich models display the suitability and applicability of the Langmuir model. Analysis of experimental adsorption data of different kinetic models including pseudo‐first and second order, Elovich and intraparticle diffusion models indicate the applicability of the second‐order equation model. The adsorbent (0.005 g) is applicable for successful removal of SY dye (> 98%) in short time (9 min) under ultrasound condition. A three layer ANN models with 8 and 6 neurons at hidden layer was selected as optimal models using stirrer and ultrasonic, respectively. These models displayed a good agreement between forecasted data and experimental data with the determination coefficient (R²) of 0.9948 and 0.9907 and mean squared error (MSE) of 0.0001 and 0.0002 for training set using stirrer and ultrasonic, respectively.     

Application of neural networks with novel independent component analysis methodologies for the simultaneous determination of cadmium,copper, and lead using an ISE array

The paper introduces a novel chemometric strategy based on independent component analysis (ICA) coupled with a back‐propagation neural network. In this approach, one of the most popular ICA methods, the fast fixed‐point algorithm for ICA (fastICA), was implemented by the genetic algorithm (geneticICA) to avoid the local maxima problem commonly observed with fastICA. As a case study, an ion‐selective electrode (ISE) array, consisting of three working electrodes and one reference electrode, was used for the simultaneous determination of three heavy metals (cadmium, copper, and lead) in aqueous solutions, which are normally prone to severe interferences. The robustness and appropriateness of the approach were assessed using the average mean of relative error (MRE) of triplicated external validation. After configuration and optimization, the average MRE for Cu was <5%. For the determination of Cd and Pb, whose ISEs normally cannot tolerate Cu ions even at the microgram per liter levels, the MREs were 8%. This article demonstrated that this approach can be applied to the detection of heavy metal contamination in industrial wastewater with prediction accuracies comparable with other popular quantitative chemometric neural network methods. Copyright © 2014 John Wiley & Sons, Ltd.     

2D bitmapping approach for identification and quantitation of common base flavor adulterants using surface acoustic wave arrays and artificial neural network data analysis

Arrays of polymer-coated surface acoustic wave microsensors are used in conjunction with a variety of signal-processing algorithms known as artificial neural networks (ANN). This format of data analysis has the capability to characterize complex mixtures of volatile and semi-volatile organic compounds commonly found in base flavors. The approach described, which minimizes the number of training sets while retaining the robustness of an ANN, utilizes a 2D bitmap matrix. The matrix is obtained by converting the time domain kinetics of sensor response into a bitmap. The high data throughput of this approach enables quantitation on the order of ppm of common base flavor adulterants.     

大鼠胰腺及癌组织红外光谱连续小波特征提取及径向基人工神经网络识别

采用水平衰减全反射(HATR)傅里叶变换红外光谱法(FTIR)测定了SD大鼠胰腺正常组织与非正常组织的谱图,提出了一种新的基于FTIR的连续小波特征提取与径向基人工神经网络分类方法以提高FTIR对早期SD大鼠胰腺癌的诊断准确率。利用连续小波多分辨率分析法提取FTIR特征量,对于提取的特征量采用径向基函数神经网络进行模式分类。对SD大鼠的胰腺正常组织、早期癌组织及进展期癌组织的FTIR,利用连续小波多分辨率分析法提取9个特征量,进行RBF神经网络分类判断。当目标误差为0.01,径向基函数的分布常数为5时,网络达到最优化,总的正确识别率为96.67%。并对影响分类结果的网络参数、目标误差和分布常数对分类样品的影响做了讨论。实验结果表明:此方法对早期胰腺癌具有较高的诊断率。     

Extending the dynamic range of the determination of copper by adsorption differential pulse stripping method using a principal component artificial neural network

A principal component artificial neural network (PC-ANN) has been applied for the analysis of the voltammogram data of Cu(II) and Cu(II)–PAN complex for extending the dynamic range of the determination of Cu(II) (5–550 μg/l). A three layer back-propagation network (6:5:1) was used with learning rate (r=0.09) and momentum (m=0.9), with sigmoidal transfer function in the hidden layer and one bias node in the input layer. Overall, the application of PC-ANN enables the extension of the dynamic range of the determination of Cu(II) from its narrow linear range (5–50 μg/l) to the high dynamic range (5–550 μg/l).     

Direct analysis of blood serum by total reflection X-ray fluorescence spectrometry and application of an artificial neural network approach for cancer diagnosis

Iron, copper, zinc and selenium were determined directly in serum samples from healthy individuals (n=33) and cancer patients (n=27) by total reflection X-ray fluorescence spectrometry using the Compton peak as internal standard [L.M. Marcó P. et al., Spectrochim. Acta Part B 54 (1999) 1469–1480]. The standardized concentrations of these elements were used as input data for two-layer artificial neural networks trained with the generalized delta rule in order to classify such individuals according to their health status. Various artificial neural networks, comprising a linear function in the input layer, a hyperbolic tangent function in the hidden layer and a sigmoid function in the output layer, were evaluated for such a purpose. Of the networks studied, the (4:4:1) gave the highest estimation (98%) and prediction rates (94%). The latter demonstrates the potential of the total reflection X-ray fluorescence spectrometry/artificial neural network approach in clinical chemistry.     

Separation of organic acid compounds from biological samples by zinc oxide nanoparticles–chitosan using genetic algorithm based on response surface methodology and artificial neural network

In this study, zinc oxide nanoparticles–chitosan based on solid phase extraction and high performance liquid chromatography was developed for the separation of organic compounds including citric, tartaric and oxalic acids from biological samples. For simulation and optimization of this method, the hybrids of genetic algorithm with response surface methodology (RSM) and artificial neural network (ANN) have been used. The predictive capability and generalization of both predictive models (RSM and ANN) were compared by unseen data. The results have shown the superiority of ANN compared with RSM. At the optimum conditions, the limits of detections of 2.2–2.9 µg L⁻¹ were obtained for the analytes. The developed procedure was then applied to the extraction and determination of organic acid compounds from biological samples. Copyright © 2014 John Wiley & Sons, Ltd.     

Synthesis of poly(vinylidene fluoride) (PVdF)/silica hybrids having interpenetrating polymer network structure by using crystallization between PVdF chains

High transparent and homogeneous poly(vinylidene fluoride) (PVdF)/silica hybrids were obtained by using an in‐situ interpenetrating polymer network (IPN) method. The simultaneous formation of PVdF gel resulting from the physical cross‐linking and silica gel from sol–gel process prevented the aggregation of PVdF in silica gel matrix. To form the physical cross‐linking between PVdF chains, the cosolvent system of dimethylformaide (DMF) and γ‐butyrolactone was used. The obtained PVdF/silica hybrids had an entangled combination of physical PVdF gel and silica gel, which was called a “complete‐ IPN” structure. The physical cross‐linking between PVdF chains in silica gel matrix was confirmed by differential scanning calorimetry (DSC) measurements. The miscibility between PVdF and silica phase was examined by scanning electron microscopy (SEM) and tapping mode atomic force microscopy (TM‐AFM) measurements. © 2005 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 43: 3543–3550, 2005