应用理论α系数校正X射线荧光光谱法分析锰矿时的基体效应

在锰矿的X-射线荧光光谱分析中应用理论a系数法对其基体效应作了校正,从而使锰矿中主要及次要组分(包括Mn、Fe、MgO、Al2>O3>、SiO2>、P、S、K2>O、CaO、TiO2>、NiO、Cu、Zn及BaO等14项)的测定结果与所分析的标准物质的证书值相符.锰矿样品用四硼酸锂熔融,所得熔片用于X-射线荧光光谱分析.此外还发现,由直接灼烧锰矿试样所测得的灼烧损失值与由锰矿与四硼酸锂一起灼烧所测得的值不同且常小于后者.基于这一事实,提出了由灼烧锰矿与四硼酸锂混合物的条件下测定灼烧损失的方法,测得制备锰标准曲线的标准偏差为0.08%;测定一种矿样中锰量时其结果的相对标准偏差(n=11)为0.19%.     

理论α系数-X射线荧光光谱法分析水系沉积物中主次量元素

本文将NBSGSC程序移植到长城0520计算机中,求得一组COLA模式的a系数并输入到3080E3型XRF光谱仪计算机中,作联机分析,只需10个标样即可得到满意的结果,可与用50个标样的经验系数法相媲美。     

应用手持式XRF分析仪快速测定白铜中的Cu、Ni、Co含量

采用国产手持式XRF分析仪快速分析了白铜合金主元素的含量,探讨了白铜合金基体效应对测量结果的影响,采用经验系数法中多元回归分析建立数学模型,较好地校正基体效应,分析白铜合金中铜、镍、钴的平均相对误差分别为0.39%、1.83%、1.59%。     

多元非线性荧光校正的人工神经网络方法

由实验测定得知罗丹明Ｂ，丁基罗丹明Ｂ，曙红Ｂ组成的三组分混和荧光分析体系存在严重的荧光熄灭现象，其混和物的荧光光谱呈非线性，ＰＬＳ难以校正。本文成功地将ＢＰ－ＡＮＮ应用于此多元非线性荧光校正问题，完成了三组分的同时测定，得到了满意的结果。     

神经群结构,算法与X射线荧光光谱分析研究

研究了一种多组分型神经群网络结构，根据多元体系各变量间的内在规律，可在神经网络中由相互间具有紧密联系的一些神经元的集合形成群结构，采用这种全连接方式的神经群网络结构，减少了连接权重，剔除了噪音，从而增强了模型稳定性，提高了Ｘ射线荧光光谱预测准确度，显著增加了神经网络的外推预测能力，降低了训练模型所需的标样数。     

田口方法在X射线荧光分析时曲线校正中的应用

根据田口玄一博士在测量工程学中提出的信噪比概念,计算了X荧光分析中某一标准曲线极限信噪比。当日常测试中系统的信噪比大于极限值时,不需要对曲线进行校正;当日常测试中系统的信噪比小于极限值时,应对曲线进行校正．以保证该测量系统工作的稳定性。     

非对称加权惩罚最小二乘算法校正X射线荧光法定量精铜痕量银元素模型

针对精铜中痕量银元素定量时特征X射线存在背景干扰的问题,采用非对称加权惩罚最小二乘算法(arPLS)对X射线荧光光谱进行基线校正,建立精铜中痕量银元素定量检测模型。通过改变算法平滑参数(λ)和收敛条件(ratio),研究痕量元素X射线光谱基线随参数变化规律,对60组光谱数据采用调整参数并图像监控的方式估计基线。所建立的定量模型不确定度系数R²=0.9862,均方根误差(RMSE)为0.0040 wt%。与不经背景校正算法相比,定量模型R²提高了0.0211,RMSE降低了0.0023 wt%,预测值和参考值平均相对误差缩减了2倍,预测不确定度由0.0028 wt%降低为0.0025 wt%。所建立的定量模型可应用于冶金在线检测、金属回收、合金强化等领域。     

熔融制样-X射线荧光光谱法测定海洋沉积物中氯等元素

应用X射线荧光光谱法测定了海洋沉积物中12种元素（即硫、铝、铁、钙、钾、磷、钛、锰、氯、硅、钠及镁）。样品预先在120℃烘8h后,称取0.500 0g与5.000g混合熔剂（四硼酸锂67g与偏硼酸锂33g混合）置于铂-金坩埚中混匀后,先在600℃预氧化200s,使还原性物质充分氧化,随即升温至1 000℃熔融9min。...     

基于同步辐射X射线荧光光谱与一维卷积神经网络的癌症筛查方法

癌症是全球范围内引起高发病率与高死亡率的疾病之一。现有癌症检测方法耗时、昂贵、专业人员依赖性强,开发一种无损、快速筛查方法非常重要。在前期工作基础上,发展了基于同步辐射X射线荧光光谱技术(SRXRF)与深度学习技术结合的一种非靶标金属组学方法筛查癌症患者。首先,分析控制组与癌症组共269份血清样本的SRXRF谱线,得到Ca、Mn、Zn、Ge、Br在两类人群中具有代表性差异,可以作为癌症筛查的标志物;其次,对于平均光谱进行归一化(Normalization)、迭代自适应加权惩罚最小二乘法(airPLS)、Savitzky-Golay平滑(SG)、标准正态变换(SNV)的预处理,并建立偏最小二乘判别分析(PLSDA)、K近邻法(KNN)、软独立建模分类法(SIMCA)的化学计量学模型,三种模型对癌症筛查的最优准确率分别为89.89%、93.26%、90.95%;最后,基于像素级光谱,搭建三种一维卷积神经网络(1DCNN)模型,三种模型准确率分别为93.56%、95.24%、93.27%,相对于化学计量学模型均有所提高,增加卷积层的数量有助于数据特征提取,模型准确率提高了1.68%。将三种模...     

Comparison of Artificial Neural Networks with Partial Least Squares Regression for Simultaneous Determinations by ICP-AES

Simultaneous determination of several elements （U, Ta, Mn, Zr and W） with inductively coupled plasma atomic emission spectrometry （ICP-AES） in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network （ANN） and partial least squares regression （PLS） for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line （263.553 nm） with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 （input, hidden and output neurons） network was used with linear transfer function after both hidden and output layers. The PI,S model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions （REP） in test set were 3.75% and 3.56% for ANN and PLS respectively.     

Resolution of Differential Pulse Voltammetric Peaks Using Genetic Algorithm Based Variable Selection‐Partial Least Squares and Principal Component‐Artificial Neural Networks

Differential Pulse Voltammetry has been used for the simultaneous determination of cysteine, tyrosine and trptophan on the unmodified glassy carbon electrode. In the analysis of these analytes in the same samples, the main difficulty is the high degree of overlapping of voltammograms. The relationships between the currents and the concentrations are complex and highly nonlinear. The predictive ability of principal component regression (PCR), partial least squares regression (PLS), genetic algorithm‐partial least squares regression (GA‐PLS) and principal component‐artificial neural networks (PC‐ANNs) were examined for simultaneous determination of three amino acids. For a regression model, everything that could not help in constructing the model may be considered as noise without further specification. PC‐ANN and GA‐PLS use significant data and show superiority over other applied multivariate methods. The proposed method was also applied satisfactorily to determination of analytes in some synthetic samples.     

近红外光谱-径向基神经网络在异烟肼片无损定量分析中的应用

应用异烟肼片粉末的近红外漫反射光谱数据分别结合偏最小二乘法(PLS)和径向基神经网络(RBFNN)建立定量分析模型,并用所建模型对预测集样品进行了预测,结果表明:应用RBFNN所建立的定量分析模型优于PLS模型,相关系数(r)值由0.99593提高到0.99734,交互验证均方根误差(RMSECV)值由0.00523下降到0.00423,预测均方根误差(RMSEP)值由0.00614下降到0.00501。     

基于旋转曲面变换PSO算法的神经网络用于胺类有机物毒性分类

神经网络模型能有效地模拟非线性的输入输出关系。本研究应用三层前馈网络对51种胺类有机物进行了结构-毒性关系的分类研究。常规的神经网络权值训练算法,例如误差反传算法,存在着收敛速度慢,容易陷入局部极值点等问题。因此提出旋转曲面变换粒子群优化算法,将被优化函数的局部极小点变换为全局最大点,同时不改变比局部极小点的值更小的区域的函数形状。此方法和粒子群优化相结合,能使待优化函数跳出局部极值点,提高训练神经网络权值的效率。实验结果显示,基于旋转曲面变换粒子群优化算法的神经网络,权值训练过程收敛速度较快,且自检误差和预报误差都较小,是一种有效的胺类有机物毒性分类方法。     

统计方法校正O3LYP方法计算的生成热

由于引入各种内在近似, 密度泛函理论存在固有误差. 本文采用O3LYP/6-311+G(3df, 2p)//O3LYP/6-31G(d)计算了220个中小型有机分子的生成热(Δ_fH_calc^Θ), 随后应用神经网络(ANN)和多元线性回归(MLR)方法对Δ_fH_calc^Θ进行校正. 采用计算得到的生成热、零点能、分子中原子总数、氢原子个数、双中心成键电子数、双中心反键电子数、单中心价层孤对电子数、单中心内层电子数作为ANN和MLR的描述符. 以180个分子作为训练集构造ANN或MLR模型, 并对40 个独立测试集分子的Δ_fH_calc^Θ进行了预测. 结果表明: 经过ANN和MLR校正后,训练集分子生成热的理论计算值和实验值间的均方根偏差(RMSD)从24.7 kJ·mol^-1分别降低到11.8、13.0 kJ·mol^-1; 独立测试集分子的RMSD从21.3 kJ·mol^-1分别降低到10.4、12.1 kJ·mol^-1. 因此ANN模型的拟合和预测能力要明显优于MLR模型.     

Simultaneous Spectrophotometric Determination of Iron and Cobalt in Micellar Medium by Using a Principal Component Artificial Neural Network and Multivariate Calibration

A simple and reliable method for simultaneous spectrophotometric determination of iron(II) and cobalt(II) has been established. The method is based on complex formation with 1‐(2‐pyridylazo)‐2‐naphtol (PAN) in a micellar medium. Despite a spectral overlap, Fe²⁺ and Co²⁺ have been simultaneously determined with chemometric approaches involving principal component artificial neural network (PC‐ANN), principal component regression (PCR) and partial least squares (PLS). Various synthetic mixtures of iron and cobalt were assessed and the results obtained by the applications of these chemometric approaches were evaluated and compared. It was found that the PC‐ANN method afforded relatively better precision than that of PCR or PLS. The proposed method permits detection limits of 0.05 and 0.07 ng mL^?1 for Co and Fe, respectively. The influences of pH, ligand amount, solvent percentage and time on the absorbance were also investigated. The proposed method was also applied satisfactorily for the determination of Fe(II) and Co(II) in real and synthetic samples.     

Application of Near Infrared Diffuse Reflectance Spectroscopy with Radial Basis Function Neural Network to Determination of Rifampincin Isoniazid and Pyrazinamide Tablets

Partial least squares(PLS),back-propagation neural network(BPNN)and radial basis function neural network(RBFNN)were respectively used for estalishing quantative analysis models with near infrared(NIR)diffuse reflectance spectra for determining the contents of rifampincin(RMP),isoniazid(INH)and pyrazinamide(PZA)in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform(FFT)and standard normal variate(SNV)transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis(PCA)method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models' parameters were chosen by comparing the root mean square error of cross-validation(RMSECV)values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.     

Quantitative Near-infrared Spectroscopic Analysis of Trimethoprim by Artificial Neural Networks Combined with Modified Genetic Algorithm

A novel method for rapid,accurate and nondestructive determination of trimethoprim in complex matrix was presented.Near-infrared spectroscopy coupled with multivariate calibration（partial least-squares and artificial neural networks） was applied in the experiment.The variable selection process based on a modified genetic algorithm with fixed number of selected variables was proceeded,which can reduce the training time and enhance the predictive ability when coupled with artificial neural network model.