Determination of the composition of sialoliths composed of carbonate apatite and albumin using artificial neural networks

The determination of the components of the sialoliths is important both from the point of view of chances for a successful medical treatment of the patients and because the prevention of further re-occurrence of sialolithiasis depends upon the knowledge of the nature of the constituents of the concrements. Despite the fact that infrared spectroscopy is widely used for the determination of the composition of sialoliths, urinary calculi and bladder stones, we found no data for any chemometric method developed for such purposes. Here, a method is presented for quantitative determination of the content of salivary calculi composed of albumin and carbonate apatite (one of the most often found constituents in the analyzed calculi from the patients from Macedonia) using artificial neural networks (ANN). The results were checked on real samples using the standard addition method. The precision of the method was estimated using the relative standard deviation, which shows that it is suitable for routine analysis.     

分子三维投影法在苯酚类化合物构效关系研究中的应用

对苯酚类化合物进行三维投影得到了5个形状参数,将其与3个Am指数及8个量子化学参数相结合.由最佳变量子集回归法对变量进行了压缩与选择,运用多元回归分析和人工神经网络法分别构造了预测数学模型,得到了满意的结果.     

Determination of the composition of human urinary calculi composed of whewellite, weddellite and carbonate apatite using artificial neural networks

More than half of the analyzed calculi from patients from Macedonia are composed of whewellite, weddellite and carbonate apatite (as single components or in binary or ternary mixtures). In order to develop a simple and satisfactorily reliable method for quantitative analysis of urinary calculi, the possibility was explored to employ artificial neural networks (ANNs) as a tool for such a purpose. By changing the number of input and hidden neurons, a search was made for the three-layered feed-forward ANN which would give the best performance. The root-mean-square errors (RMSE) for the test samples are: 0.035 for whewellite, 0.064 for weddellite and 0.078 for carbonate apatite. The accuracy of the method was checked using standard-addition method on real samples. The discrepancies between calculated and predicted mass fraction of constituents were in the range acceptable for use of the proposed method in clinical laboratories.     

原子三角法在定量结构/活性相关分析中的应用

分子相似度;回归分析;人工神经网络;原子三角法在定量结构/活性相关分析中的应用     

Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of electrophoretic mobility of a series of sulfonamides in capillary zone electrophoresis. The cross-validation method was used to evaluate the prediction ability of the generated networks. The mobility of sulfonamides as positively charged species at low pH and negatively charged species at high pH was investigated. The results obtained using neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression (MLR) technique. Comparison of the results shows the superiority of the neural network models over the regression models.     

A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods

A comparative study of analysis methods (traditional calibration method and artificial neural networks (ANN) prediction method) for laser induced breakdown spectroscopy (LIBS) data of different Al alloy samples was performed. In the calibration method, the intensity of the analyte lines obtained from different samples are plotted against their concentration to form calibration curves for different elements from which the concentrations of unknown elements were deduced by comparing its LIBS signal with the calibration curves. Using ANN, an artificial neural network model is trained with a set of input data of known composition samples. The trained neural network is then used to predict the elemental concentration from the test spectra. The present results reveal that artificial neural networks are capable of predicting values better than traditional method in most cases.     

The correlations of the enthalpy of vaporization and the surface tension of molecular liquids

Correlation relations based on Stefan's rule, which defined dependence between the enthalpy of vaporization, the surface tension, the molar volume and the molar mass of a substance, were obtained. For development of the correlation equations two computational procedures were used: a method of the least squares and a method of artificial neural networks. The method of artificial neural networks was shown to give somewhat better results than the linear least-squares procedure. The average deviation of the calculated values from the experimental ones did not exceed 6% for training set of substances and 10% for control set (the method of the least squares). For the method of artificial neural networks it is 3% and 8%, respectively.     

用傅立叶红外光谱测定泌尿系结石中的草酸钙

对泌尿系结石的组成进行结构分析,为分析结石病的产生原因及预防复发提供参考,在临床诊断和治疗上具有重要意义.傅立叶红外光谱是研究泌尿系结石构造的一种较理想的分析手段.用红外光谱仪定量分析泌尿结石中草酸钙采用的内标法、吸光度比值法和一阶导数法进行了比较,建立的分析方法可以简便对草酸钙混合结石中一水草酸钙(COM)和二水草酸钙(COD)进行定量测定,分析结果可初步满足临床诊断的要求.     

盐湖水化学类型的人工神经网络判别方法

研究了作为典型径向基函数网络之一的概率神经网络在盐湖水化学类型分类预测中的应用，验证了该方法的可靠性，得到了满意的分类预测结果。实验结果和网络结构分析表明，概率神经网络方法比熟知的反向传播算法(BP)网络要好。概率神经网络的研究应用为化学模式识别提供了一个新工具。     

Qsar Models for Predicting the Acute Toxicity of Selected Organic Chemicals with Diverse Structures to Aquatic Non-Vertebrates and Humans

Abstract

The linear and non-linear relationships of acute toxicity (as determined on five aquatic non-vertebrates and humans) to molecular structure have been investigated on 38 structurally-diverse chemicals. The compounds selected are the organic chemicals from the 50 priority chemicals prescribed by the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) programme. The models used for the evaluations are the best combination of physico-chemical properties that could be obtained so far for each organism, using the partial least squares projection to latent structures (PLS) regression method and backpropagated neural networks (BPN). Non-linear models, whether derived from PLS regression or backpropagated neural networks, appear to be better than linear models for describing the relationship between acute toxicity and molecular structure. BPN models, in turn, outperform non-linear models obtained from PLS regression. The predictive power of BPN models for the crustacean test species are better than the model for humans (based on human lethal concentration). The physico-chemical properties found to be important to predict both human acute toxicity and the toxicity to aquatic non-vertebrates are the n?octanol water partition coefficient (Pow) and heat of formation (HF). Aside from the two former properties, the contribution of parameters that reflect size and electronic properties of the molecule to the model is also high, but the type of physico-chemical properties differs from one model to another. In all of the best BPN models, some of the principal component analysis (PCA) scores of the ¹³C-NMR spectrum, with electron withdrawing/accepting capacity (LUMO, HOMO and IP) are molecular size/volume (VDW or MS1) parameters are relevant. The chemical deviating from the QSAR models include non-pesticides as well as some of the pesticides tested. The latter type of chemical fits in a number of the QSAR models. Outliers for one species may be different from those of other test organisms.     

广义二面角在定量结构/活性相关分析中的应用

化合物的结构与其活性具有相关性 .建立相关性需要由结构提取特征 ,并运用这些特征作为自变量去构造数学模型 ,以便预测未知的化合物及其活性 .目前 ,在诸多 3D- QSAR方法中 ,应用最为广泛的是 Co MFA( Comparasive Molecular Field Analysis)方法 [1～ 3] ,但新近的研究发现 ,Co MFA方法对于某类化合物 (如苯胺类化合物 )适应性较差 ,但分子在三维空间投影面积及量化参数的引入可大大改善所得结果[4 ] .本文从另一角度 (即广义二面角 )研究了分子在三维空间的形状 ,得到了满意的结果 .1　实验部分本文选用 34个 1 - [( 2 - Hydroxy…     

Evaluation of feed-forward back propagation and radial basis function neural networks in simultaneous kinetic spectrophotometric determination of nitroaniline isomers

Mixtures of 2-, 3-, and 4-nitoroanilines, are simultaneously analyzed with spectrophotometry, based on their different kinetic properties. These nitroanilines react differentially with 1,2-naphtoquinone-4-sulphonate (NQS) at pH 7 in micellar medium to produce colored product. The differential kinetic spectra were monitored and recorded at 500 nm, and the data obtained from the experiments were processed by chemometric approaches, such as back-propagation neural networks (BPNNs), radial basis function neural networks (RBFNNs), and partial least squares (PLS). Experimental conditions were optimized and training the network was performed using principal components (PCs) of the original data. A set of synthetic mixtures of nitroanilines was evaluated and the results obtained by the application of these chemometric approaches were discussed and compared. The analytical performance of the models was characterized by relative standard errors. It was found that the artificial neural networks model affords relatively better results than PLS. The proposed method was applied to the determination of considered nitroanilines in water samples.     

应用量化参数和拓扑指数研究吡喃酮类化合物结构与活性的相关性

QSAR;量子化学参数;应用量化参数和拓扑指数研究吡喃酮类化合物结构与活性的相关性     

Characterization of Galician (N.W. Spain) quality brand potatoes: a comparison study of several pattern recognition techniques

Authenticity is an important food quality criterion and rapid methods to guarantee it are widely demanded by food producers, processors, consumers and regulatory bodies. The objective of this work was to develop a classification system in order to confirm the authenticity of Galician potatoes with a Certified Brand of Origin and Quality (CBOQ) 'Denominación Específica: Patata de Galicia' and to differentiate them from other potatoes that did not have this CBOQ. Ten selected metals were determined by atomic spectroscopy in 102 potato samples which were divided into two categories: CBOQ and non-CBOQ potatoes. Multivariate chemometric techniques, such as cluster analysis and principal component analysis, were applied to perform a preliminary study of the data structure. Four supervised pattern recognition procedures [including linear discriminant analysis (LDA), K-nearest neighbours (KNN), soft independent modelling of class analogy (SIMCA) and multilayer feed-forward neural networks (MLF-ANN)] were used to classify samples into the two categories considered on the basis of the chemical data. Results for LDA, KNN and MLF-ANN are acceptable for the non-CBOQ class, whereas SIMCA showed better recognition and prediction abilities for the CBOQ class. A more sophisticated neural network approach performed by the combination of the self-organizing with adaptive neighbourhood network (SOAN) and MLF network was employed to optimize the classification. Using this combined method, excellent performance in terms of classification and prediction abilities was obtained for the two categories with a success rate ranging from 98 to 100%. The metal profiles provided sufficient information to enable classification rules to be developed for identifying potatoes according to their origin brand based on SOAN-MLF neural networks.     

人工神经网络用于近红外光谱测定柴油闪点

采用主成分－人工神经网络对不同留程柴油的近红外光谱进行校正,预测其闪点。采用监控集控制网络训练过程,以避免过训练。探讨了人工神经网络（ＡＮＮ）、直接线性连接人工神经网络（ＬＡＮＮ）的校正效果,并与局部权重回归（ＬＷＲ）、主成分回归（ＰＣＲ）及偏最小二乘（ＰＬＳ）等校正方法进行了比较,认为人工神经及直接线性关联的较好手段。     

An accurate quantitative analysis of polymorphs based on artificial neural networks

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.     

人工神经网络在纸浆卡伯值光学定量分析中的应用

卡伯值 (硬度 )是纸浆的重要质量指标 ,是制浆过程控制的关键参数 .目前的测量方法包括化学分析法和光学分析法两大类型 ,国内大多数的制浆造纸厂采用离线的传统化学分析法来测定纸浆的卡伯值 ,需要比较长的时间 .而光学分析法因具有实时性好、精度和可靠性高等优点 ,已逐步用于卡伯值的在线测量和控制 .研究 [1] 发现 ,在 460～ 580 nm的可见光谱范围内 ,蒸煮液吸光度的变化可以表征纸浆中木素含量的变化 .本文将可见分光光谱技术应用于蒸煮液中木素含量的在线测量 ,根据蒸煮液在所选波段的吸光度来预测纸浆的卡伯值 ,建立纸浆卡伯值与蒸煮…