胆石症的多元判别分析

取胆石症患者组血浆（２６例）、胆汁（１６例）和非胆石症组血浆（２３例）、胆汁（１２例），用同位素源激发Ｘ射线多道能谱分析仪测定６种微量元素含量．并对两种样本的有关元素作多元判别分析．结果，患者组血浆中微量元素含量均高于对照组，其中锌有极显著差异（Ｐ＜０．０１）；患者组和对照组血浆、胆汁经多元判别分析的判别率为７０％．     

Detection of single-nucleotide polymorphisms coding for three ovine prion protein variants by primer extension assay and capillary electrophoresis

An alternative method is described for the determination of ovine prion protein allelic variants at codon 136, 154, and 171. The four mutations responsible for amino acid changes are typed simultaneously. The technique utilizes dideoxy chain termination reaction using fluorescently labeled dideoxy nucleotides. The single-base extended primers are resolved on a capillary electrophoresis instrument. Data obtained by our approach are presented according to genotype distribution in some breeds as a part of the validation procedure.     

判别分析用于烃类化合物分类及汽油样品的族组成分析

研究了判别分析用于烃类化合物分类的可行性，以198个烃类化合物在BD－1和BD－5固定相上同一柱温下的保留指数差及其在各柱上的温度系数为判别变量，成功地建立了烷烃、环烷烃、芳烃和烯烃的判别函数，以此对裂化汽油，重整生成汽油、石脑油和蒸馏常顶汽油进行了族组成分析，经气相色谱－质谱分析验证了结果的正确性，为汽油产品的族组成分析提供了一种新的方法。     

Optimization of a mathematical topological pattern for the prediction of antihistaminic activity

Molecular topology was used to develop a mathematical model capable of classifying compounds according to antihistaminic activity. The equations used for this purpose were derived using multilinear regression and linear discriminant analysis. The topological pattern of activity obtained allows the reliable prediction of antihistaminic activity in drugs frequently used for other therapeutic purposes. Based on the results, the proposed pattern is seemingly only valid for drugs that interact with histamine through competitive inhibition with H1 receptors.     

气相色谱-质谱技术结合化学计量学对5种动物油进行判别分析

测定了5种不同种类动物油(鸡油、牛油、鸭油、羊油和猪油)的脂肪酸组成和含量,探讨了利用动物油脂肪酸的指标对不同种类的动物油进行分类和判别的可能性。采用气相色谱-质谱联用技术(GC-MS)对5种动物油脂肪酸的组成和含量进行测定,利用SPSS 19.0统计软件进行主成分分析、聚类分析和判别分析。对5种动物油的脂肪酸进行统计、分析和对比,得出动物油的主要组成为C16∶0,C18∶0,C18∶1c-9,C18∶2c-9,12和C14∶0,这5种脂肪酸在鸡油、牛油、鸭油、羊油和猪油中的总含量分别为92.1%,93.5%,93.5%,90.6%和95.6%。5种脂肪酸的不饱和度均小于1。主成分分析降维得到3个主成分,利用主成分分析数据,依次进行聚类分析和判别分析,建立了3个动物油典则判别函数,相关系数均大于0.995,对于鸡油、牛油、鸭油、羊油和猪油的初始分类正确率和交叉验证正确率均为100%。     

基于香气成分气相色谱-质谱指纹图谱的判别分析和相似度评价用于绿茶等级差异研究

该文以研究指纹图谱在绿茶等级判别方面应用的可行性为目的。以信阳毛尖茶为研究对象，以茶汤香气成分为研究目标，采用顶空固相微萃取与气相色谱-质谱联用技术，筛选其香气特征成分，建立不同等级信阳毛尖茶香气成分GC-MS指纹图谱。发现运用判别分析法可以对2个系列7个等级41个信阳毛尖茶样进行很好的等级区分；通过不同等级色谱指纹图谱的相似度计算发现，相似度与茶样等级之间具有较好的相关性，尤其是对于品质较好的茶样，这种相关性更好。表明运用判别分析法和相似度计算方法能够反映绿茶等级之间的差异性，且基于相似度评价的方法具有客观量化的特征。     

Discrimination of Human and Nonhuman Blood by Raman Spectroscopy and Partial Least Squares Discriminant Analysis

A novel method is reported to discriminate human and animal blood by Raman spectroscopy without complex sample preparation. A partial least squares discriminant analysis model was constructed from a calibration set of Raman spectra from three species of animal blood using 785-nm laser excitation. The discrimination between human and nonhuman blood was calculated by three sigma. Various performance measures, including a blind test and external validation, confirmed the discriminatory performance of the chemometric model. The model provided 100% accuracy in its differentiation between human and nonhuman blood. These results demonstrate that Raman spectroscopy is a promising tool for the discrimination of animal and human blood.     

Shrunken centroids regularized discriminant analysis as a promising strategy for metabolomics data exploration

Metabolomics datasets generated by modern analytical instruments tend to be increasingly complex. In this study, a recent method named shrunken centroids regularized discriminant analysis (SCRDA) has been introduced and applied in the exploration of metabolomics dataset. It is a supervised method for variable selection, discriminant analysis and biomarker screening. By regularizing the estimate of the within‐class covariance matrix, SCRDA can deal with the singularity issue of linear discriminant analysis. Then a shrinkage estimator is applied to perform variable selection. The method presented is illustrated through the simulated datasets and three complex metabolomics datasets. Commonly used orthogonal partial least squares discriminant analysis and two other similar statistical methods, penalized linear discriminant analysis and nearest shrunken centroids, are used for comparisons. The results illustrate that SCRDA has some desirable abilities in variable selection, classification and prediction. Moreover, the biomarkers identified by SCRDA are further demonstrated to be in accordance with the biochemical research. It has been proved that SCRDA can be applied as a promising strategy in metabolomics. Copyright © 2014 John Wiley & Sons, Ltd.     

压电晶体传感器阵列判别分析小分子脂肪醇蒸气

选用８个不同材料涂层压电石英晶体组成传感器阵列，所获频移数据用逐步判别（ＳＤＡ）法解析，对小分子脂肪醇类与非醇类溶剂蒸气用二类判别法分析，找到由少于８个传感器组成阵列可达最佳判别能力，训练集回测正确率平均为８４．８％，预测集识别正确率平均为８７．３％。此外，该阵列用于识别酒、饮料，结果较佳。     

核酸适配子共轭金纳米粒子探针成像朊蛋白细胞内在化途径

将巯基修饰的核酸适配子(aptmer)偶联到金纳米粒子(AuNPs)表面,制备出朊蛋白特异性的Apt-AuNPs纳米光学探针,并成功应用到细胞表面朊蛋白的光散射成像和电子透射显微成像分析.通过对Apt-AuNPs探针进入细胞的途径及其在细胞内命运的进一步研究表明,窖蛋白介导的内吞作用可能是其进入细胞的一个重要途径.Apt-AuNPs纳米探针制备简单、成本低廉,可能被广泛应用于生物医学成像领域.     

Characterization of Biodiesel by Infrared Spectroscopy with Partial Least Square Discriminant Analysis

This paper reports a method to simultaneously classify 7% biodiesel and 93% diesel based on vegetable oil as the source of the biodiesel. Mafurra, moringa, and cotton biodiesel blends were characterized by infrared spectroscopy with partial least square discriminant analysis. The efficiency of model was evaluated based on the sensitivity and specificity. The model showed excellent results as all samples were correctly classified based on the raw material. Hence, the sensitivity and specificity parameters showed values of 1, which means 100% correct characterization of the samples in the calibration and prediction sets. Therefore, infrared spectroscopy with partial least square discriminant analysis is suitable for the characterization of biodiesel/diesel blends.     

Using combinations of principal component scores from different spectral ranges in near-infrared region to improve discrimination for samples of complex composition

Principal component analysis (PCA) is widely used as an exploratory data analysis tool in the field of vibrational spectroscopy, particularly near-infrared (NIR) spectroscopy. PCA represents original spectral data containing large variables into a few feature-containing variables, or scores. Although multiple spectral ranges can be simultaneously used for PCA, only one series of scores generated by merging the selected spectral ranges is generally used for qualitative analysis. Alternatively, the combined use of an independent series of scores generated from separate spectral ranges has not been exploited.The aim of this study is to evaluate the use of PCA to discriminate between two geographical origins of sesame samples, when scores independently generated from separate spectral ranges are optimally combined. An accurate and rapid analytical method to determine the origin is essentially required for the correct value estimation and proper production distribution. Sesame is chosen in this study because it is difficult to visually discriminate the geographical origins and its composition is highly complex. For this purpose, we collected diffuse reflectance near-infrared (NIR) spectroscopic data from geographically diverse sesame samples over a period of eight years. The discrimination error obtained by applying linear discriminant analysis (LDA) was improved when separate scores from two spectral ranges were optimally combined, compared to the discrimination errors obtained when scores from singly merged two spectral ranges were used.     

Discrimination of Osteonecrosis and Normal Tissues by Near-Infrared Spectroscopy and Successive Projections Algorithm-Linear Discriminant Analysis

Osteonecrosis of femoral head (ONFH) is a disease characterized by an impaired blood flow in the bone. The pathogenesis is still unknown, which makes an exact diagnosis troublesome and heavily dependent on experience. Exploring the information of molecular level by modern spectroscopy may help to discover the underlying pathogenesis and find its diagnostic application in clinical medicine. The study focuses on the combination of near-infrared (NIR) spectroscopy and classification models for discriminating ONFH and normal tissues. A total of 128 surgical specimens was prepared and NIR spectra were recorded by an integrating sphere. The experiment data set was divided into three subsets, i.e., the training set, validation set, and test set. Successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to compress variables and build the diagnostic model. Partial least square-discriminant analysis (PLS-DA) was used as the reference. Principal component analysis (PCA) was used for exploratory analysis. The results showed that compared to PLS-DA, SPA-LDA provided a more parsimonious model using only seven variables and achieved better performance, i.e., sensitivity of 90.5 and 85%, and specificity of 100 and 95.5% for the validation and test sets, respectively. It indicated that NIR spectroscopy combined with SPA-LDA algorithm was a feasible aid tool for discriminating ONFH from normal tissue.     

Discriminant analysis in the presence of interferences: Combined application of target factor analysis and a Bayesian soft-classifier

A method is described for performing discriminant analysis in the presence of interfering background signal. The method is based on performing target factor analysis on a data set comprised of contributions from analyte(s) and interfering components. A library of data from representative analyte classes is tested for possible contributing factors by performing oblique rotations of the principal factors to obtain the best match, in a least-squares sense, between test and predicted vectors. The degree of match between the test and predicted vectors is measured by the Pearson correlation coefficient, r, and the distribution of r for each class is determined. A Bayesian soft classifier is used to calculate the posterior probability based on the distributions of r for each class, which assist the analyst in assessing the presence of one or more analytes. The method is demonstrated by analyses performed on spectra obtained by laser induced breakdown spectroscopy (LIBS). Single and multiple bullet jacketing transfers to steel and porcelain substrates were analyzed to identify the jacketing materials. Additionally, the metal surrounding bullet holes was analyzed to identify the class of bullet jacketing that passed through a stainless steel plate. Of 36 single sample transfers, the copper jacketed (CJ) and non-jacketed (NJ) class on porcelain had an average posterior probability of the metal deposited on the substrate of 1.0. Metal jacketed (MJ) bullet transfers to steel and porcelain were not detected as successfully. Multiple transfers of CJ/NJ and CJ/MJ on the two substrates resulted in posterior probabilities that reflected the presence of both jacketing materials. The MJ/NJ transfers gave posterior probabilities that reflected the presence of the NJ material, but the MJ component was mistaken for CJ on steel, while non-zero probabilities were obtained for both CJ and MJ on porcelain. Jacketing transfer from a bullet to steel as the projectile passed through the steel also proved difficult to analyze. Over 50% of the samples left insufficient transfer to be identified. Transfer from NJ and CJ jacketing was successfully identified by posterior probabilities greater than 0.8.     

Artificial neural networks for prediction of mycobacterial promoter sequences

A multilayered feed-forward ANN architecture trained using the error-back-propagation (EBP) algorithm has been developed for predicting whether a given nucleotide sequence is a mycobacterial promoter sequence. Owing to the high prediction capability (97%) of the developed network model, it has been further used in conjunction with the caliper randomization (CR) approach for determining the structurally/functionally important regions in the promoter sequences. The results obtained thereby indicate that: (i) upstream region of −35 box, (ii) −35 region, (iii) spacer region and, (iv) −10 box, are important for mycobacterial promoters. The CR approach also suggests that the −38 to −29 region plays a significant role in determining whether a given sequence is a mycobacterial promoter. In essence, the present study establishes ANNs as a tool for predicting mycobacterial promoter sequences and determining structurally/functionally important sub-regions therein.     

Factors analysis of protein O-glycosylation site prediction

To improve the prediction accuracy of O-glycosylation sites, and analyze the structure of the O-glycosylation sites, factor analysis based prediction is proposed in this study. Our studies show that factor analysis strongly boosts machine learning algorithms’ performance in glycosylation site prediction besides demonstrates advantages compared to principal component analysis and nonnegative matrix factorization. In addition, we have found that factor analysis based linear discriminant analysis seem to be a desirable method in O-glycosylation site prediction for its advantage in both accuracy and time complexity than other machine learning methods. To the best of our knowledge, it is the first work to employ factor analysis in glycosylation site prediction and will inspire more future work in this topic.     

An analysis of the interaction energy in some SN2 reactions

The interaction energy between an incoming group X^– and the substrate CRH₂Y at the geometry of the transition state (TS) for bimolecular nucleophilic substitution reactions (with X, Y, and R equal to H and F) has been subjected to decomposition according to the Morokuma scheme. The influence of the basis set and of the geometry chosen for the TS is examined. The results bring out regular trends in the different terms of the decomposition along the whole set of reactions, but they are not sufficient to give a rationale of the energetic factors involved in these reactions.     

Influence of variable selection on partial least squares discriminant analysis models for explosive residue classification

Using a series of thirteen organic materials that includes novel high-nitrogen energetic materials, conventional organic military explosives, and benign organic materials, we have demonstrated the importance of variable selection for maximizing residue discrimination with partial least squares discriminant analysis (PLS-DA). We built several PLS-DA models using different variable sets based on laser induced breakdown spectroscopy (LIBS) spectra of the organic residues on an aluminum substrate under an argon atmosphere. The model classification results for each sample are presented and the influence of the variables on these results is discussed. We found that using the whole spectra as the data input for the PLS-DA model gave the best results. However, variables due to the surrounding atmosphere and the substrate contribute to discrimination when the whole spectra are used, indicating this may not be the most robust model. Further iterative testing with additional validation data sets is necessary to determine the most robust model.