Worldwide interlaboratory study on the determination of ochratoxin A in different wine type samples

Interlaboratory studies are decisive tools to help the validation of a specific analytical methodology or to assess the reproducibility of the use of different methods to analyze a given compound or compounds in certain sample matrices. In this work, homogeneous samples of two white wines (“White Wine” and “White Liqueur Wine”) and one red wine (“Red Fortified Wine”) from Portugal with different production techniques and characteristics, namely in alcohol strength (10.5%, 16.0% and 19.0% ethanolic content, respectively), were analyzed for their contents in ochratoxin A (OTA), a mycotoxin generated from fungal contamination. White Liqueur Wine was naturally contaminated, whereas the other two wine type were spiked with ethanolic OTA solutions. The participation of 24 laboratories from 17 countries of five continents was ensured for this study. Although with no restrictions in terms of analytical methodology to employ, 75% of the laboratories resorted to immunoaffinity columns clean-up followed by high performance liquid chromatography with fluorescence detection (HPLC-FD), most of them in accordance with the European Standard EN 14133. For White Wine samples, the general mean OTA concentration was 1.96 μg/l (two outliers) with interlaboratorial standard deviation (s_L) of 0.53 μg/l; for White Liqueur Wine, mean of 1.59 μg/l (one outlier), with s_L = 0.59 μg/l; and for Red Fortified Wine, mean of 2.73 μg/l (no outliers), with s_L = 0.96 μg/l. Outliers were determined by Cochran and Grubbs tests. The Horrat index, recommended by the Association of Official Analytical Chemists (AOAC) for the quality assurance of the collaborative study was, on average, 1.7. This study proved that OTA determination in wines is reproducible, regardless of the methodology employed.     

On shadow elimination after moving region segmentation based on different threshold selection strategies

A popular approach for detecting moving object regions in video sequences is the application of the background subtraction technique. According to this technique the background (reference) image is subtracted from the current image frame and the moving parts are detected by the selection of a suitable threshold. In this paper we present our work to discriminate the moving pixels of the generated difference images from the relatively stationary pixels through the use of three different threshold selection strategies, namely, (i) ‘3σ edit rule’, (ii) rule utilizing the Hampel identifier, and (iii) rule based on an ad hoc selection of threshold. Further, after segmentation a method of classification, based on a moving shadow search technique, previously developed by the authors, has been applied to segregate the moving shadow region from the actual moving object. The speed-up achieved through the use of the three aforementioned techniques on the core moving shadow search process, compared to that where no such process has been applied, has been documented. The final outcomes of applying the shadow detection technique after segmenting using each of the threshold selection strategies, one at a time, on some indoor video sequences have been demonstrated and comparison of the methods made.     

On the robustness of balanced fractional 2m factorial designs of resolution 2l+1 in the presence of outliers

Summary By use of the algebraic structure, we obtain a simplified expression for the outlier-insensitivity factor for balanced fractional 2^m factorial (2^m-BFF) designs of resolution 2l+1 derived from simple arrays (S-arrays), whose measure has been introduced by Ghosh and Kipnegeno (1985,J. Statist. Plann. Inference,11, 119–129). It is defined by use of the measure suggested by Box and Draper (1975,Biometrika, 62 (2), 347–352). As examples, we study the sensitivity ofA-optimal 2^m-BFF designs of resolution VII (i.e.,l=3) given by Shirakura (1976,Ann. Statist.,4, 515–531; 1977,Hiroshima Math. J.,7, 217–285). We observe that these designs are robust in the sense that they have low sensitivities. Research supported in part by Grant 59530012 (C) and 60530014 (C), Japan.     

面向LAMOST的天体光谱离群数据挖掘系统研究

在宇宙中寻求未知天体是人类探索宇宙奥妙所追求的目标之一,离群数据挖掘是发现未知天体光谱数据的一种有效途径。文章首先以VC++和Oracle9i为开发工具,设计与实现了面向LAMOST的恒星光谱离群数据挖掘系统,并给出了其软件体系结构和模块功能。其次,对基于中值滤波器的恒星光谱数据预处理、基于距离的恒星光谱数据聚类、基于距离支持度的恒星光谱数据离群数据挖掘、基于主分量分析法PCA的恒星光谱数据离群数据的三维可视化等主要关键技术进行了详细描述。最后,基于SDSS恒星光谱数据的运行结果表明,利用该系统寻找天体光谱离群数据是可行的,从而为寻找未知的、特殊的天体光谱数据提供了一种新途径。     

OUTLIER TEST IN RANDOMIZED LINEAR MODEL

In this paper, we give an approach for detecting one or more outliers inrandomized linear model The likelihood ratio test statistic and its distributions under the null hypothesis and the alternative bypothesis are given. Furthermore, the rebustneas of the test statistic in a certain sere is proved. Finally, the optimality properties of the test are derived.     

Benchmarking and validating algorithms that estimate p<Emphasis Type="Italic">K</Emphasis><Subscript>a</Subscript> values of drugs based on their molecular structures

The REGDIA regression diagnostics algorithm in S-Plus is introduced in order to examine the accuracy of pK _a predictions made with four updated programs: PALLAS, MARVIN, ACD/pKa and SPARC. This report reviews the current status of computational tools for predicting the pK _a values of organic drug-like compounds. Outlier predicted pK _a values correspond to molecules that are poorly characterized by the pK _a prediction program concerned. The statistical detection of outliers can fail because of masking and swamping effects. The Williams graph was selected to give the most reliable detection of outliers. Six statistical characteristics (F _exp, R ², , MEP, AIC, and s(e) in pK _a units) of the results obtained when four selected pK _a prediction algorithms were applied to three datasets were examined. The highest values of F _exp, R ², , the lowest values of MEP and s(e), and the most negative AIC were found using the ACD/pK _a algorithm for pK _a prediction, so this algorithm achieves the best predictive power and the most accurate results. The proposed accuracy test performed by the REGDIA program can also be applied to test the accuracy of other predicted values, such as log P, log D, aqueous solubility or certain physicochemical properties of drug molecules.     

Efficient Computation of Subset Influence in Regression

Abstract

The detection of influential cases is now accepted as an essential component of regression diagnostics. It is also well established that two or more cases that are individually regarded as noninfluential may act in concert to achieve a high level of joint influence. However, for the majority of data sets it is computationally infeasible to calculate the influence for all subsets of a given size. In this article we address this problem and suggest an algorithm that greatly reduces the computational effort by making use of a sequence of upper bounds on the influence value. These upper bounds are much less costly to evaluate and greatly reduce the number of subsets for which the influence value must be explicitly determined.     

A Deterministic Algorithm for Robust Location and Scatter

Most algorithms for highly robust estimators of multivariate location and scatter start by drawing a large number of random subsets. For instance, the FASTMCD algorithm of Rousseeuw and Van Driessen starts in this way, and then takes so-called concentration steps to obtain a more accurate approximation to the MCD. The FASTMCD algorithm is affine equivariant but not permutation invariant. In this article, we present a deterministic algorithm, denoted as DetMCD, which does not use random subsets and is even faster. It computes a small number of deterministic initial estimators, followed by concentration steps. DetMCD is permutation invariant and very close to affine equivariant. We compare it to FASTMCD and to the OGK estimator of Maronna and Zamar. We also illustrate it on real and simulated datasets, with applications involving principal component analysis, classification, and time series analysis. Supplemental material (Matlab code of the DetMCD algorithm and the datasets) is available online.     

三维坐标异常数据判定方法的模拟与实验研究

近红外漫反射光谱具有无创伤、连续、无感染、速度快等诸多优势,在人体成分无创伤检测方面有很好的应用前景。但是在测量过程中,随机噪声、干扰组分以及检测条件的改变等容易导致异常光谱。判定并剔除异常光谱对于提高近红外无创血液成分检测的可靠性具有重要意义。首先分析了近红外漫反射光谱无创血糖检测中可能出现的异常数据类型,提出了一种综合利用马氏距离、光谱残差和化学值残差三个指标构造三维空间对样本集进行检验的三维坐标异常数据判定方法。其次,针对三层皮肤组织模型,在参数中设置人为失误、极端成分含量以及异常温度变化的样本,通过蒙特卡罗（MC)模拟程序得到一组正常模拟数据以及一组包含化学值异常和光谱异常的模拟数据,并利用三维坐标法进行异常数据的判定。结果显示,该方法能识别出全部异常样本,剔除这些异常样本后,偏最小二乘（PLS）校正模型的交互验证均方根误差（RMSECV）由21.2 mmol·L-1降低到1.1 mmol·L-1,初步验证了该方法的可行性。进一步,对三位受试者开展了口服葡萄糖耐量试验（OGTT）,通过在测量受试者血糖参考值的同时同步采集其手指部位的漫反射光谱,获得了三组在体实验数据。并利用三维坐标法和蒙特卡罗交互验证法进行异常数据的判定和剔除,最后建立PLS模型比较两种异常数据判别方法的效果：剔除三维坐标法识别出的异常数据后,三组样本建立的校正模型的决定系数显著提升,RMSECV平均值由2.1 mmol·L-1降低至0.8 mmol·L-1,效果优于蒙特卡罗交互验证法的结果。这些结果表明,基于马氏距离、光谱残差和化学值残差的三维坐标异常数据判定方法能有效识别近红外无创血糖测量中的异常数据,在在体成分检测应用中有显著优势。