应用系统聚类法对海洛因裂解谱图分类

采用系统聚类法对海洛因样品裂解谱图进行分类研究.为了能够对铂丝裂解法分析海洛因溶液的可行性进行正确评价,首先对系统聚类法中的相似性量度和聚类方法进行了选择,然后运用相关性分析法和系统聚类法对裂解谱图的重现性结果进行测定,得到了较好的评价结果;最后对10种海洛因样品的裂解谱图进行了系统聚类分析,得出了分类结果.     

差分拉曼光谱技术结合K-means聚类法对牙膏的快速分类

建立了差分拉曼光谱技术结合K-means聚类法对牙膏快速分类的方法。对37个牙膏样品编号,将其分别涂抹于载玻片上,晾干,使用差分拉曼光谱仪进行扫描。调用R语言软件中fpc、factoextra、cluster数据库中的na.omit和scale函数对37个牙膏样品的差分拉曼光谱数据进行标准化处理,利用手肘法和Gap Statistic算法优化聚类数。在最佳聚类数为4的条件下,通过K-means聚类法对牙膏样品进行分类,并使用层次聚类分析法进行验证。结果显示,37个牙膏样品被分为4类,并且两种方法的分类结果一致。     

X射线荧光光谱结合聚类分析对橡胶轮胎的检验

应用X射线荧光光谱结合系统聚类分析对30种不同的橡胶轮胎样本进行检验研究。利用X射线荧光光谱仪对橡胶轮胎中的主要元素及其含量进行测定,根据测定结果使用聚类分析对样本进行分类。选择最长距离法作为类与类之间距离的定义方法,采用平方欧式距离作为度量区间描述样品间亲疏程度进行系统聚类,最终将30个样本分为12类。该方法简单易行,分类结果可靠且无损轮胎样本,为实际案件的橡胶轮胎检验提供了一种检验思路。     

X射线荧光光谱法结合多元统计学对热敏纸的检验研究

为建立一种快速且无损检验热敏纸的科学有效的方法,利用能量色散型X射线荧光光谱(XRF)对38个不同商家,不同规格的热敏纸样品进行检验,首先根据每个样品所测量得到的元素的不同,将38个样品分成四大类,同时采用SPSS25.0软件中的系统聚类法对38个样品的元素数据进行聚类分析处理,结果分成了12小组,再结合SPSS25.0软件中的判别分析法对上述结果进行验证,实现了基于X射线荧光光谱结合聚类分析建立数学模型用于区分热敏纸种类的目的,该方法简单易行,可以为案件侦破提供线索、指明方向。     

烟草中游离氨基酸的超高效液相色谱快速分析及化学计量学研究

采用超高效液相色谱法(UPLC)对烟草中的游离氨基酸进行了快速分离与测定。烟草中17种氨基酸在8 min之内实现了快速分离和测定。同时采用聚类分析法(CA)和主成分分析法(PCA)对31个烟草样品(4个品种:烤烟,香料烟,晒烟,白肋烟)中烟草氨基酸含量差异进行了研究。结果表明烟草样品中氨基酸总量为:白肋烟晾晒烟香料烟烤烟。聚类分析表明不同种类烟草样品可以完全各自聚为一类,同时主成分分析中第一主成分与第二主成分占到氨基酸总变量的73.3%,其中第一主成分占到总变量的62.3%,表明不同种类间烟草样品中氨基酸组分及含量差异较大,其结果与聚类分析一致。该方法简单快捷,各种氨基酸的含量分布特征能反应不同种类烟丝的特性,可用于不同种类烟草的比较和分类。     

超光谱技术结合化学计量法区分直液式走珠笔墨迹种类

为了填补直液式走珠笔墨迹种类区分的空白,试验对市面上常见的29种直液式走珠笔墨迹样品进行了400～1 000nm光谱区间的超光谱成像,将得到的光谱数据进行简单的平滑预处理后,按照其光谱走势进行了初步的鉴别分类,采用SPSS.25分析软件中的沃尔德系统聚类分析法进一步分类,并采用主成分分析法评价了聚类分析的分类结果。试验结果表明:按照650～1 000nm光谱区间曲线上升趋势的差异,可将29个墨迹样品分为第Ⅰ大类和第Ⅱ大类,根据系统聚类分析的树状图和按集中计划表绘制的散点图能够进一步将这29种墨迹样品分为8小类,利用主成分分析法分别提取Ⅰ、Ⅱ大类光谱数据中7个和4个主成分,其中,前两个主成分累计方差贡献率分别达到了87%,97%,用其绘制主成分得分图,结果显示8个小类中的样品均能聚在一起,表明系统聚类效果良好。     

X射线荧光光谱法对橡胶鞋底的分类

为建立案件现场常见橡胶鞋底物证的分类方法,利用X射线荧光光谱,对40个不同品牌、不同系列的橡胶鞋底样品中的无机元素进行了检验。并结合多元统计学,分别选用主成分分析法、k均值聚类法及判别分析法等对实验结果进行分析。结果表明,该方法简便快速、结果准确可靠且无损检材,对样品的分类效果较好。利用该方法可以对橡胶鞋底物证进行分类。     

利用拉曼光谱对塑料拖鞋鞋底样品的分类研究

为建立案件现场常见塑料拖鞋鞋底物证的分类方法,利用拉曼光谱对30个不同品牌、不同系列的塑料拖鞋鞋底样品进行了检验并进行分类。同时,结合多元统计学,分别利用系统聚类法、相关性分析法与主成分分析法对实验结果进行分析。结果表明,该方法简便快速、实验结果准确可靠,且无损检材,对样品的分类效果较好。     

基于离子迁移谱的假币用纸快速检测及溯源

该文采用离子迁移谱（IMS）对20种假币用纸进行分析，通过快速检测假币用纸中的可挥发性组分，构建假币用纸的指纹谱图库。基于主成分分析（PCA）和层次聚类分析（HCA）对迁移时间8 ~ 17.5 ms范围的迁移谱信息进行处理，从而对假币用纸进行分类。PCA可将同种假币用纸聚类，HCA则可进一步将20种假币用纸准确分类，其对6个未知来源的半成品假币的分类正确率达83.33%。结果表明，IMS快速检测分析是比较和分类假币用纸的有效方法。     

不同产地白芷药材高效液相色谱指纹图谱研究

本文采用高效液相色谱-二极管阵列检测器(HPLC-DAD)法建立中药白芷的指纹图谱.应用化学计量学中两种不同的模式识别方法(主成分分析法和系统聚类分析法)对实验数据进行处理,以找出来自三个不同产地30个中药白芷样品间的相似性及差异性.两种模式识别方法均能成功地按样品的来源将不同产地的样品正确分类.建立了不同产地中药白芷的识别方法,该方法能有效地控制中药白芷的质量,并能为其它中药产品的化学模式识别提供参考.     

Identification of the Flavone in the Flower Bud of Panax Quinquefolium and Its Content Determination in the Different Parts

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Classification of rapeseed and soybean oils by use of unsupervised pattern-recognition methods and neural networks

Unsupervised pattern-recognition methods and Kohonen neural networks have been applied to the classification of rapeseed and soybean oil samples according to their type and quality by use of chemical and physical properties (density, refractive index, saponification value, and iodine and acid numbers) and thermal properties (thermal decomposition temperatures) as variables. A multilayer feed-forward (MLF) neural network (NN) has been used to select the most important variables for accurate classification of edible oils. To accomplish this task different neural networks architectures trained by back propagation of error method, using chemical, physical, and thermal properties as inputs, were employed. The network with the best performance and the smallest root mean squared (RMS) error was chosen. The results of MLF network sensitivity analysis enabled the identification of key properties, which were again used as variables in principal components analysis (PCA), cluster analysis (CA), and in Kohonen self-organizing feature maps (SOFM) to prove their reliability.     

Classification of byzantine glass samples by the use of different chemical component features

Summary An attempt is made to perform a classification of a small data set (nine unique Byzantine glass samples) by the use of different chemical component features (all components, major components, minor metallic components and colouring components). The strategy applied was cluster analysis (average and single linkage, centroid linkage). The classification using different variables has indicated that some samples have a more special position in the sample set than required by the traditional approaches of determination of similarities or dissimilarities between samples.

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Klassifizierung von byzantinischen Glasproben mit Hilfe verschiedenartiger chemischer Komponenten

     

Ultra-performance LC/TOF MS analysis of medicinal Panax herbs for metabolomic research

In this study, metabolite profiling of five medicinal Panax herbs including Panax ginseng (Chinese ginseng), Panax notoginseng (Sanchi), Panax japonicus (Rhizoma Panacis Majoris), Panax quinquefolium L. (American ginseng), and P. ginseng (Korean ginseng) were performed using ultra-performance LC-quadrupole TOF MS (UPLC-QTOFMS) and multivariate statistical analysis technique. Principal component analysis (PCA) of the analytical data showed that the five Panax herbs could be separated into five different groups of phytochemicals. The chemical markers such as ginsenoside Rf, 20(S)-pseudoginsenoside F11, malonyl gisenoside Rb1, and gisenoside Rb2 accountable for such variations were identified through the loadings plot of PCA, and were identified tentatively by the accurate mass of TOFMS and partially verified by the available reference standards. Results from this study indicate that the proposed method is reliable for the rapid analysis of a group of metabolites present in herbal medicines and other natural products and applicable in the differentiation of complex samples that share similar chemical ingredients.     

Classification of byzantine glass samples by principal component analysis

Summary Nine samples of byzantine glass classified previously by cluster analysis are classified by principal component analysis (PCA). A visual inspection of plots in coordinates of the first two principal components gives essentially the same results as cluster analysis. In addition, PCA indicates relationships among the classification variables.

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Klassifizierung byzantinischer Glasproben durch Analyse der Hauptbestandteile

     

基于显微共聚焦拉曼光谱技术快速检测3种梭菌中肉毒梭菌

提出了一种基于显微共聚焦拉曼光谱技术的肉毒梭菌快速鉴别方法.利用共聚焦显微拉曼光谱技术(CRM)采集了肉毒梭菌、艰难梭菌和产气荚膜梭菌的拉曼光谱,比较了3种梭菌的平均拉曼光谱,采用基线校正、标准正态变换、Savitzky-Golay 5点平滑和最大最小值归一化预处理后,借助主成分分析(PCA)降维并提取特征变量,对样本...     

胶束电动毛细管色谱法同时测定西洋参中人参皂苷Rg1、Re和Rb1

建立了西洋参中人参皂苷Rg1、Re及Rb1同时分离测定的胶束电动毛细管色谱新方法,以解决西洋参样品中难溶于水的3种人参皂苷的准确定量问题。以40.2 cm(有效长度30 cm)×50 μm的熔融石英毛细管柱为分离柱,分离缓冲液的组成为V(15 mmol/L Na2B4O7+30 mmol/L H3BO3 (pH 9.0)+100 mmol/L十二烷基硫酸钠(SDS)+30 g/L聚乙二醇35000):V(甲醇):V(异丙醇)=2:1:1,于214 nm下检测。详细研究了影响分离的因素。Rg1、Re及Rb1检出限(信噪比(S/N)为3)分别为30、40及30 mg/L,定量限(S/N=9)分别为90、120及90 mg/L,加标回收率为87.4%～95.2%。用该法测定了西洋参标准物质,并与高效液相色谱法的检测结果进行了比对,结果吻合。应用该方法分别测定了中国、加拿大及美国的西洋参,获得满意的结果。     

Monitoring of water quality in South Paris district by clustering and SIMCA classification

With the aim of obtaining a monitoring tool to assess the quality of water, a multivariate statistical procedure based on cluster analysis (CA) coupled with soft independent modelling class analogy (SIMCA) algorithm, providing an effective classification method, is proposed. The experimental data set, carried out throughout the year 2004, was composed of analytical parameters from 68 water sources in a vast southwest area of Paris. Nine variables carrying the most useful information were selected and investigated (nitrate, sulphate, chloride, turbidity, conductivity, hardness, alkalinity, coliforms and Escherichia coli). Principal component analysis provided considerable data reduction, gathering in the first two principal components the majority of information representing about 92.2% of the total variance. CA grouped samples belonging to different sites, distinctly correlating them with chemical variables, and a classification model was built by SIMCA. This model was optimised and validated and then applied to a new data matrix, consisting of the parameters measured during the year 2005 from the same objects, providing a fast and accurate classification of all the samples. The most of the examined sources appeared unchanged during the 2-year period, but five sources resulted distributed in different classes, due to statistical significant changes of some characteristic analytical parameters.     

CE Profiling of Organic Acids in Distilled Alcohol Beverages Using Pattern Recognition Analysis

The objective of this work has been to assess the potential of capillary isotachophoretic organic acids profiling using multivariate statistical methods to classify brandy samples and wine distillate samples. The leading electrolyte was 10 mmol L⁻¹ hydrochloric acid including 0.1% methylhydroxylethylcellulose adjusted with β-alanine to pH 2.9. The terminating electrolyte was 5 mmol L⁻¹ acetic acid. Principal component analysis, cluster analysis, and linear discriminant analysis were used for the classification of beverages. The results show that for the 12 acids analysed, 98.57% of the total variance is extracted by the six principal components (PC). After performing backward linear discriminant analysis, a classification function was obtained containing four variables: formic (PC2-loadings: 0.989), lactic (PC1-loadings: 0.886), malic (PC1-loadings: 0.989) and oxalic (PC2-loadings: 0.777) acids, which provide 100.0% correct classification of brandies and wine distillates.

     

Analysis of Psoralea corylifolia L. fruits in different regions

Application of multivariate data analysis has become a popular method in the last decades, mainly because it can provide information not otherwise accessible. The information includes classification, searching similarities, finding relationships, finding physical significance to principal components, etc. Twenty-two Chinese medicinal herbs containing twelve constituents were collected and determined by HPLC. The results were studied by hierarchical cluster analysis (HCA) and principal components analysis (PCA). It was shown that the samples could be clustered reasonably into three groups, hence corresponding with the typical habitats of Psoralea corylifolia L.