Relation Between Thermal Decomposition of Medicinal Herbs and their Non-Metals Concentration

The studies on the concentration of total nitrogen, phosphorus, sulphur, chlorine, iodine and boron as well as on the thermal decomposition of commercial raw plant materials used in medicine were performed. The 50 independent samples of herbs originating from 25 medicinal plant species collected in 1986–92 were analysed. The content of non-metallic elements was determined spectrophotometrically after previous mineralization of plant sample. The thermal decomposition was performed using the derivatograph with the application of 100 mg samples and heating rate of 5°C min⁻¹. In order to obtain more clear classification of the analysed plant materials principal component analysis (PCA) was applied. Interpretation of PCA results for two databases (non-metals and thermoanalytical data sets) allows to state, that samples of herbs from the same plant species in majority of cases are characterized by similar elemental composition and similar course of their thermal decomposition. In this way the differences in general chemical composition of medicinal plants raw materials can be determined. This revised version was published online in August 2006 with corrections to the Cover Date.     

Influence of The Elemental Composition of Medicinal Fruits on The Results of Their Thermal Analysis

Studies on the thermal decomposition and on the elemental composition of commercial raw plant materials used in medicine were performed. 16independent samples of fruits originating from 12 medicinal plant species collected in1988–92 were analysed. The thermal decomposition was performed using the derivatograph. The content of non-metallic (N, P, S, Cl, I and B) and metallic (Ca, Mg, Fe, Mn, Cu and Zn)elements was determined by spectrophotometric techniques after previous mineralization of sample. In order to obtain more clear classification of the analysed plant materials principal component analysis was applied. The interpretation of PCA results for three databases (thermoanalytical, non-metals and metals data sets) allows to state, that samples of fruits from the same plant species in the majority of cases are characterized by similar elemental composition and similar course of their thermal decomposition. In this way the differences in general chemical composition of medicinal plants raw materials can be determined. This revised version was published online in August 2006 with corrections to the Cover Date.     

五种抗HIV活性中草药微量元素的主成分分析

对产自云南的5种抗HIV活性中草药:臭灵丹、紫茎泽兰、辣子草、滇石栎、密蒙花进行了微量元素测定和主成分分析.结果显示,微量元素分为3个主分量,综合评价第一位的是臭灵丹.     

不相交主成分分析(PCA)和遗传算法(GA)用于差异表达基因的识别

建立了一种基于不相交主成分分析(Disjoint PCA)和遗传算法(GA)的特征变量选择方法, 并用于从基因表达谱(Gene expression profiles)数据中识别差异表达的基因. 在该方法中, 用不相交主成分分析评估基因组在区分两类不同样品时的区分能力; 用GA寻找区分能力最强的基因组; 所识别基因的偶然相关性用统计方法评估. 由于该方法考虑了基因间的协同作用更接近于基因的生物过程, 从而使所识别的基因具有更好的差异表达能力. 将该方法应用于肝细胞癌(HCC)样品的基因芯片数据分析, 结果表明, 所识别的基因具有较强的区分能力, 优于常用的基因芯片显著性分析(Significance analysis of microarrays, SAM)方法.     

基于主成分分析的彩色扫描仪光谱特性化

为了实现扫描仪在不同光源、不同观察者条件下准确获取颜色信息,最大程度的避免同色异谱现象,本文采用光谱的方法对扫描仪进行特性化处理,通过多项式回归和BP神经网络分别与主成分分析法结合,首先对检测样本的光谱反射率进行主成分分析,提取主成分与主成分系数,通过实验得到主成分系数与多项式回归、BP神经网络结构之间的转换模型,实现了扫描仪低维RGB信号对原始光谱反射率信息的重构,进而实现扫描仪的光谱特性化.实验结果表明,多项式项数为19项时,达到训练样本的均方根误差为1.7%,检测样本的均方根误差为1.9%.而包含15个隐层节点的单隐层BP神经网络结构为比较合理的网络结构,达到训练样本的均方根误差为1.3%,检测样本的均方根误差为1.5%.对彩色扫描仪的特征化处理,采用多项式回归法得到光谱特性化精度较低,采用BP神经网络模型能够实现更高的光谱特性化精度.     

能量色散X射线散射结合主成分分析对液体易制毒化学品的探测与鉴别分类

采用能量色散X射线散射(EDXRS)技术探测了8种液体易制毒化学品的X射线散射光谱, 结果显示液体易制毒物质具有各自特征的EDXRS散射图谱. 将液体易制毒化学品的EDXRS散射信息与主成分分析结合, 发现前2个主成分可以表达X射线散射光谱的主要信息, 在PC1～PC2得分分布图上可将液体易制毒化学品进行分类. 研究结果表明, EDXRS光谱技术结合主成分分析法可以实现探测、 鉴别分类液体易制毒化学品, 为隐藏液体易制毒化学品的监管控制提供一个可行的鉴别方法.     

Thermal decomposition of methylxanthines

The thermal decomposition of theophylline, theobromine, caffeine, diprophylline and aminophylline were evaluated by calorimetrical, thermoanalytical and computational methods. Calorimetrical studies have been performed with aid of a heat flux Mettler Toledo DSC system. 10 mg samples were encapsulated in a 40 μL flat-bottomed aluminium pans. Measurements in the temperature range form 20 to 400°C were carried out at a heating rate of 10 and 20°C min⁻¹ under an air stream. It has been established that the values of melting points, heat of transitions and enthalpy for methylxanthines under study varied with the increasing of heating rate. Thermoanalytical studies have been followed by using of a derivatograph. 50, 100 and 200 mg samples of the studied compounds were heated in a static air atmosphere at a heating rate of 3, 5, 10 and 15°C min⁻¹ up to the final temperature of 800°C. By DTA, TG and DTG methods the influence of heating rate and sample size on thermal destruction of the studied methylxanthines has been determined. For chemometric evaluation of thermoanalytical results the principal component analysis (PCA) was applied. This method revealed that first of all the heating rate influences on the results of thermal decomposition. The most advantageous results can be obtained taking into account sample masses and heating rates located in the central part of the two-dimensional PCA graph. As a result, similar data could be obtained for 100 mg samples heated at 10°C·min⁻¹ and for 200 mg samples heated at 5°C min⁻¹.     

表面增强拉曼光谱结合主成分分析快速筛查食品接触材料中多环芳烃

建立了表面增强拉曼/主成分分析快速筛查食品接触材料中4种多环芳烃的分析方法。采用纳米银溶胶作为增强基底,碘化钾为絮凝剂,实现了4种多环芳烃（芘、荧蒽、苯并［b］荧蒽、苯并［k］荧蒽）的表面增强拉曼分析。针对食品接触材料中4种多环芳烃拉曼谱峰重叠难以鉴别区分的问题,采用主成分分析法分别对同浓度多环芳烃、不同浓度多环芳烃以及多环芳烃混合样品进行分析。结果表明,4种多环芳烃均可得到较好的鉴别。该方法成功用于食品接触材料迁移液中4种多环芳烃的快速筛查。     

Classification of Wines Based on Combination of ^1H NMR Spectroscopy and Principal Component Analysis

A combination of ^1H nuclear magnetic resonance （NMR） spectroscopy and principal component analysis （PCA） has shown the potential for being a useful method for classification of type, production origin or geographic origin of wines. In this preliminary study, twenty-one bottled wines were classified/separated for their location of production in Shacheng, Changli and Yantai, and the types of the blended, medium dry, dry white and dry red wines, using the NMR-PCA method. The wines were produced by three subsidiary companies of an enterprise according to the same national standard. The separation was believed to be mainly due to the fermentation process for different wines and environmental variations, such as local climate, soil, underground water, sunlight and rainfall. The major chemicals associated with the separation were identified.     

基于微量热法和主成分分析的盐酸巴马汀抗白色念珠菌作用的研究

采用LKB-2277生物活性检测系统, 测定了37 ℃时白色念珠菌在巴马汀作用下生长代谢的热谱曲线, 并获得9个相应的定量热动力学参数, 经主成分分析综合评价巴马汀的抗白色念珠菌作用. 结果表明巴马汀的抗白色念珠菌作用主要受热谱曲线第二指数生长期的生长速率常数k2和最大产热功率 的影响, 通过分析这两个主要参数的数值变化, 能更方便、快捷和准确地评价巴马汀的抗白色念珠菌作用, 为进一步评价其它药物和化合物的抗菌作用提供了有用的方法和基础.     

Characterization of Automobile Plastics by Principal Component Analysis and Near-Infrared Spectroscopy

Based on near-infrared diffuse reflection spectroscopy, multivariate calibration models for discarded automobile plastic were constructed using principal component analysis and clustering analysis to rapidly characterize four widely employed materials: polypropylene, polyethylene, acrylonitrile butadiene styrene, and polymethylmethacrylate with an accuracy rate of 97%. The method was shown to rapidly discriminate waste automobile plastic.     

Classification of Chinese Herbal Medicine by Laser-Induced Breakdown Spectroscopy with Principal Component Analysis and Artificial Neural Network

Chinese herbal medicine has attracted increasing attention because of the unique and significant efficacy in various diseases. In this paper, three types of Chinese herbal medicine, the roots of Angelica pubescens, Codonopsis pilosula, and Ligusticum wallichii with different places of origin or parts, are analyzed and identified using laser-induced breakdown spectroscopy (LIBS) combined with principal component analysis (PCA) and artificial neural network (ANN). The study of the roots of A. pubescens was performed. The score matrix is obtained by principal component analysis, and the backpropagation artificial neural network (BP-ANN) model is established to identify the origin of the medicine based on LIBS spectroscopy of the roots of A. pubescens with three places of origin. The results show that the average classification accuracy is 99.89%, which exhibits better prediction of classification than linear discriminant analysis or support vector machine learning methods. To verify the effectiveness of PCA combined with the BP-ANN model, this method is used to identify the origin of C. pilosula. Meanwhile, the root and stem of L. wallichii are analyzed by the same method to distinguish the medicinal materials accurately. The recognition rate of C. pilosula is 95.83%, and that of L. wallichii is 99.85%. The results present that LIBS combined with PCA and BP-ANN is a useful tool for identification of Chinese herbal medicine and is expected to achieve automatic real-time, fast, and powerful measurements.     

基于主成分分析和小波神经网络的近红外多组分建模研究

将小麦叶片原始光谱经过预处理后,采用主成分分析(PCA)对数据进行降维,取前3个主成分输入小波神经网络,建立了基于主成分分析和小波神经网络的近红外多组分预测模型(WNN);进一步研究了小波基函数个数的选取(WNN隐层节点数)对小波神经网络模型性能的影响,并将WNN模型与偏最小二乘法(PLS)和传统的反向传播神经网络(BPNN)模型进行了比较.结果表明,所建立的WNN模型能用于同时预测小麦叶片全氮和可溶性总糖两种组分含量,其预测均方根误差(RMSEP)分别为0.101%和0.089%,预测相关系数(R)分别为0.980和0.967.另外,在收敛速度和预测精度上,WNN模型明显优于BPNN和PLS模型,从而为将小波神经网络用于近红外光谱的多组分定量分析奠定了基础.     

主成分分析-支持向量机用于肝病分类模型

将主成分分析(PCA)用于肝功能检测数据特征提取,然后用支持向量机(SVM)对乙肝、丙肝、肝硬化、正常人样本建立分类模型。采用高斯径向基函数(RBF)为核函数,调节核函数参数C及σ以建立最佳支持向量机模型。该模型对训练集的识别率为99.3%,对预测集的预测率为96.4%。结果表明:PCA-SVM法建立的肝病分类模型能较好的区分乙肝、丙肝、肝硬化及正常人,且分类效果优于传统支持向量机及人工神经网络(ANN)分类模型。     

主成分分析用于浙贝母中无机元素含量的研究

采用原子吸收光谱（AAS）法测定了10个浙贝母样品中12种无机元素的含量,建立浙贝母无机元素指纹图谱,并用SPSS13．0方差分析和主成分分析对浙贝母中的微量元素进行了分析。结果表明,浙贝母的特征无机元素是Cd,Cu,Pb,Mg,Cr,Fe,Zn。可见主成分分析法是浙贝母无机元素分析的有效方法。     

Analysis of the Dynamic Decomposition of Unsaturated Fatty Acids and Tocopherols in Commercial Oils During Deep Frying

The decomposition of unsaturated fatty acids and tocopherols in 10 commercial edible oils during deep frying was investigated. The dominant tocopherol in oils rich in polyunsaturated fatty acids (PUFAs) was γ-tocopherol, except for natural perilla oil (δ-tocopherol dominant), and the main tocopherol in oleic acid-rich oils was α-tocopherol. The PUFA-rich oils had higher tocopherol contents than the oleic acid-rich oils. Both the reduction rate of total unsaturated fatty acid (TUFA) and total tocopherol (TToc) were linear with frying time (t). The decomposition rate of TToc is faster than that of TUFA since the slope values obtained from fitting equations (Y?=?k t) k_TToc (1.520–14.483) were obviously larger than for k_TUFA (0.155–0.270). By establishing a dynamic decomposition index, unsaturated fatty acids and tocopherol in oils showed dynamic decomposition over multiple frying cycles. The obtained results showed that decomposition characteristics of oils are related to their fatty acid compositions.     

药物水溶解度的QSAR研究和VolSurf参数

药物的水溶解度与其吸收密切相关。本文利用一种新的计算方法,VolSurf,预测药物的水溶解度并测定有利于药物水溶解度的主要分子特征。被测化合物包括26个结构不同的药物,通过偏最小二乘分析法,对药物水溶解度实验值与分子特征进行相关,得到较好的模型(r2=0.90,q2=0.77)。将化合物分为训练集和预测集进行相关分析,结果表明以18个化合物所建立的训练集模型对其余8个化合物有较好的预测能力,预测的标准偏差(SDEP)为0.59。参数分析表明分子与水相互作用的3个局部能量最小值越小,且它们之间的距离越大,对其水溶解度越有利;亲水性占主导因素的分子有高的水溶解度;分子的疏水性越强,在水中的溶解性越弱;大分子的溶解度较小分子溶解度低。     

Fisher判别在气相色谱-质谱分析助燃剂燃烧残留物中的应用

为寻找一种用于火场助燃剂燃烧残留物鉴定的更为准确、有效的模式识别方法,对7种常见助燃剂在不同载体上的燃烧残留物样品及未知送检样品进行气相色谱-质谱（GC-MS）分析测试,通过特征组分分析鉴定出未知样品中含有汽油成分。同时运用Fisher判别及PCA（主成分分析）/Fisher判别联用两种判别方法对样本数据进行了分析处理,PCA/Fisher判别联用的结果表明送检样本中含有硝基油漆稀料成分,而仅使用Fisher判别的结果表明送检样本中含有93^#汽油。通过将两种分析方法所得结果与GC-MS特征组分分析的结果进行比对发现,Fisher判别能够对7种助燃剂燃烧残留物的样本实现更有效的分类,对未知样本的判别更为有效。该研究结果为火场助燃剂鉴定提供了新的数据分析手段。     

Application of multi-way data analysis on excitation-emission spectra for plant identification

The ability to distinguish among diets fed to Damascus goats using excitation-emission luminescence spectra was investigated. These diets consisted of Medicago sativa L. (alfalfa), Trifolium spp. (clover), Pistacia lentiscus, Phyllirea latifolia and Pinus brutia. The three-dimensional luminescence response surface from phosphate buffered saline (PBS) extracts of each material was analyzed using muti-way analysis chemometric tools (MPCA) and parallel factor analysis (PARAFAC). Using three principal components, the spectra from each diet material were distinguished. Additionally, fecal samples from goats fed diets of either alfalfa or clover hays were investigated. The application of MPCA and PARAFAC to these samples using models derived from the pre-digested diet materials was strongly suggestive of the utility of similarly derive training samples for the elucidation of botanical diet composition for animals.     

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.