血清代谢轮廓分析在评估乙肝肝硬化病程中的应用

采用高效液相色谱-质谱联用(HPLC-MS)作为代谢组学研究平台,分析不同Child-Pugh分级肝硬化病人和健康人群的血清标本,获取代谢轮廓.对数据进行主成分分析(PCA)和正交偏最小二乘判别分析(OPLS-DA),用各组病例的80%作为训练数据构建疾病的OPLS-DA区分模型,以剩余的20%作为检测数据,观察模型对...     

气相色谱-质谱法鉴别香水样品真伪

建立气相色谱-质谱法鉴别香水样品真伪的方法。利用气相色谱-质谱联用法测定香水中化学成分及其含量，按照商品不同价位将分析数据分组进行主成分分析以及偏最小二乘分析，并通过模型载荷图与变量投影重要性分析，判断影响香水真假的主要差别成分。结果显示，主成分分析构建的模型可行性优于偏最小二乘-判别分析构建的模型，影响香水真假的主要差别成分为芳樟醇。该方法可用于真假香水的鉴别。     

基于最小二乘支持向量机的色谱指纹图谱预测银杏叶总抗氧化活性

在色谱图基线校正和色谱峰匹配基础上,提出以40个银杏叶提取物HPLC指纹图谱的色谱图轮廓作为输入,相应的提取物总抗氧化活性作为输出,建立最小二乘支持向量机回归模型,并对包含10个样本的测试集进行了预测.最小二乘支持向量机的测试集预测误差均方根(RMSEP)为0.0230,预测结果优于目前普遍使用的误差反向传播神经网络和偏最小二乘回归.与采用色谱峰面积为分析变量的模型预测结果比较表明:采用消除干扰后的色谱图全谱轮廓保留了样本的全部信息,预测结果更好     

基于LVs-LS-SVM近红外光谱检测柴油运动粘度的方法研究

为了研究不同光谱输入方法对柴油运动粘度预测模型的影响,本文对预处理后的柴油光谱进行主成分分析(PCA)得到的前6个主成分(PCs)、建立偏最小二乘(PLS)回归曲线选取有效波长(EWs)和运用偏最小二乘回归(PLSR)建模得到的14个潜变量(LVs),将三者分别输入至最小二乘支持向量机(LS-SVM),对结果进行比较分析表明:LVs-LS-SVM建模得到的结果为柴油运动粘度(R_(Pre)~2=0.839,RMSEP=0.317,RPD=1.54),优于EWs-LS-SVM和PCs-LS-SVM模型,为柴油参数测定便携式仪器的开发奠定基础。     

UHPLC/Q-Orbitrap-MS结合化学计量学方法研究酸枣仁入血成分

建立了一种基于超高效液相色谱-质谱联用技术结合化学计量学快速分析酸枣仁入血成分的方法。以腹腔注射对氯苯丙氨酸(PCPA)建立失眠大鼠模型,给药组连续灌胃给予酸枣仁水提物(30 g/kg)5 d后,分别收集模型组和给药组的血清样品。采用Oasis PRIME HLB 96孔板对血清样品预处理,以超高效液相色谱-静电场轨道离子阱质谱(UHPLC/Q-Orbitrap-MS)进行数据采集,最后采用主成分分析(PCA)、正交偏最小二乘判别分析(OPLS-DA)及变量投影重要性分析(VIP)筛选差异性的原型化合物及代谢产物。共鉴定和推断了20个入血成分,包括11个原型成分及9个代谢产物。研究结果为进一步深入探讨酸枣仁改善失眠的效应物质提供了基础。     

阿卡波糖对Ⅱ型糖尿病大鼠尿液代谢轮廓的影响

采用超高效液相色谱-质谱联用(UPLC-MS/MS)方法研究了阿卡波糖对Ⅱ型糖尿病大鼠代谢轮廓的影响, 分析了健康组、 糖尿病模型组和糖尿病给予阿卡波糖组的大鼠尿样, 采用主成分分析法(PCA)和偏最小二乘法-判别分析(PLS-DA)对数据进行分析. PCA得分图表明, 健康组、 糖尿病组和阿卡波糖组的代谢轮廓有显著差别, 根据PLS-DA载荷图筛选, 将对各组分离贡献大的化合物的串联质谱分析数据经Human Metabolome Database(HMDB)和Mass Bank.jp等数据库检索, 进行质谱信息匹配, 鉴定出苯乙酰甘氨酸、 肌酐及葡萄糖酸等8种内源性代谢物为潜在生物标记物.     

电喷雾质谱结合化学计量学方法快速筛选玛咖促睾丸间质细胞增殖活性成分

应用电喷雾串联质谱法( ESI-MS/MS)对云南和秘鲁产玛咖样品中低极性化学成分进行检测,获得各样品的一级质谱指纹谱图和各离子峰的二级质谱数据,并测定各样品促进大鼠睾丸间质细胞增殖活性,采用化学计量学方法主成分分析( PCA)、偏最小二乘法( PLS)和灰色关联度分析( GRA)对所获得的一级质谱数据进行处理后,可有效区分玛咖的不同产地,且筛选出其中具有促大鼠睾丸间质细胞增殖活性的可能成分,进而通过二级质谱数据分析得到可能活性成分的结构。实验结果表明,玛咖低极性成分具有很好的促大鼠睾丸间质细胞增殖活性,其中活性较强的主要为N-benzylhexadecanamide和N-benzy-(9Z,12Z,15Z)-octadecatrien-amide。本方法为简单、快速分析中药中促睾丸间质细胞增殖活性成分筛选方法提供了借鉴。     

顶空固相微萃取-全二维气相色谱/飞行时间质谱法分析闽南乌龙茶中的挥发性成分及其在分类中的应用

采用顶空固相微萃取(HS-SPME)结合全二维气相色谱/飞行时间质谱(GC×GC-TOF MS)分析了闽南乌龙茶中的挥发性成分。从48份不同等级和产季的乌龙茶(铁观音、黄金桂、本山、毛蟹和梅占)中获得了2000余种挥发性化合物,经筛选得到51种共有组分,并结合质谱数据库、保留指数与结构谱图等进行了初步鉴定。在此基础上采用主成分分析法(PCA)获得得分投影图,直观给出了不同样品的分类趋势。通过逐步判别获得9种对分类结果有显著影响的组分,并以此为变量通过Fisher判别法(FDA)建立了4个判别函数,对样品的分类准确率达到97.9%。本试验证实了以挥发性成分识别闽南乌龙茶的可行性。     

液相色谱-质谱联用系统在肝硬化不同阶段代谢轮廓研究中的应用

采用高效液相色谱-轨道离子阱质谱联用(HPLC-LTQ Orbitrap XL MS)代谢组学研究平台分析不同阶段肝硬化病人和健康人群的血清标本,获取代谢轮廓。采用模式识别方法结合非参数检验对数据进行分析。研究发现,由肝硬化A级组、B级组、C级组和健康对照组的代谢轮廓构建的正交偏最小二乘判别分析(OPLS-DA)模型(R2（Y）=90.1%, Q2=66.7%),对检测组数据的预测准确率达到93.8%,具有很好的判别能力。从代谢轮廓中可以鉴别出用于区分不同疾病阶段的特异性代谢标志物,如溶血磷脂酰胆碱、甘氨鹅去氧胆酸、半胱氨酸、甘氨酸、氨基己二酸、哌可酸等。研究结果表明: 利用代谢组学方法获得的血清代谢轮廓可以用来构建区分模型和寻找代谢标志物,为乙肝肝硬化的诊断和监测提供支持和依据。     

粒子群最小二乘支持向量机结合偏最小二乘法用于芝麻油质量的鉴别

结合粒子群最小二乘支持向量机(PSO-LSSVM)与偏最小二乘法(PLS)提出一种基于气相色谱技术的新方法,对芝麻油进行真伪鉴别,并对掺伪品中掺假比例进行定量分析。采用主成分分析法(PCA)对857个样本的脂肪酸色谱数据进行分析,优选主成分作为最小二乘支持向量机(LSSVM)的输入向量。利用粒子群算法(PSO)优化LSSVM,构建芝麻油掺伪鉴别的两级分类模型,同时运用PLS建立掺伪芝麻油中掺伪油脂的定量校正模型,两级分类模型的准确率分别达到了100%和98.7%,定量分析模型的平均预测标准偏差(RMSEP)为3.91%。结果表明,本方法的鉴别准确性和模型泛化能力均优于经典的BP神经网络和支持向量机(SVM),可用于食用油脂加工和流通环节的质量控制,为食用油质量的准确鉴定提供了一条有效途径。     

Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development

The application of liquid chromatography/mass spectrometry (LC/MS) followed by principal components analysis (PCA) has been successfully applied to the screening of rat urine following the administration of three candidate pharmaceuticals. With this methodology it was possible to differentiate the control samples from the dosed samples and to identify the components of the mass spectrum responsible for the separation. These data clearly show that LC/MS is a viable alternative, or complementary, technique to proton NMR for metabonomics applications in drug discovery and development.     

t检验法用于评价中药色谱指纹图谱的相似度

本文采用t检验法评价中药色谱指纹图谱的相似度。利用来自六个不同厂家的三黄片样品的高效液相色谱指纹图谱数据来验证t检验法的适用性,并用主成分分析(PCA)进一步验证方法的可行性。在PCA的聚类图上,t检验无显著性差异的样品聚为一类,t检验有显著性差异的样品各为一类。结果表明:用t检验法能客观地反映中药样品间的相似性和差异性。     

三波长融合指纹图谱结合6组分定量和主成分分析评价银翘解毒片的质量

建立了三波长融合高效液相色谱指纹图谱,并结合6组分定量和主成分分析(PCA)评价25批银翘解毒片的质量一致性。采用反相高效液相色谱法(RP-HPLC)分别于230、279、327 nm下检测。运用多波长融合指纹图谱技术建立银翘解毒片三波长融合指纹图谱,采用系统指纹定量法对其进行定性和整体定量评价。结果鉴别出25批银翘解毒片样品完全合格且区分良好。同时测定6组分含量,与指纹图谱结合,从整体和局部角度评价银翘解毒片质量。此外,运用PCA法对融合指纹图谱进行分析,通过主成分得分图可以明显区分来自两个厂家的25批银翘解毒片样品。方法综合性较强且有效,为科学评价与有效控制银翘解毒片的质量提供了可靠的参考。     

Peak purity determination with principal component analysis of high-performance liquid chromatography-diode array detection data

A method is proposed for the determination of chromatographic peak purity by means of principal component analysis (PCA) of high-performance liquid chromatography with diode array detection (HPLC-DAD) data. The method is exemplified with analysis of binary mixtures of lidocaine and prilocaine with different levels of separation. Lidocaine and prilocaine have very similar spectra and the chromatograms used had substantial peak overlap. The samples analysed contained a constant amount of lidocaine and a minor amount of prilocaine (0.02-2 conc.%) and hence the focus was on determining the purity of the lidocaine peak in the presence of much smaller levels of prilocaine. The peak purity determination was made by examination of relative observation residuals, scores and loadings from the PCA decomposition of DAD data over a chromatographic peak. As a reference method, the functions for peak purity analysis in the chromatographic data system used (Chromeleon) were applied. The PCA method showed good results at the same level as the detection limit of baseline-separated prilocaine, outperforming the methods in Chromeleon by a factor of ten. There is a discussion of the interpretation of the result, with some comparisons with evolving factor analysis (EFA). The main advantage of the PCA method for determination of peak purity over methods like EFA lies in its simplicity, short time of calculation and ease of use.     

Discrimination of Polygoni Multiflori radix and Cynanchi Auriculati radix using ultra‐high performance liquid chromatography fingerprints and chemical pattern recognition

In this work, a strategy was proposed to discriminate Polygoni Multiflori Radix (PMR) and its adulterant (Cynanchi Auriculati Radix, CAR). Ultra‐high performance liquid chromatography (UHPLC) fingerprints were established to analyze samples containing PMR, CAR and mixtures simultaneously. Multivariate classification methods were applied to analyze the obtained UHPLC fingerprints, including principal component analysis (PCA), partial least square discriminant analysis (PLS‐DA), soft independent modeling of class analogy (SIMCA), support vector machine discriminant analysis (SVMDA) and counter‐propagation artificial neural network (CP‐ANN). A plot of PCA score showed that PMR and CAR samples belonged to separate clusters (PMR class and CAR class), and samples of mixtures were located near PMR or CAR classes. Analysis by PLS‐DA, SVMDA and CP‐ANN performed well for recognition and prediction in terms of PMR and CAR samples. Moreover, the PLS‐DA method performed best in the detection of adulterated samples, even if the adulterant was about 25%.     

One- and two-dimensional gas chromatography–mass spectrometry and high performance liquid chromatography–diode-array detector fingerprints of complex substances: A comparison of classification performance of similar,complex Rhizoma Curcumae samples with the aid of chemometrics

Many complex natural or synthetic products are analysed either by the GC–MS (gas chromatography–mass spectrometry) or HPLC–DAD (high performance liquid chromatography–diode-array detector) technique, each of which produces a one-dimensional fingerprint for a given sample. This may be used for classification of different batches of a product. GC–MS and HPLC–DAD analyses of complex, similar substances represented by the three common types of the TCM (traditional Chinese medicine), Rhizoma Curcumae were analysed in the form of one- and two-dimensional matrices firstly with the use of PCA (Principal component analysis), which showed a reasonable separation of the samples for each technique. However, the separation patterns were rather different for each analytical method, and PCA of the combined data matrix showed improved discrimination of the three types of object; close associations between the GC–MS and HPLC–DAD variables were observed. LDA (linear discriminant analysis), BP-ANN (back propagation-artificial neural networks) and LS-SVM (least squares-support vector machine) chemometrics methods were then applied to classify the training and prediction sets. For one-dimensional matrices, all training models indicated that several samples would be misclassified; the same was observed for each prediction set. However, by comparison, in the analysis of the combined matrix, all models gave 100% classification with the training set, and the LS-SVM calibration also produced a 100% result for prediction, with the BP-ANN calibration closely behind. This has important implications for comparing complex substances such as the TCMs because clearly the one-dimensional data matrices alone produce inferior results for training and prediction as compared to the combined data matrix models. Thus, product samples may be misclassified with the use of the one-dimensional data because of insufficient information.     

蟾酥急性毒性的代谢组学研究

本文中,我们运用代谢组学方法结合心电图分析对蟾酥导致的大鼠急性毒性进行了研究,通过超高效液相色谱-飞行时间质谱建立了大鼠血清的代谢指纹谱,采用主成分分析和正交偏最小二乘法判别分析法分析了对照组和各给药组之间的代谢物谱差异,通过变量重要性投影和T检验选取潜在的生物标志物,结合质谱同位素分析、数据库检索以及标准品对潜在生物标志物进行了鉴定。结果表明,蟾酥可导致心脏心率减慢、心律失常、甚至出现心肌梗塞现象,其导致心脏损伤的原因可能是通过阻碍自由脂肪酸再酰化或激活蛋白激酶通路干扰了脂质代谢,这对于阐述蟾酥毒性作用机理提供了新思路。     

Forensic classification of ballpoint pen inks using high performance liquid chromatography and infrared spectroscopy with principal components analysis and linear discriminant analysis

Several varieties of blue ballpoint pen inks were analyzed by high performance liquid chromatography (HPLC) and infrared spectroscopy (IR). The chromatographic data extracted at four wavelengths (254, 279, 370 and 400 nm) was analyzed individually and at a combination of these wavelengths by the soft independent modeling of class analogies (SIMCA) technique using principal components analysis (PCA) to estimate the separation between the pen samples. Linear discriminant analysis (LDA) measured the probability with which an observation could be assigned to a pen class. The best resolution was obtained by HPLC using data from all four wavelengths together, differentiating 96.4% pen pairs successfully using PCA and 97.9% pen samples by LDA. PCA separated 60.7% of the pen pairs and LDA provided a correct classification of 62.5% of the pens analyzed by IR. The results of this study indicate that HPLC coupled with chemometrics provided a better discrimination of ballpoint pen inks compared to IR. The need to develop a suitable IR method for analysing blue ballpoint pen inks has been emphasized and it is hoped that the development of such a method would indeed provide a valuable tool for the non-destructive analysis of blue ballpoint pen ink samples for forensic purposes.     

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.     

Rime samples characterization and comparison using classical and fuzzy principal components analysis

The main objective of this paper is to introduce principal component analysis and two robust fuzzy principal component algorithms as useful tools in characterizing and comparing rime samples collected in different locations in Poland (2004–2007). The efficiency of the applied procedures was illustrated on a data set containing 108 rime samples and concentration of anions, cations, HCHO, as well as pH and conductivity. The fuzzy principal component algorithms achieved better results mainly because they are more compressible than classical PCA and very robust to outliers. For example, a three component model, fuzzy principal component analysis-first component (FPCA-1) accounts for 62.37% of the total variance and fuzzy principal component analysis-orthogonal (FPCA-o) 90.11%; PCA accounts only for 58.30%. The first two principal components explain 51.41% of the total variance in the case of FPCA-1 and 79.59% in the case of FPCA-o as compared to only 47.55% for PCA. As a direct consequence, PCA showed only a partial differentiation of rime samples onto the plane or in the space described by different combination of two or three principal components, whereas a much sharper differentiation of the samples, regarding their origin and location, is observed when FPCAs are applied.