基于核磁共振代谢组学技术比较不同产地铁棍山药的化学成分差异

该研究阐明了不同产地铁棍山药中化学成分的差异,为道地山药的产地鉴别提供了理论依据。利用核磁共振氢谱（1H NMR）对铁棍山药进行分析,指认、归属了图谱中的30种主要代谢物后,通过主成分分析（PCA）、正交偏最小二乘法判别分析（OPLS－DA）对7种不同产地铁棍山药中的化学成分进行了比较。结果显示,温县垆土山药中精氨酸的含量最高,蔗糖含量最低,孟州垆土山药中苏氨酸、谷氨酰胺的含量最高;温县沙土山药的丙氨酸和苹果酸含量最高,武陟沙土山药的尿囊素含量最高;沙土地的铁棍山药中2-羟基异丁酸的含量均高于垆土地山药。7种铁棍山药葡萄糖和果糖的变量重要性投影（VIP）均小于1.0,并不是主要成分。液相色谱检测蔗糖、柠檬酸、苹果酸的结果与OPLS－DA载荷图结果一致;7种铁棍山药的葡萄糖和果糖含量均低于液相色谱检出限（2.0 mg/g）,表明可以利用葡萄糖、果糖的含量和葡萄糖显色试剂鉴别铁棍山药是否为道地怀山药。     

一种基于最小二乘支持向量机算法的近红外光谱判别分析方法

将最小二乘支持向量机(LSSVM)用于近红外(NIR)光谱分析,建立一种新型的NIR光谱快速鉴别方法。以丹参药材道地性鉴别为例,对其NIR漫反射光谱进行主成分分析后,运用LSSVM法建立NIR光谱非线性分类模型,对丹参药材道地性进行快速鉴别。将本方法与经典SVM和BP神经网络法相比较,结果表明,本法判别准确率高,计算时间少,可推广应用于中药等天然产物质量快速鉴别。     

硝基漆品牌厂家红外光谱鉴别及预测模型建立

在法庭科学领域,硝基漆的检验鉴别是一项重要的工作。为提高检验工作效率,提高分析可靠性,提出一种基于中红外光谱结合化学计量学的硝基漆鉴别方法。实验采集并获取长颈鹿等6种品牌共计59个样本的红外光谱数据,借助主成分分析、K近邻模型和判别分析构建分类模型。结果表明,PCA模型和K近邻模型对59个样本的区分能力相对较弱,判别分析模型的区分能力较强,实现了96.6%的准确区分和归类,分类结果理想。该方法能够快速,准确,无损地鉴别硝基漆,可为其他物证的检验鉴别提供一定的借鉴和参考。     

基于红外光谱多元分析的记号笔种类鉴别研究

借助衰减全反射红外光谱结合K近邻算法和层次聚类,建立记号笔墨水种类鉴别的分类模型,为记号笔的种类鉴别提供有效的新方法。研究基于记号笔墨水的外光谱矩阵数据,通过建立主成分和判别分析分析模型和K近邻算法分类模型,实现对模型性能的比较和对模型分类结果的验证。实验结果表明,以水性和油性作为分类标准,模型对样本的区分能力好,其正确率为100%。借助红外谱图进一步分析水性油性样本时,其最强峰与其对应溶剂相符合。借助K近邻算法进行验证性分析,按重要性加权特征给不同的样本施加不同的权重,运用训练样本即为测试样本的方法交互验证,选取K值为1,训练集∶保持集=3∶1,建立分类模型,模型总分类准确率达100%,区分效果良好,不同品牌的油性、水性样本能被聚类为一组。综上,衰减全反射红外光谱结合K近邻算法和层次聚类可作为记号笔墨水种类鉴别的一种快速准确的分析方法。     

轮胎橡胶颗粒红外光谱的多元分类研究

交通肇事现场轮胎橡胶颗粒的检验鉴别是一项常见的工作。为降低检验成本,提高检验效率,提出一种基于红外光谱及其化学模式研究的轮胎橡胶颗粒鉴别方法。实验采集并获取朝阳等4种品牌共计46个样本的红外光谱数据,预处理采用自动基线校正、峰面积归一化、多元散射校正、Savitzky-Golay 7点平滑,借助主成分分析和判别分析构建分类模型。结果表明,46个样本主要由丁苯橡胶、顺丁橡胶和异戊橡胶三种类型,PCA模型对46个样本的区分能力相对较弱,判别分析模型则实现了100%的准确区分和归类,分类结果理想,该方法快速、无损、准确,具有一定的普适性和借鉴意义。     

中药道地性微量元素鉴定法的方法学探索

探讨和发展了用元素指纹谱法，特征参数和关联系数法鉴定中药道地性的方法，研究表明，如果选择合适的参数或对数据作适当预处理，利用微量元素鉴别中药是可能的，并介绍了这三种方法的特点。     

基于显微共聚焦拉曼技术对三种食源性致病菌的快速鉴别研究

采用显微共聚焦拉曼技术,建立了对三种常见食源性致病菌快速鉴别的检测方法。使用XploRA PLUS共聚焦拉曼光谱仪,在激光功率为5 mW、积分时间为30s、积分次数为1次的条件下,对德尔卑沙门氏菌、副溶血性弧菌和金黄色葡萄球菌进行了拉曼光谱数据的采集。对拉曼光谱采用多项式平滑算法和荧光背底扣除后,采用主成分分析法(PCA)对预处理后的数据进行降维,提取出前三个主成分的累计方差贡献率达到了95.4％,样本明显的聚为了3类。同时结合Fisher判别分析法(FLD)构建分类模型,对三种样本进行交叉验证,分类准确率达到了100％。结果表明,采用显微共聚焦拉曼技术与PCA-FLD方法结合可实现对三种食源性致病菌的快速准确鉴别且模型检测精度高,方法具有一定的实用性及参考价值。     

应用电感耦合等离子体质谱法结合化学计量学方法对野生和栽培鱼腥草的鉴别

选野生和栽培鱼腥草样本各25个,分别从每个样本称取0.500 0g,用5.0mL硝酸和2.0mL过氧化氢按预设程序进行微波消解,制得样品溶液25.0mL,按电感耦合等离子体质谱法(ICP-MS)测定其中22种无机元素,以45Sc、72 Ge、103 Rh、159 Tb、175 Lu和209 Bi为内标,用混合标准溶液制得标准曲线,各元素的线性关系良好。在已知样本的基础上,按标准加入法进行回收率和精密度试验,测得回收率为82.0%～114%,测定值的相对标准偏差(n=6)为0.20%～4.5%。对所测得数据进行独立样本t检验统计分析,结果表明:地理生长环境、种植技术和人类活动等因素造成了野生和栽培鱼腥草中无机元素含量的差异。对50个鱼腥草样本×22个元素变量数据矩阵(50×22)进行主成分分析,其结果基本反映了无机元素与野生和栽培鱼腥草之间的关系,但不能进行准确的鉴别。进一步对50×22个元素变量数据通过逐步判别分析,筛选出具有显著作用的变量元素(Na、K、Cr、Se、Cd),建立了典型判别函数和线性判别函数模型,达到了两种不同类型鱼腥草的完全区分。为验证判别模型的可靠性,进行了回代检验和交叉验证,得到正确判别率为100%。据此认为应用ICP-MS结合化学计量学方法可实现野生和栽培鱼腥草的鉴别。     

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。     

基于Bayes判别的手帕纸红外光谱鉴别

手帕纸是犯罪现场常见的物证之一,在法庭科学领域备受关注。为了实现对市场上手帕纸的快速分类鉴别的目的,本文采用了具有无损检验特点的傅里叶红外光谱,结合主成分分析(PCA)与Bayes判别对8种品牌96个手帕纸样本建立分类模型。结果表明,分别利用PCA和Bayes判别对样本进行分类的准确率并不理想,采用Bayes判别对PCA降维后的特征向量进行建模,分类准确率可达100%。采用傅里叶红外光谱结合Bayes判别实现了对手帕纸的精准分类,为实践中手帕纸的分类鉴别提供一定的参考和借鉴。     

Nondestructive discrimination of ivories and prediction of their specific gravity by Fourier-transform Raman spectroscopy and chemometrics

Fourier-transform (FF) Raman spectroscopy and chemometrics were used for nondestructive analysis of ivories. The discrimination of five kinds of ivories, two subspecies of African elephant, mammoth, hippopotamus, and sperm whale, was investigated, and a calibration model for predicting their specific gravity was developed. FT-Raman spectra were measured in situ for them and chemometrics analyses were carried out for the 3050-350 cm(-1) region. The five kinds of ivories were clearly discriminated from each other on the scores plots of two or three principal components (PCs) obtained by principal component analysis (PCA). The loadings plot for PC 1 shows that the discrimination relies on the content ratio of organic collagenous protein and inorganic hydroxyapatite of ivories. The loadings plot for PC 2 shows that bands due to the CH3 and CH2 stretching modes of the protein also play a role in the discrimination. Using partial least squares regression (PLSR), we developed a calibration model that predicts the specific gravity of the ivories from the FT-Raman spectra. The correlation coefficient and root mean square error of cross validation (RMSECV) of this model were 0.980 and 0.024, respectively.     

Non-destructive analysis of the two subspecies of African elephants, mammoth, hippopotamus, and sperm whale ivories by visible and short-wave near infrared spectroscopy and chemometrics

Visible (VIS) and short-wave near infrared (SW-NIR) spectroscopy was used for non-destructive analysis of ivories. VIS-SW-NIR (500-1000 nm) spectra were measured in situ for five kinds of ivories, that is two subspecies of African elephants, mammoth, hippopotamus, and sperm whale. Chemometrics analyses were carried out for the spectral data from 500 to 1000 nm region. The five kinds of ivories were clearly discriminated from each other on the scores plot of two principal components (PCs) obtained by principal component analysis (PCA). It was noteworthy that the ivories of the two subspecies of African elephants were discriminated by the scores of PC 1. The loadings plot for PC 1 showed that the discrimination relies on the intensity changes in bands due to collagenous proteins and water interacting with proteins. It was found that the scores plot of PC 2 is useful to distinguish between the ivories of the two subspecies of African elephants and the other ivories. We also developed a calibration model that predicted the specific gravity of five kinds of ivories from their VIS-SW-NIR spectral data using partial least squares (PLS)-1 regression. The correlation coefficient and root mean square error of cross validation (RMSECV) of this model were 0.960 and 0.037, respectively.     

Capillary electrophoretic profiling and pattern recognition analysis of urinary nucleosides from thyroid cancer patients

Metabolic profiling analysis by capillary electrophoresis (CE) was combined with pattern recognition methods to see some correlation between urinary nucleoside levels and thyroid cancer. A total of 15 nucleosides were identified in urines from 12 female thyroid cancer patients and 12 healthy females studied. From the scatter plot evaluation, inosine showed the highest estimated diagnostic power with ca. 97.725% confidence level, followed by N²-methylguanosine. Star symbol graphs showed differences in levels of both minor and major nucleosides between cancer and normal groups more efficiently, compared with histogram. The stepwise discriminant analysis (SDA) selected N²-methylguanosine, N²,N²-dimethylguanosine and 1-methylguanosine as the most discriminating variables between thyroid cancer and normal groups. The canonical discriminant analysis (CDA) correctly classified all urine specimens studied into two separate clusters of cancer and normal groups in a canonical plot. The principal component analysis (PCA) distinguished cancer patients from normal controls in a principal component plot. The cluster analysis (CA) yielded a dendrogram displaying group separation without any single wrong linkage.     

Live single-cell video-mass spectrometry for cellular and subcellular molecular detection and cell classification

The molecular content from the cytoplasm of a live, single mammalian cell and its organelle were trapped with a nano-electrospray ionization (ESI) tip acting as a micropipette under a video microscope, and hundreds of small molecular peaks were detected by direct nano-ESI mass spectrometry (MS). Granule- or cytoplasm-specific peaks in a mast cell (RBL 2H3) model were extracted by paired t-test to demonstrate their specific localization. Some of the typical and specific molecules were successfully identified by MS/MS analysis. This method was also applied to the cell classification of seven types of cell lines at the single-cellular level by principal component analysis (PCA), revealing seven clusters in the multivariate score plot.     

Determination of the Polar Compounds in Vegetable Oil by Ultra-Performance Liquid Chromatography–Quadrupole-Time-of-Flight-Mass Spectrometry with Chemometrics

High-throughput ultra-performance liquid chromatography–quadrupole time-of-flight mass spectrometry was combined with chemometric tools for the rapid determination of polar components in camellia oil, rapeseed oil, and waste cooking oil. The results were analyzed by two unsupervised methods: principal component analysis (one-way ANOVA, p<.05) and volcano plot analysis (p<.05, fold change ≥2) and supervised method: partial least squares discriminant analysis. The results showed that the oils were correctly classified based on their polar components. The first three principal components reflected most detail with a cumulative contribution rate of 84.67% using principal component analysis. The prediction accuracy was close to 100% using partial least squares discriminant analysis. Nineteen components were screened by principal component analysis; twelve were preliminary identified as palmitamide, phytosphingosine, eicosasphinganine, 1-monopalmitin, glyceryl monooleate, glyceryl monostearate, 1α-hydroxyvitamin D2, 1-linoleoyl glycerol, oleamide, sphinganine, stearamide, and linoleic acid. The proposed method may be applied to effectively and accurately authenticate edible oils.     

Distinguishing Ontario ginseng landraces and ginseng species using NMR-based metabolomics

The use of ¹H-NMR-based metabolomics to distinguish and identify unique markers of five Ontario ginseng (Panax quinquefolius L.) landraces and two ginseng species (P. quinquefolius and P. ginseng) was evaluated. Three landraces (2, 3, and 5) were distinguished from one another in the principal component analysis (PCA) scores plot. Further analysis was conducted and specific discriminating metabolites from the PCA loadings were determined. Landraces 3 and 5 were distinguishable on the basis of a decreased NMR intensity in the methyl ginsenoside region, indicating decreased overall ginsenoside levels. In addition, landrace 5 was separated by an increased amount of sucrose relative to the rest of the landraces. Landrace 2 was separated from the rest of the landraces by the increased level of ginsenoside R_b1. The Ontario P. quinquefolius was also compared with Asian P. ginseng by PCA, and clear separation between the two groups was detected in the PCA scores plot. The PCA loadings plot and a t-test NMR difference plot were able to identify an increased level of maltose and a decreased level of sucrose in the Asian ginseng compared with the Ontario ginseng. An overall decrease of ginsenoside content, especially ginsenoside R_b1, was also detected in the Asian ginseng’s metabolic profile. This study demonstrates the potential of NMR-based metabolomics as a powerful high-throughput technique in distinguishing various closely related ginseng landraces and its ability to identify metabolic differences from Ontario and Asian ginseng. The results from this study will allow better understanding for quality assessment, species authentication, and the potential for developing a fully automated method for quality control.

Direct analysis in real time mass spectrometry and multivariate data analysis: A novel approach to rapid identification of analytical markers for quality control of traditional Chinese medicine preparation

The paper presents a novel strategy to identify analytical markers of traditional Chinese medicine preparation (TCMP) rapidly via direct analysis in real time mass spectrometry (DART-MS). A commonly used TCMP, Danshen injection, was employed as a model. The optimal analysis conditions were achieved by measuring the contribution of various experimental parameters to the mass spectra. Salvianolic acids and saccharides were simultaneously determined within a single 1-min DART-MS run. Furthermore, spectra of Danshen injections supplied by five manufacturers were processed with principal component analysis (PCA). Obvious clustering was observed in the PCA score plot, and candidate markers were recognized from the contribution plots of PCA. The suitability of potential markers was then confirmed by contrasting with the results of traditional analysis methods. Using this strategy, fructose, glucose, sucrose, protocatechuic aldehyde and salvianolic acid A were rapidly identified as the markers of Danshen injections. The combination of DART-MS with PCA provides a reliable approach to the identification of analytical markers for quality control of TCMP.     

基于主成分分析的骨碎补药材乙醇和环己烷提取物的高效液相色谱指纹图谱及指标成分的定量分析

建立了骨碎补药材乙醇和环己烷提取物的高效液相色谱(HPLC)指纹图谱,并利用主成分分析法(PCA)对指纹图谱进行统计分析,以各主要色谱峰的保留时间和峰面积为变量得到score图和loading图。在score图和loading图中,骨碎补的正品和非正品可明显区分,且揭示出对此区分贡献最大的4个潜在指标成分,其中已知成分为柚皮苷、新北美圣草苷和E-4-O-β-D-葡萄糖酰咖啡酸。同时测定了这3种成分在19批正品和非正品骨碎补药材中的含量,其中10批骨碎补药材正品中3种成分的含量为: 柚皮苷6.36～10.1 mg/g,新北美圣草苷5.14～9.21 mg/g,E-4-O-β-D-葡萄糖酰咖啡酸1.87～3.19 mg/g。该方法更全面地反映了药材的化学成分信息,并能从定性和定量两方面控制骨碎补药材的内在质量。     

模式识别法分析5种植物油脂

通过模式识别方法区分花生油、大豆油、米糠油、棕榈油和菜籽油。采用气相色谱法分析5种植物油脂的脂肪酸,用面积归一化法计算每个植物油脂样品的各脂肪酸相对含量。以每个植物油脂中9个脂肪酸的相对含量为变量,采用SPSS13.0软件的模式识别技术对119个植物油脂样品进行区分。由主成分分析图可知,花生油、大豆油、米糠油、棕榈油和菜籽油被清晰地分为5组。判别分析建立的判别方程能较好地实现样品的判别,自身验证和交互验证的准确率均为100%。另取每种植物油脂各5个样品(共25个)进行验证,识别准确率为100%。对调和有棕榈油的花生油进行主成分分析,在主成分分析图上,调和油的分布点在花生油分布区域与棕榈油分布区域之间。     

Comparative study of characteristic compounds of three species of truffle

The Panxi area in Sichuan Province is the main area for the production of truffles in China, and several species of truffle are known to exist in this region. Nevertheless, it is unclear what the differences in chemical composition between the truffles are. Using an ultra-high-performance liquid chromatography quadrupole/orbitrap high-resolution mass spectrometry coupled with Compound Discoverer 3.0, we identified chemical components in three mainly known truffles from the Panxi region. Further analysis of chemical composition differences was conducted using principal component analysis, and orthogonal partial least squares discriminant analysis. Note that, 78.9% of the variance was uncovered by the principal component analysis model. As a result of the orthogonal partial least squares discriminant analysis model, the three species of truffles (Tuber pesudohimalayense, Tuber indicum, and Tuber sinense) from Panxi were better discriminated, with R²X, R²Y, and Q² being 0.821, 0.993, and 0.947, respectively. In this study, 87 components were identified. T. pesudohimalayense contained significantly higher levels of nine different compounds than the other two species. Hence, it was possible to identify similarities and differences between three species of truffles from Panxi in terms of chemical composition. This can be used as a basis for quality control.