表面解吸常压化学电离质谱快速鉴别樟木制品

采用自行研制的表面解吸常压化学电离质谱(SDAPCI-MS),在无需样品预处理的情况下,对樟木制品及普通木材进行检测,在正离子模式及m/z 90～400范围内获得其化学指纹图谱,并通过主成分分析(PCA)方法对所获指纹谱图信息进行分析,进而对不同样品进行鉴别。结果表明,SDAPCI-MS能够对樟木表面多种特征化学成分(樟脑,香叶醇等)进行解吸电离,快速获得樟木的化学指纹谱图,并能够对目标组分做多级串联质谱鉴定。结合PCA方法,可对不同品质、不同种类的木材样品进行区分。结果表明,本方法灵敏度高,分析速度快(单个样品分析时间小于3 min),可望应用于珍贵木材快速无损分析及品质鉴定。     

表面解吸常压化学电离质谱快速鉴别硫磺熏蒸八角

采用自行研制的表面解吸常压化学电离质谱(DAPCI-MS),无需样品预处理,对硫磺熏蒸八角和未熏八角直接进行正、负离子模式检测,获得其化学指纹图谱,并通过主成分分析(PCA)及聚类分析(CA)方法对所获指纹谱图信息进行分析,进而对不同样品进行鉴别。结果表明,在正、负离子模式下,DAPCI-MS都可对八角表面多种特征化学成分进行分析,快速获得八角的化学指纹谱图,并能够对目标组分进行多级串联质谱鉴定,结合PCA及CA方法可对八角是否经硫磺熏蒸进行快速鉴别。本方法无需样品预处理,灵敏度高,分析速度快,无污染,可望应用于市场上硫熏制品的快速鉴别。     

基于表面解吸常压化学电离质谱法快速评价咖啡种子活力

为快速、无损地区分不同活力的咖啡种子,采用自行研制的表面解吸常压化学电离质谱(DAPCI-MS),在无需样品预处理的前提下,获得咖啡种子表面的化学指纹图谱,并分别进行了主成分分析(PCA)、聚类分析(CA)和判别分析(DA),获得不同活力的咖啡种子样品的质谱信息特征.结果表明,在正离子模式下,DAPCI-MS结合多变量分析方法能有效区分不同活力的咖啡种子.PCA提取了3个主成分,累计贡献率达到92.2％;CA可以将相同活力的咖啡种子聚在一起,准确率为100％;DA对训练样本的回判正确率为100％,交叉验证分析成功率为100％,对外部验证样本进行DA,正确率95.7％.本方法具有无需样品预处理,分析速度快,灵敏度高,对种子无损伤等优点,能为其它种子活力测定提供参考.     

膜萃取电喷雾电离质谱快速分析菌血血样的研究

采用自行研制的膜萃取电喷雾电离质谱(MEESI-MS),将大肠杆菌、金黄色葡萄球菌、肺炎克雷伯氏菌和铜绿假单胞菌4种细菌通过血液培养后,正、负离子模式下进行质谱检测,获得其化学指纹图谱,通过主成分分析(PCA)方法对所获指纹谱图信息进行分析,并对样品进行分类。结果表明,MEESI-MS可快速获取菌血血样化学指纹谱图,结合PCA方法对菌血血样中细菌种类进行快速鉴别,菌血样品培养时间约60 min,整体分析时间少于65 min。本方法样品预处理简单、操作简便、分析速度快,可望用于临床菌血症和败血症的细菌种类的快速鉴别。     

表面解吸常压化学电离质谱快速分析六味地黄丸

采用新型表面解吸常压化学电离(Surface Desorption Atmospheric Pressure Chemical Ionization, SDAPCI)质谱法, 在敞开环境下, 对潮湿的空气进行电晕放电产生试剂离子, 进而在六味地黄丸表面发生解吸电离过程, 在无需复杂预处理的前提下对六味地黄丸中的待测物进行离子化, 从而获得了六味地黄丸在正负离子模式下的化学指纹图谱, 并利用主成分分析法对质谱指纹数据进行处理, 可对6个厂家生产的多个批次产品进行较好的区分. 结果表明, SDAPCI-MS技术能够快速测定六味地黄丸的剂型和生产厂家信息, 并能够对目标组分做多级串联质谱鉴定, 发现痕量目标组分. 研究方法可望应用于中成药药品生产质量控制和成品检测等领域.     

慢性乙型肝炎患者呼出气体的电喷雾萃取电离质谱分析

应用电喷雾萃取电离质谱法对慢性乙型肝炎患者(41例)及正常人(34例)的呼出气体样本进行检测,迅速获得其一级质谱指纹谱图,采用化学计量学方法主成分分析(PCA)和聚类分析(CA)对所获得的一级质谱数据进行处理后,可有效区分患者与正常人群,且不同临床类型慢性乙肝聚类分析图谱有不同表现,此结果有助于慢性乙型肝炎的临床诊断和病情分析.另外,本方法也为发展简单、快速、非侵入性慢性乙型肝炎临床诊断方法提供解决思路.     

嗜酸性氧化亚铁硫杆菌代谢产物的常压化学电离质谱分析

本研究以721矿和745矿嗜酸性氧化亚铁硫杆菌为研究对象,采用常压化学电离质谱直接分析其代谢产物,分别考察了顶空采样( Headspace sampling)、界面采样( Interface sampling)和中性解吸采样( Neutral desorption sampling)3种进样方式对电离效果的影响。在优化条件下,常压化学电离质谱对微生物纯菌种和混合菌种的代谢产物均具有良好的分析能力,可根据获得的代谢产物指纹谱图结合主成分分析( PCA)方法和聚类分析( CA)方法区分2个放射性强弱不同区域共4类嗜酸性微生物样品,并对主要胺类、酯类等代谢成分进行串联质谱鉴定,为耐辐射微生物的相关研究提供了一种可借鉴的分析方法。     

表面解吸常压化学电离质谱法直接分析牙齿微区表面

采用纳升取样表面解吸常压化学电离质谱法(nano-SDAPCI-MS)结合主成分分析(PCA),建立了一种采用具有微米级针尖的金属取样针直接对龋齿不同部位取样并进行快速质谱分析的方法.数据分析结果表明,同一颗龋齿不同部位的质谱指纹谱图之间存在差异;在不需要样品预处理的前提下通过串联质谱快速测定了龋齿中的乳酸、丙酮酸、苯乙酸和丙酸等成分.采用PCA方法可较好地将龋齿病灶位置与邻近正常组织进行区分,也可对不同牙病及健康牙齿进行区分.本方法可方便地对牙齿进行直接微区分析,为鉴别牙齿疾病及观测治疗效果提供了一种快速、简单的方法,为生物体中微细部位的快速取样及直接质谱分析提供了一种可能的解决方案.     

基于质子转移反应-飞行时间质谱快速鉴别不同产地闽北水仙茶

采用质子转移反应-飞行时间质谱仪(PTR-TOF-MS), 构建了3个产地(武夷山、建阳、建瓯)113个闽北水仙茶样品香气的化学指纹图谱, 对所得的闽北水仙茶香气指纹图谱进行主成分分析(PCA), 获得了不同产地闽北水仙茶样品的质谱信息特征, 然后采用软独立建模分类法(SIMCA)、K最邻近结点算法(KNN)、偏最小二乘判别分析法(PLS-DA)对闽北水仙茶的质谱信息进行了模式识别.结果表明, PTR-TOF-MS结合分类识别模式能有效区分不同产地的闽北水仙茶.PCA 提取了3个主成分, 累计贡献率为84.66%;3个识别模型的校正集判别正确率分别为89.38%、100.00%和100.00%, 预测集的判别正确率分别为83.18%、 96.46%和95.57%.基于此成功建立了不同产地的闽北水仙茶识别模型.本方法无需样品预处理、分析速度快、灵敏度高、对茶叶无损伤, 为茶叶产地溯源提供了新方法.     

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。     

Differentiation of human kidney stones induced by melamine and uric acid using surface desorption atmospheric pressure chemical ionization mass spectrometry

Clinically obtained human kidney stones of different pathogenesis were dissolved in acetic acid/methanol solutions and then rapidly analyzed by surface desorption atmospheric pressure chemical ionization mass spectrometry (SDAPCI-MS) without any desalination treatment. The mass spectral fingerprints of six groups of kidney stone samples were rapidly recorded in the mass range of m/z 50-400. A set of ten melamine-induced kidney stone samples and nine uric acid derived kidney stone samples were successfully differentiated from other groups by principal component analysis of SDAPCI-MS fingerprints upon positive-ion detection mode. In contrast, the mass spectra recorded using negative-ion detection mode did not give enough information to differentiate those stone samples. The results showed that in addition to the melamine, the chemical compounds enwrapped in the melamine-induced kidney stone samples differed from other kidney stone samples, providing useful hints for studying on the formation mechanisms of melamine-induced kidney stones. This study also provides useful information on establishing a MS-based platform for rapid analysis of the melamine-induced human kidney stones at molecular levels.     

主成分-线性判别法对大气易挥发性有机化合物的预警

应用遥感傅里叶变换红外光谱,采用主成分提取-线性判别分析(PCA-LDA)技术,对丙酮、二氯甲烷、甲苯、苯、氯仿和甲醇等六组分的任意混合体系进行定性鉴别。被选用的这6种大气有毒有机化合物的红外光谱图相互间存在着严重的混叠,并和反向传播人工神经网络(BP-ANN)的预测结果进行了比较。PCA-LDA的鉴别判对率达92.2%,识别率94.4%,误判率7.8%;BP-ANN分别为91.1%、95.6%和8.9%。结果表明PCA处理克服了LDA对多变量数据预测的局限性,预测性能和BP-ANN相当。鉴于BP-ANN计算耗时和繁琐,PCA-LDA模型被确定为建立VOCs预警模型最适当的方法。     

Quality assessment of cortex cinnamomi by HPLC chemical fingerprint, principle component analysis and cluster analysis

HPLC fingerprint analysis, principle component analysis (PCA), and cluster analysis were introduced for quality assessment of Cortex cinnamomi (CC). The fingerprint of CC was developed and validated by analyzing 30 samples of CC from different species and geographic locations. Seventeen chromatographic peaks were selected as characteristic peaks and their relative peak areas (RPA) were calculated for quantitative expression of the HPLC fingerprints. The correlation coefficients of similarity in chromatograms were higher than 0.95 for the same species while much lower than 0.6 for different species. Besides, two principal components (PCs) have been extracted by PCA. PC1 separated Cinnamomum cassia from other species, capturing 56.75% of variance while PC2 contributed for their further separation, capturing 19.08% variance. The scores of the samples showed that the samples could be clustered reasonably into different groups corresponding to different species and different regions. The scores and loading plots together revealed different chemical properties of each group clearly. The cluster analysis confirmed the results of PCA analysis. Therefore, HPLC fingerprint in combination with chemometric techniques provide a very flexible and reliable method for quality assessment of traditional Chinese medicines.     

基于粒子群算法的最小二乘支持向量机在红花提取液近红外定量分析中的应用

提出一种基于粒子群算法的最小二乘支持向量机(PSO-LS-SVM)方法,用于建立红花提取过程关键质控指标的定量分析模型.近红外光谱数据经波段选择、预处理和主成分分析(降维)后,利用粒子群优化(PSO)算法对最小二乘支持向量机算法中的参数进行优化,然后使用最优参数建立固含量和羟基红花黄色素A(HSYA)浓度的定量校正模型.将校正结果与偏最小二乘法回归(PLSR)和BP神经网络(BP-ANN)比较,并将所建的3个模型用于红花提取过程未知样本的预测.结果表明,BP-ANN校正结果优于PSO-LS-SVM和PLSR,但是对验证集和未知样品集的预测能力较差,而PSO-LS-SVM和PLSR模型的校正、验证结果相近,相关系数均大于0.987,RMSEC和RMSEP值相近且小于0.074,RPD值均大于6.26,RSEP均小于5.70％.对于未知样品集,pSO-LS-SVM模型的RPD值大于8.06,RMSEP和RSEP值分别小于0.07％和5.84％,较BP-ANN和PLSR模型更低.本研究所建立的PSO-LS-SVM模型表现出较好的模型稳定性和预测精度,具有一定的实践意义和应用价值,可推广用于红花提取过程的近红外光谱定量分析.     

基于液体阵列味觉仿生传感器鉴别白酒香型的新方法

通过模拟哺乳动物的味觉系统, 建立了交叉响应的液体阵列传感器, 为鉴别白酒香型提供了新方法. 选用7种染料和1种卟啉化合物作为传感单元, 构建液体阵列传感器, 集合8个传感单元的光谱响应信号构成分析物的指纹图谱, 达到识别的目的. 使用96孔板酶标仪采集响应数据, 结合主成分分析(PCA)、分层聚类分析(HCA)和判别分析(LDA)等模式识别方法进行数据处理, 对9种具有代表性的不同香型白酒样品进行了鉴别分析. PCA结果表明, 该方法对于白酒的检测主要基于酒体微量成分, 其中酸类物质对识别的贡献最大(贡献率达54.3%), 芳香类物质贡献率为18.6%; 同时, 仅用63.4%的数据信息量即可对白酒香型进行区分. HCA结果表明, 平行样均正确归类, 各白酒之间的相似程度在聚类图上得到体现. LDA结果表明, 该阵列对于9种白酒样品香型识别的准确率达到100%.     

发酵虫草菌粉及产品指纹图谱建立及多指标成分分析

《中国药典》收载的发酵虫草菌粉产品的质量标准中,规定以鸟苷、腺苷、尿苷的含量作为评价相关产品质量的标准.但除此之外,还有许多其他的核苷类成分对发酵虫草菌粉质量控制的影响尚未被探讨.为探究发酵虫草菌粉及产品质控指标选择的合理性,采用超高效液相色谱-紫外检测法对19批发酵虫草菌粉及产品中9种核苷成分(尿嘧啶、胞苷、鸟嘌呤、...     

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.     

Mass spectrometry imaging of latent fingerprints using titanium oxide development powder as an existing matrix

Recent research has focused on increasing the evidentiary value of latent fingerprints through chemical analysis. Although researchers have optimized the use of organic and metal matrices for matrix‐assisted laser desorption/ionization‐mass spectrometry imaging (MALDI‐MSI) of latent fingerprints, the use of development powders as matrices has not been fully investigated. Carbon forensic powder (CFP), a common nonporous development technique, was shown to be an efficient one‐step matrix; however, a high‐resolution mass spectrometer was required in the low mass range due to carbon clusters. Titanium oxide (TiO₂) is another commonly used development powder, especially for dark nonporous surfaces. Here, forensic TiO₂ powder is utilized as a single‐step development and matrix technique for chemical imaging of latent fingerprints without the requirement of a high‐resolution mass spectrometer. All studied compounds were successfully detected when TiO₂ was used as the matrix in positive mode, although, generally, the overall ion signals were lower than the previously studied CFP. TiO₂ provided quality mass spectrometry (MS) images of endogenous and exogenous latent fingerprint compounds. The subsequent addition of traditional matrices on top of the TiO₂ powder was ineffective for universal detection of latent fingerprint compounds. Forensic TiO₂ development powder works as an efficient single‐step development and matrix technique for MALDI‐MSI analysis of latent fingerprints in positive mode and does not require a high‐resolution mass spectrometer for analysis.     

Characterization and classification of medicinal plants according to their antioxidant profile estimated by thin layer chromatography assisted by chemometric expertise

This study focuses on the characterization and classification of 42 medicinal plants extracts according to their antioxidant activity. Principal component analysis (PCA), cluster analysis (CA) and the combination of PCA with linear discriminant analysis (PCA-LDA) were used as multivariate exploratory techniques for chromatographic data analysis. For the separation of the compounds a mobile phase containing ethyl acetate: toluene: formic acid: water (30:1.5:4:3 v/v/v/v) and different HPTLC plates (Silica gel 60 and HPTLC Silica gel 60?F₂₅₄) were used. The chromatographic plates were evaluated using the images obtained after spraying the plates with 2-aminoethyldiphenylborate solution (NTS, 0.2% in ethanol) and also after their reaction with 2, 2-diphenyl-1-picrylhydrazyl solution (DPPH^?) (0.2% in ethanol). The score projection on the plane defined by first two components (PC1 and PC2) revealed two large groups of the investigated samples depicted according to their antioxidant capacity. A better classification of samples according to their antioxidant capacity was obtained using the CA and PCA-LDA methodology in all cases. The excellent results obtained in this study concerning the classification of medicinal plants according to their antioxidant capacity using the PCA-LDA methodology applied to TLC chromatograms might lead to a new paradigm in the field of medicinal plant holistic evaluation.