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多级质谱串联在各个领域都有广泛应用。双线形离子阱的小型质谱可以实现类似传统三重四极杆质谱仪的串联质谱分析功能,而在此过程中,双阱间的离子传输为重要的仪器功能。在已发表的双线形离子阱工作中,对阱间离子传输,尤其是质量选择性传输鲜有系统的研究。本工作研究了离子阱q值、阱内气压、辅助性交流电(AC)的强度、辅助性AC的作用时长等因素对传输的目标离子强度的影响,优化了离子传输条件,如q1=q2=0.3,阱内气压为0.37 Pa,AC强度为350 mV,离子传输时长大于10 ms等。该结果对小型质谱双线形离子阱的自主研发和提升阱间离子传输效率具有指导作用。 相似文献
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液相色谱-串联质谱分离鉴定树莓叶中黄酮类化合物 总被引:3,自引:0,他引:3
树莓叶作为一种新的黄酮资源,为了分析鉴定其黄酮类化合物的结构,采用高效液相色谱-二极管阵列-离子阱多级质谱(HPLC-PDA-MSn)联用技术分离鉴定树莓叶乙醇提取物中的黄酮类化合物。通过离子阱多级质谱提供的准分子离子峰和多级碎片离子信息,分析得到了黄酮类化合物的相对分子质量、黄酮糖苷的组成结构、黄酮糖苷中糖的类型和多糖链的连接次序等。结合液相色谱相对保留时间、标准品和相关文献对照,对树莓叶乙醇提取物中的15个黄酮类化合物的可能结构进行推断。结果表明,树莓叶中有8个槲皮素衍生化的黄酮化合物和7个山奈酚衍生化的黄酮化合物,这与树莓中主要含有槲皮素和山奈酚衍生化的黄酮化合物报道一致。 相似文献
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液相色谱-电喷雾质谱联用技术分析人参皂苷 总被引:11,自引:4,他引:11
应用液相色谱 -电喷雾质谱 ( L C-MS)联用技术 ,对 8种人参皂苷单体进行了探讨。发现在电喷雾负离子检测方式下 ,在离子阱和四极杆这两种不同质量分析器的质谱仪中 ,8种皂苷分子所产生的准分子离子峰均为 [M+ 45 ]-峰 ;并根据离子阱多级质谱扫描功能产生的碰撞诱导裂解 ( CID)谱 ,研究了该类化合物分子在电喷雾质谱、负离子检测方式下的裂解特征 ,有助于该类化合物的分子量确定和结构鉴定 ;同时建立了液相色谱 -质谱联用法 ,可用于该类化合物的定性分析 相似文献
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近十年来,国际上对子紫杉醇等具有6/8/6环骨架的紫杉烷类二萜化合物进行了一定的质谱研究,但对具有5/7/6环骨架化合物的MS/MS研究报道甚少。本文在前期工作的基础上[1-4],利用FAB-MS/MS方法,探讨了三个4,20-双键5/7/6环骨架化合物的裂解方式,以及取代基种类和位置对质谱裂解的影响,为该类化合物及其代谢物的结构解析提供了有力依据。这类化合物的结构如下:通过FAB-MS谱测定,发现化合物1和2的分子离子主要以[M+Na]+离子形式存在。而[M+Na]+离子的CID-MS/MS话… 相似文献
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建立了胶质瘤细胞样本中酰基肉碱类化合物的LC-MS/MS代谢轮廓分析方法。首先,采用Q-Exactive四极杆-轨道阱高分辨串联质谱的全扫描(Full MS Scan)和平行反应监测(PRM)模式对细胞样本中酰基肉碱进行定性分析。根据一级和二级高分辨质谱数据,并结合酰基肉碱类化合物的特征裂解规律,在U87MG胶质瘤细胞、胶质瘤干细胞样细胞和胶质瘤干细胞分化细胞3种干性不同的胶质瘤细胞样品中鉴定出17种酰基肉碱类化合物。采用Qtrap 5500四极杆-线性离子阱串联质谱的多反应监测(MRM)模式,建立了细胞样本中17种酰基肉碱化合物的代谢轮廓分析方法,并对比分析了3种胶质瘤细胞中酰基肉碱化合物的差异。结果表明,该方法的线性关系良好,线性相关系数大于0.99,准确度与精密度均符合要求。细胞样本中酰基肉碱的定量分析结果表明,与胶质瘤干细胞相比,胶质瘤干细胞分化细胞和胶质瘤细胞中的肉碱和酰基肉碱含量均明显上调。此研究可为细胞样本中酰基肉碱类化合物的代谢轮廓分析提供方法参考。 相似文献
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准确高效的离子解离技术对串联质谱分析方法具有非常重要的影响。四极离子阱可以存储离子,实现离子选择存储或逐出并进行多级质谱分析,适合各种解离技术在阱内的实施。由于生物质谱分析等研究工作对分子结构鉴定的需求,研究人员陆续开发了一系列阱内离子解离技术,推动了相关仪器与应用的发展。本文在离子阱几何结构和阱内离子运动规律的基础上,对是否依赖背景气体碰撞的两类阱内离子解离技术进行综述。其中,将依赖于背景气体碰撞的解离技术分为基于共振激发和非共振激发两类,归纳了每种解离技术的实施过程、解离特点及应用,并对各种解离技术存在的问题以及未来的研究方向进行总结。 相似文献
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随着离子阱质谱分析技术的广泛应用,离子阱共振激发过程的理论模拟和实验验证对于深入研究离子阱质谱性能具有重要的意义。常见的线性离子阱所使用的共振激发弹出电压ac(alternating current)的设置有两种方式:一种是设定1个很小的恒定值(如1 V0-p),另一种是设定为1个幅值扫描范围。然而,鲜有人对比研究这两种设定方式对离子阱质谱分辨率等相关性能的影响,也尚未有人将该实验结果用理论模拟的方式进行验证。本工作基于自行搭建的小型连续进样接口离子阱质谱平台,利用纳升电喷雾电离源,以利血平为研究对象,通过理论模拟和实验对照研究扫描共振激发对离子阱质谱性能的影响。首先,通过实验研究了恒定ac共振激发和扫描ac共振激发对离子阱质谱分辨率和灵敏度的影响,实验结果表明,ac电压的初始设定应高于一定的阈值,并且使用扫描共振ac激发有利于提高离子阱质谱的分辨率和灵敏度。随后,利用Simion离子光学证明了扫描共振激发ac相比于恒定共振激发ac可以防止阱中离子的提前激发。最后,对100 mg/L芬太尼和那可汀标准样品进行检测,结果表明,利用扫描ac共振激发相比于恒定ac共振激发可将分辨率提高2倍以上。本研究通过理论模拟和实验对照研究幅值扫描ac对离子阱质谱性能的影响,建立了离子阱质谱共振激发过程的理论模拟方法和相关程序,为进一步研究离子阱中更复杂的非线性共振等高阶运动奠定了基础,在一定程度上提升了离子阱质谱的分析性能,加速了离子阱质谱的仪器调试过程。 相似文献
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基于本征时间尺度分解算法的齿轮箱故障诊断 总被引:2,自引:0,他引:2
针对现有信号处理方法在齿轮箱故障诊断中的不足,将本征时间尺度分解算法( IntrinsicTime- scale Decomposition,ITD)应用到齿轮箱故障诊断中.首先介绍了ITD算法;然后将ITD算法应用到齿轮箱的故障诊断中,得到了正确的结论;最后将ITD算法与经验模式分解(EMD)算法进行了比较,结果表明... 相似文献
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在ITD方法[1]-[7]中,准确区分真假模态和精确识别模态参数是识别技术的两个关键问题。该法采用模态置信因子来区别真假模态[4][5],其准确度受测试数据中的噪声与测点个数的影响较严重。该法将最小二乘方程组化为法方程[4]来求解系统矩阵,增加了所求方程的病态性和不稳定性[8][9]。为此,本文进行了改进,借助于对不同序列频率值的比较来区别真假模态,当测点较少也有足够的可靠性,且测试噪声对频率的影响也最小。在算法上本文采用了改进的Gram-Schmidt正交化方法来求解识别计算中的最小二乘方程组,使识别精度有了提高。本文用FORTRAN77语言编制了识别程序,进行了若干试算,结果令人满意。 相似文献
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针对强背景噪声下滚动轴承振动信号故障特征信息难以提取的问题,提出了结合固有时间尺度分解(ITD)-形态滤波和Teager能量谱的滚动轴承故障特征提取与诊断方法。首先对滚动轴承振动信号采用ITD方法分解,得到若干个固有旋转分量;考虑到噪声主要分布在高频段,取前2个高频的固有旋转分量进行形态滤波,并将滤波后的信号与剩余固有旋转分量重构;对重构信号计算Teager能量算子并绘制Teager能量谱,从Teager能量谱中可以识别出故障特征。将本方法应用于滚动轴承的内圈故障和外圈故障诊断,结果表明ITD-形态滤波可以有效去除振动信号中的背景噪声并保留冲击特征,Teager能量谱可以直观并准确显示出故障特征。 相似文献
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《Measurement》2014
Targeting that the measured vibration signal of roller bearing contains the characteristics of non-stationary and nonlinear, and the extraction features may contain smaller correlation and redundancy characteristics in the roller bearing fault diagnosis, the vibration signal processing method based upon improved ITD (intrinsic time-scale decomposition) and feature selection method based on Wrapper mode are put forward. In addition, in the design of the classifier, targeting the limitation of existing pattern recognition method, a new pattern recognition method-variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification. However, the parameters are fitted by using least squares in VPMCD method, while least squares regression is sensitive to “abnormal value”. Therefore, a robust regression-variable predictive mode-based class discriminate (RRVPMCD) method is proposed in this paper, robust regression is adopted to estimate parameters and the effect of “abnormal value” in the estimation of parameters would be reduced by giving each feature a weight. Firstly, improved ITD method and feature selection method based on Wrapper mode are combined to extract the fault features of roller bearing vibration signals, and feature vector matrixes are established, then a predictive model is built through the method of RRVPMCD, finally, the established predictive model is used for pattern recognition. Experimental results show that the model based on the improved ITD, the Wrapper feature selection and RRVPMCD method can effectively identify work status and fault type of roller bearing. 相似文献
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Blind source separation based vibration mode identification 总被引:1,自引:0,他引:1
In this paper, a novel method for linear normal mode (LNM) identification based on blind source separation (BSS) is introduced. Modal coordinates are considered as a specific case of sources that have certain time structure. This structure makes modal coordinates identifiable by many BSS algorithms. However, algorithms based on second-order statistics are particularly suited for extracting LNMs of a vibration system. Two well-known BSS algorithms are considered. First, algorithm for multiple unknown signals extraction (AMUSE) is used to illustrate the similarity with Ibrahim time domain (ITD) modal identification method. Second, second order blind identification (SOBI) is used to demonstrate noise robustness of BSS-based mode shape extraction. Numerical simulations and experimental results from these BSS algorithms and ITD method are presented. 相似文献
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Zhanqiang Xing Jianfeng Qu Yi Chai Qiu Tang Yuming Zhou 《Journal of Mechanical Science and Technology》2017,31(2):545-553
The gear vibration signal is nonlinear and non-stationary, gear fault diagnosis under variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault diagnosis method based on Intrinsic time-scale decomposition (ITD)-Singular value decomposition (SVD) and Support vector machine (SVM) is proposed in this paper. The ITD method is adopted to decompose the vibration signal of gearbox into several Proper rotation components (PRCs). Subsequently, the singular value decomposition is proposed to obtain the singular value vectors of the proper rotation components and improve the robustness of feature extraction under variable conditions. Finally, the Support vector machine is applied to classify the fault type of gear. According to the experimental results, the performance of ITD-SVD exceeds those of the time-frequency analysis methods with EMD and WPT combined with SVD for feature extraction, and the classifier of SVM outperforms those for K-nearest neighbors (K-NN) and Back propagation (BP). Moreover, the proposed approach can accurately diagnose and identify different fault types of gear under variable conditions. 相似文献
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The identification of modal parameters from the response data only is studied for structural systems under nonstationary ambient
vibration. In a previous paper by the authors, the modal parameters of a system were identified using the correlation method
in conjunction with the curve-fitting technique. This was done by working within the assumption that the ambient excitation
is a nonstationary white noise in the form of a product model. In the present paper, the Ibrahim time-domain method (ITD)
is extended for modal-parameter identification from the nonstationary ambient response data without any additional treatment
of converting the original data into the form of free vibration. The ambient responses corresponding to various nonstationary
inputs can be approximately expressed as a sum of exponential functions. In effect, the ITD method can be used in conjunction
with the channel-expansion technique to identify the major modes of a structural system. To distinguish the structural modes
from the non-structural modes, the concept of mode -shape coherence and confidence factor is employed. Numerical simulations,
including one example of using the practical excitation data, confirm the validity and robustness of the proposed method for
identification of modal parameters from the nonstationary ambient response. 相似文献