We assessed the quality of each spectrum by using the Pearson correlation coefficient to compare the 11,876 intensity measures of each spectrum. Using the cut-off cri- teria we established of 1-mean > 0.2 for QC spectra and > 0.4 for specimen spectra, we obtained very similar results if we used peak intensities (less than 100 values per spec- trum) to generate the correlation matrix. Before the outlier spectra and batch effect variance were removed, the corre- lation coefficients ranged from 0.75 to 0.95 in each full data set (Table 1A). Removing poor-quality spectra improved the correlations, 0.88 to 0.96 (Table 1B) as did removing the batch effect, 0.95 to 0.99 (Table 1C). Dupli- cate spectra from individual samples showed a high degree of reproducibility as demonstrated by a median Pearson correlation coefficient of 0.98 for the 207 pairs of spectra in the CMLS-F4 data set. Results for the other data sets were similar (results not shown).
meaning. The simplest approach to feature extraction from mass spectra is to use abundance(intensity) information of every m/z measured as features. While this ap- proach to feature extraction is straight forward, it places additional demand on the feature selection and classification stages since a very large number of features are used (≈ 15, 000) and most studies employ a modest number of cases (' 500). More- over, mass spectrometers can only distinguish the masses of proteins within a finite resolution level. More than one m/z measures can correspond to the same protein. Thus, high level of correlation are expected between close m/z values [77, 46]. From a biomedical perspective, it is important to find a moderate number of proteins that most contribute to correct classification, such that these potential biomarkers can be identified and biochemically validated. Therefore it is necessary to extract useful information or patterns from the preprocessed MS datasets, though good choice of patterns can lead to improvements in clustering performance. Principal component analysis (PCA), factor analysis and linear discriminative analysis has been widely used methods in pattern recognition for feature extraction and dimensionality re- duction. But recently being realized that signal processing based pattern recongition can provide a set of novel and useful tools for solving highly relevant problems in genomics and proteomics. In , the principle of linear predictive coding (LPC) has been effectively applied in SELDI-MS datasets and promising results were obtained in distinguishing healthy from cancer using simple LPC-based decision logic. The researchers reported that the applications of signal-processing based pattern ananl- ysis can offer effective tools for the study of complex biological problems. Therefore in our experiment, we applied linear predictive coding (LPC) to extract or select the features from the given MS dataset (m/z), though the raw form doesn’t convey useful information for the task of classification. Considering MS data as a signal, the features can be extracted and it can be represented as LPC coefficients[60, 62].
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7.1 Example of mass spectrum in which the relative intensity is plotted against mass-to-charge ratio(m/z). The data in this example are from the FDA-NCI Clinical Proteomics Program Databank. Every point of the mass-spectra is a candidate feature and usually the spectra of a cancer patient differs from that of a healthy person. . . . . . . . . . . 66
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In the work, a study of mass fragmentation routes by the electron-impact mass spectrometry data has been examined for two open chain intermediates of indole derivatives and two pyrroloquino- lines. By the isolation of open chain intermediate and the mass spectra, the structures of pyrrolo- quinoline have been confirmed.
Abstract: In Mass spectrometry separation of ions takes place on the basis of there mass to charge ratio. Mass spectroscopy is an advanced and powerful technique for qualitative and quantitative analysis. Detector plays an important role in mass spectrometer for the separated charged ions. Results are displayed as spectra of the relative abundance of detected ions as a function of the mass-to-charge ratio. The detector with desirable properties in mass spectrometer should have high amplification, Fast time response, Low noise, High collection efficiency, Low cost, Narrow distribution of responses, Same response for all masses, Large dynamic range, Long term stability, Long life and can be Mounted outside of the vacuum if possible. Type of mass detectors and there working capacity reviewed in the present article. These mass detectors are a] Electron Multiplier, Faraday cups, Photographic plates, Scintillation counter, channel electron Multipliers, Resistive anode Encoder image Detectors, High mass detection detectors); b] Conversion Dynodes- Helium leak detectors, Advanced detectors; c] Cryogenic Detectors-Multi Pixel Photon counter and d] Other Detectors- TQD Tandem Quadrupole MS Detector, Photonics BI Polar Maldi TOf Detector and Flexar SQ 300 MS Detector. It is also observed that evoluation in mass spectrometry always takes place as per the type and need of analysis.
Traditionally, metabolite identification studies were initiated once the drug cleared the discovery process and just entered into development process. By this time potential candidates were available and metabolism studies were conducted. From this study metabolites were isolated and characterized by conventional spectrometry and synthesis of such metabolites was carried out. By comparing the UV spectra, retention time or by spiking the sample, the presence of such metabolites in biological samples were confirmed. Traditionally GC-MS and off flow liquid scintillation counting were employed for metabolite identification (Ramanathan et al., 2007, Prakash et al., 2007, Zhu et al., 2011). Till the late 1990s GC-MS was primarily used for metabolite identification. However, use of GCMS declined because of two major drawbacks viz, requirement of dervatization of analyte(s) and temperature fluctuation. This resulted in frequent shifting in chromatographic retention time (Mastovska and Lehotay, 2003, Koek et al., 2006).
In quantitative metabolomics studies, the most crucial step was arresting snapshots of all interesting metabolites. However, the procedure customized for Streptomyces was so rare that most studies consulted the procedure from other bacteria even yeast, leading to inaccurate and unreliable metabolomics analysis. In this study, a base solution (acetone: ethanol = 1:1, mol/mol) was added to a quenching solution to keep the integrity of the cell membrane. Based on the molar transition energy (E T ) of the organic solvents, five solutions were used to carry out the quench- ing procedures. These were acetone, isoamylol, propanol, methanol, and 60% (v/v) methanol. To the best of our knowledge, this is the first report which has utilized a quenching solution with E T values. Three procedures were also adopted for extraction. These were boiling, freezing–thawing, and grinding ethanol. Following the analysis of the mass balance, amino acids, organic acids, phosphate sugars, and sugar alcohols were measured using gas chroma- tography with an isotope dilution mass spectrometry. It was found that using isoamylol with a base solution (5:1, v/v) as a quenching solution and that freezing–thawing in liquid nitrogen within 50% (v/v) methanol as an extracting procedure were the best pairing for the quantitative metabolomics of Streptomyces ZYJ-6, and resulted in average recoveries of close to 100%. The concentration of intracellular metabolites obtained from this new quenching solu- tion was between two and ten times higher than that from 60% (v/v) methanol, which until now has been the most commonly used solution. Our findings are the first systematic quantitative metabolomics tools for Streptomyces ZYJ-6 and, therefore, will be important references for research in fields such as 13 C based metabolic flux analysis, multi-omic
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values di ﬀ ering by a small amount and expressed as the peak width in mass units (Deltam). But mass resolving power is defined separately as m /∆ m in a manner similar to mass resolu- tion. These definitions of resolving power in mass spectrometry and mass resolving power are contradictory, the former is expressed as a mass and the latter as a dimensionless ratio. Con- sequently, the 2013 IUPAC definition for mass resolving power is a “measure of the ability of a mass spectrometer to provide a specified value of mass resolution”, while mass resolution is defined as the observed m / z value divided by the smallest di ﬀ erence ( ∆ )m / z for two peaks that can be separated: (m / z) / ( ∆ )m / z. There are several ways to define the minimum peak separation, and the two most widely used are the valley definition and the peak width definition. Figure 2.22 pictorially illustrates the two di ﬀ erent definitions of resolution. In the valley definition, ∆ m is the closest spacing of two peaks of equal intensity with the valley (lowest value of signal) between them less than a specified fraction of the peak height. Typical valley values are 10% or 50%. The value obtained from a 5% peak width is roughly equivalent to a 10% valley. In the peak width definition, the value of ∆ m is the width of the peak measured at a specified fraction of the peak height, for example 0.5%, 5%, or 50%. Additionally, the latter is called the full width at half maximum (FWHM), which is usually used in orbitrap technology . Mass ac- curacy is the closeness of the agreement between the result of a measurement and a true value (exact mass). When a measurement is close to the true value we say it is accurate and when it is not we say it is inaccurate. Mass precision is the closeness of agreement between independent mass measurement results . When a set of mass measurements of one ion species lie close together we say the measurements are precise, and when not we say the measurements are im- precise. Figure 2.23 illustrates these four terms: (a) Mass resolution R = m /∆ m at FWHM; (b) accuracy is the proximity of the experimental measurement (blue vertical line) to the exact mass (red vertical line); precision is the repeatability of the measurement reflecting random errors.
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Using the mass spectrometer it was possible to detect helium contamination levels as low as 5% compared with 50% for the contamination probe described by Stalker. The results of the contamination probe measurement are shown on Figures 13 and 14 for comparison. These were measured in the following manner. The slopes of the traces shown in Figure 2, reference 4, were measured and the time when the slope changed was assumed to be the time when the helium contamination had reached 50% in the contamination probe cavity. The response time of the contamination probe as given by Stalker (4) was subtracted from these times.
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introduced at the top of the stratosphere would have a longer residence time than would plutonium from weapons testing which was mainly introduced to the lower parts. Besides the location of injected particles, other physical properties such as density also influence the residence time. A time lag of one year between test and deposition was observed for 239,240 Pu in ice cores (Koide et al, 1979). The same time is observed by comparing the plutonium ratio data from the stratosphere with the ice cores. The mean residence time of plutonium in the troposphere is 71 days (Holloway and Hayes, 1982). Since the processes responsible for stratospheric-tropospheric exchange depend on the time of the year (details in Appendix A.7), the occurrence of stratospheric fallout products at the earth surface is seasonally modulated. The maximum stratospheric-tropospheric exchange occurs in spring (March- June). The amount of debris, which enters the troposphere during July-October, is insignificant (Roedel, 1994). The stratospheric-tropospheric transfer behaviour is illustrated by 137 Cs-data from Moosonee, Ontario, Canada (51° 16' N, 80° 30' W) in Figure 3.2. No tropospheric fallout was expected for this location and its latitude is similar to that of the UK. Therefore a similar behaviour to stratospheric fallout in Britain can be assumed. Large nuclear tests were conducted prior to 1958, 1958, 1961 and 1962 with the highest yield occuring in 1962. Apart from two small Chinese tests (yield<20kt), no tests were performed in 1963, 1964 and 1965. Therefore the fallout during these years originates mainly from the stratospheric reservoir. For this reason these years are chosen to demonstrate the seasonal modulation of stratospheric fallout using data from the EML “Surface Air Sampling” – database (EML, 2000). It can clearly be seen in Figure 3.2 that the greatest annual Cs-137 concentration at the earths surface occurs between March and June. This period is known as the "spring-peak".
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There has been a significant increase in interest in detection of ions that have high mass. The definition of high mass depends on one’s perspective that those engaged in residual gas analysis, high mass of ions might be anything in excess of 200 Daltons, while a LC/MS and biomedical applications require detection of ions from tens and even hundreds of thousands of Daltons and GC/MS application may require detection of ions from 700-2000 Daltons. The detection efficiency decreases nearly exponentially with increasing mass of ions and becomes negligible to solve this problem several approaches have been developed, including the use of higher impact energies and high energy conversion dynodes CONVERSION DYNODES
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One can envision a reaction chamber filled with a reactive gas and a mist of microdroplets. One can also think that reactions could take place indiscriminately on all gas-liquid interfaces, both of the liquid microjets and of the microdroplets mists that, after all, have much larger surface areas. Two considerations should caution us about rushing to such conclusion. The first one is that the concentrations of reactant gases carried by the gas streams are highest at the point where they intersect the liquid microjets at the tip of the nebulizer (Scheme 1). The second one is that charged microdroplets are accelerated in the electric field with accelerations that scale with their charge-to-mass ratio (see below). Since the masses of the microdroplets continuously decrease by solvent evaporation while their charges do not, microdroplets are doubly accelerated, i.e., they have very short lifetimes in the spraying chamber. Therefore, microdroplets are in contact with very dilute reactant gases for very short times.
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Experimental designs for UPLC-DAD calibration were constructed for all nine dyes based on 5 replicate experiments at 7 levels of dye concentration (0 ppb, 100 ppb, 500 ppb, 1000 ppb, 1500 ppb, 2000 ppb, and 2500 ppb). A blank sample was measured 15 times as a quality control sample interspersed through the runs. A lower concentration design was also performed based on five replicate experiments at using standard mixtures of the 9 dyes at concentrations 10 ppb, 20 ppb, 30 ppb, 40 ppb, 50 ppb, and 18 blank injections to better characterize low limits of detection. For UPLC-MS-MS, calibration designs included standards at 10 ppb, 200 ppb, 400 ppb, 600 ppb, 800 ppb, and 1000 ppb. For each dye peak, QuanLynx™, data management software included with MassLynx™ (Waters Corporation, Milford, MA) was used to integrate peak areas above corrected baselines. For each dye standard at 1000 ppb concentration, the retention time window encompassing the baseline peak width was determined; this window was then employed as the dye peak integration window for all samples, including blanks.
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Twenty-five of the most commonly used acetate, apparel, and automotive disperse dyes (with known structures) were chosen for LC-Q-TOF analysis. Dyes with known structures were chosen for increased confidence in the identification of mass spectral ions. Out of the twenty-five dyes, samples were obtained from multiple manufacturers and were analyzed to determine differences in dye structure and number of dye components. The ability to identify the dye components present and dyestuff manufacturer using DAD chromatograms and mass spectral data was also explored by analyzing 10 unknown dyes chosen from a total of 92 dyes. Identification of dyestuff manufacturer has the potential to increase the forensic utility of the dyed fiber database. Finally, ten automotive fabrics were chosen and several dyes were identified from the twenty-five different dye standards. Method validation is important for verifying that the LC-Q-TOF method is suitable for dye identification. The HPLC method used to analyze all 92 dyes was optimized and is described in section 4.1.
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To elicit this information, two methods were employed. Laser ablation inductively coupled-plasma mass spec- trometry (LA-ICP-MS) is a powerful, well-established tool for generating quantitative compositional data in solids, and is accordingly widely applied across the earth sciences and in mineral processing research . It has, however, several drawbacks. Quadrupole mass spectrom- etry generally has a mass resolution of 1 atomic mass unit (amu), which prevents distinction between the mass of interest and isobaric mass interferences. Addition- ally, the finest spatial resolution available is limited by a minimum 3 μm-diameter spot (commonly resulting in a much larger pit, depending on the mineral). For quanti- tative trace element analysis, much larger spot sizes are required. The Cameca nanoscale secondary ion mass spectrometry (nanoSIMS) platform is an imaging tech- nique which offers solutions to both the above problems. Each of seven detectors on the nanoSIMS has mass reso- lution approaching 0.1 amu, which is very useful in dis- tinguishing, for example, 226 Ra (226.0254) from 88 Sr 138 Ba
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plained variation also declined from 14 % to 8.8 %, going from two to six factors, and then reached 8.0 % for seven fac- tors and 7.6 % for eight factors. The two-factor solution first split the data into a daytime factor and a nighttime factor, with very distinct mass spectral profiles. The daytime factor was characterized by signals at 307, 311, 323, 339 Th and other odd masses, while the nighttime factor was dominated by 308, 325, 340 and 342 Th. The odd masses are typical signatures of daytime monoterpene-derived organonitrates at the site, while the even masses, and specific odd masses e.g., a radical at 325 Th, have been identified as monoterpene ozonolysis products (Ehn et al., 2014; Yan et al., 2016). As the number of factors increased, the daytime factor was fur- ther split into new daytime factors, with diurnal profiles hav- ing various peak times around noon or early afternoon. When the number of factors increased to seven, a clear sawtooth shape in the diurnal trend was resolved with marker masses at 308, 324, 325 and 339 Th. Many of the profiles resolved in the seven-factor solution are similar to those found by Yan et al. (2016), and separating more factors did not yield new fac- tors that we could interpret. Therefore, we opted to use this seven-factor result for the main discussion below, as it pro- vided us with enough information to evaluate the binPMF method for this dataset.
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In the sources so far described, ionization takes place in the vacuum region of the mass spectrometer, thus requiring removal, either through additional pump- ing or by a reduction in the S ow rate of the mobile phase. The production of ions prior to entry to the MS high vacuum regions, i.e. at atmospheric pres- sure, would obviate these requirements. Development of atmospheric pressure ionization techniques has led to a rapid and exciting development in LC-MS instru- mentation. Although API methods have been avail- able for a number of years, it was not until the pioneering work of Fenn et al. that their potential was realized. The two variants normally employed in con- junction with HPLC are electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI).
Lipid isolation was performed according to Bligh and Dyer . Lipids were dried in vacuo and separated by 2D-HPTLC (silica plates, 5 × 5 cm, particle size 4–8 μm, Merck) using chloroform/methanol/conc. ammonia 10:5:1 (V/V/V) for first dimension and after air-drying by means of chloroform/methanol/acetic acid/water 15:3:2:0.6 (V/V/V) for second dimension. Lipids were de- tected using primuline solution (0.05 % solution of pri- muline in acetone/water 8:2 (V/V)) and 365 nm UV light. Lipid spots were scratched from plates and 250 μL of chloroform/methanol/water 10:10:0.1 (V/V/V) was added in order to extract the lipids and extraction pro- cedure was repeated two times. Obtained extracts were pooled and dried in vacuo. Lipids were dissolved in 4 μL of 20 mg/mL 2,4,6-trihydroxyacetophenone solution in methanol and applied to a stainless steel MALDI (matrix-assisted laser desorption/ionization) -target. Ricinus oil (0.5 μL in 1 mL of methanol with addition of small amount of NaCl) was used for mass calibration of the MS instruments. Experiments were performed at least with three separate samples.
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An ion mobility spectrometer may be coupled to a mass spectrometer (see Figure 3) with sample trans- fer via a pinhole, typically 50 to 100 in diameter. The mass spectrometer used in conjunction with an ion mobility spectrometer enables m / z identi R cation of the reactant and product ions. The mass spectrometer is initially programmed to scan through the chosen mass range with the IMS shutter grids continuously open. (If the ion mobility spectrometer is used in the normal pulsed mode it may take a very long time to obtain a mass spectrum, which may then not be representa- tive.) Thus, it is possible to record ions created in an ion mobility spectrometer, and a mass spectrum of IMS sample ions is shown in Figure 4.
An important content of the Chemical Weapons Convention (CWC) is the verification regime, which may in- clude inspections of declared or suspected CW facilities. Samples are collected from these facilities and sub- jected to unequivocal identification of the Convention related chemicals (CRCs). For unequivocal identification of CRCs, the Organization for the Prohibition of Chemical Weapons (OPCW) maintains a network of designat- ed laboratories by conducting official proficiency tests to evaluate their analytical capabilities  . CRCs in- clude not only the popular chemical warfare agents (e.g. Sarin and Soman) but also their precursors and degra- dation products. Consequently, a great deal of work has been devoted to build up MS database of CRCs and re- fine their analytical methods. In OPCW sample analyses, at least one of the data-rich spectrometric techniques (LC-MS/MS, GC-MS (EI), GC-MS/MS (CI), or NMR) is required to ensure unambiguous identification of CRCs. Comparison with reference data either from synthetic authentic chemicals or from database must be provided to make the identification.