Automated Mass Spectral Deconvolution Research Articles

Combination of stir bar sorptive extraction–retention time locked gas chromatography–mass spectrometry and automated mass spectral deconvolution for pesticide identification in fruits and vegetables

Fruit and vegetable samples collected at a local market were analyzed using stir bar sorptive extraction (SBSE) followed by thermal desorption, retention time locked (RTL) gas chromatographic separation and full scan mass spectrometric determination. Although the technique is proven to have sufficient extraction efficiency and productivity in pesticide analysis, the full scan chromatograms are loaded with matrix components making qualitative evaluation time consuming and too difficult to be adequate for routine laboratory screening. Identification can be facilitated to a great extent with evaluation methods provided by data evaluation software. Through the comparison of three evaluation methods we found that the mass spectral deconvolution software AMDIS supports most the decision making process, performing best in discovering positive hits and reducing data for supervision, making SBSE suitable for routine applications.

Study of automated mass spectral deconvolution and identification system (AMDIS) in pesticide residue analysis

The effects of overlapping levels and concentration ratios of overlapping components, and of scan rates of the mass spectrometer, on the capability of the automated mass spectral deconvolution and identification system (AMDIS) in pesticide residue analysis were studied. To investigate the capability of AMDIS in removing interferences from the overlapping peaks, this system was applied to data files obtained from the gas chromatography/mass spectrometry (GC/MS) analysis of two overlapping (co-eluting) pesticides (beta-HCH and PCNB) in full scan mode. Differences in overlap levels, the concentration ratios of the two overlapping components and the scan rates of the instrument were studied. When the difference in scan number of overlapping compounds was equal to 1 scan, AMDIS incompletely extracted 'purified' mass spectra but as the difference increased to 3 or more scans, complete correct spectra could be extracted. The results also show that when the scan rate was in the range of 0.4-0.90 s/scan and the concentration ratios of the target compound/interference were above 1/5, there were ideal deconvolution results for this approach. To further study the application of AMDIS to pesticide residue analysis, AMDIS was applied to the identification of pesticides spiked in real samples (cabbage and rice). Typical pesticides being evaluated were identified using AMDIS at concentrations >50 ng/g in the extracts.

LC–MS with electron ionization of cold molecules in supersonic molecular beams

A new approach is described for the combination of electron ionization and LC–MS based on sample ionization as vibrationally cold molecules in a supersonic molecular beam (Cold EI). Cold EI of sample compounds in liquid solutions (methanol, acetonitrile, water, etc.) is achieved through spray formation, followed by soft thermal vaporization of the sample particles prior to their supersonic expansion and direct electron ionization of the sample compounds while they are contained in a supersonic molecular beam (SMB). Cold EI mass spectra were demonstrated to combine an enhanced molecular ion and improved mass spectral information (in comparison with standard EI), plus all the library searchable fragments. Cold EI enables the ionization of a broad range of compounds, including the full range of non-polar samples. Four orders of magnitude linear dynamic range is demonstrated and a detection limit of 2 pg was achieved for a 774 amu compound in single ion monitoring mode at m/ z = 774. The method and apparatus are under continuous development and we feel that it can excel particularly in the analysis of unknown samples, while enabling fast LC–MS analysis through automated mass spectral deconvolution of coeluting LC peaks. In addition, the same MS system can also serve as an advanced GC–MS with supersonic molecular beams.

Combination of gas chromatography–mass spectrometry and mass spectral deconvolution for structural elucidation of an unusual C 29-steroid detected in a complex sedimentary matrix

A complex sedimentary sample from the Monterey Formation (CA, USA) has been submitted to GC–MS analysis followed by mass spectral deconvolution using Automated Mass Spectral Deconvolution and Identification System (AMDIS). Adjusting the parameters of the software allowed for the extraction of the spectrum of an unusual steroidal hydrocarbon coeluting with the major compound of the chromatogram. Following a careful interpretation of the “extracted” mass spectrum, the structure of the unknown has been postulated to be the 4,14-dimethylcholestane (DMC). Possible origins of this rare steroid are briefly discussed. Thus, application of AMDIS appears to be particularly suitable for the GC–MS analysis of natural complex mixtures characterized by a high number of analytes present in low amounts.

Metabolic networks of Cucurbita maxima phloem

Metabolomic analysis aims at a comprehensive characterization of biological samples. Yet, biologically meaningful interpretations are often limited by the poor spatial and temporal resolution of the acquired data sets. One way to remedy this is to limit the complexity of the cell types being studied. Cucurbita maxima Duch. vascular exudates provide an excellent material for metabolomics in this regard. Using automated mass spectral deconvolution, over 400 components have been detected in these exudates, but only 90 of them were tentatively identified. Many amino compounds were found in vascular exudates from leaf petioles at concentrations several orders of magnitude higher than in tissue disks from the same leaves, whereas hexoses and sucrose were found in far lower amounts. In order to find the expected impact of assimilation rates on sugar levels, total phloem composition of eight leaves from four plants was followed over 4.5 days. Surprisingly, no diurnal rhythm was found for any of the phloem metabolites that was statistically valid for all eight leaves. Instead, each leaf had its own distinct vascular exudate profile similar to leaves from the same plant, but clearly different from leaves harvested from plants at the same developmental stage. Thirty to forty per cent of all metabolite levels of individual leaves were different from the average of all metabolite profiles. Using metabolic co-regulation analysis, similarities and differences between the exudate profiles were more accurately characterized through network computation, specifically with respect to nitrogen metabolism.

Comparison of gas chromatography–pulsed flame photometric detection–mass spectrometry, automated mass spectral deconvolution and identification system and gas chromatography–tandem mass spectrometry as tools for trace level detection and identification

The complexity of a matrix is in many cases the major limiting factor in the detection and identification of trace level analytes. In this work, the ability to detect and identify trace level of pesticides in complex matrices was studied and compared in three, relatively new methods: (a) GC–PFPD–MS where simultaneous PFPD (pulsed flame photometric detection) and MS analysis is performed. The PFPD indicates the exact chromatographic time of suspected peaks for their MS identification and provides elemental information; (b) automatic GC–MS data analysis using the AMDIS (“Automated Mass Spectral Deconvolution and Identification System”) software by the National Institute of Standards and Technology; (c) GC–MS–MS analysis. A pesticide mixture (MX-5), containing diazinon, methyl parathion, ethyl parathion, methyl trithion and ethion was spiked, in descending levels from 1 ppm to 10 ppb, into soil and sage (spice) extracts and the detection level and identification quality were evaluated in each experiment. PFPD–MS and AMDIS exhibited similar performance, both superior to standard GC–MS, revealing and identifying compounds that did not exhibit an observable GC peak (either buried under the chromatographic background baseline or co-eluting with other interfering GC peaks). GC–MS–MS featured improved detection limits (lower by a factor of 6–8) compared to AMDIS and PFPD–MS. The GC–PFPD–MS–MS combination was found useful in several cases, where no reconstructed ion chromatogram MS–MS peaks existed, but an MS–MS spectrum could still be extracted at the elution time indicated by PFPD. The level of identification and confirmation with MS–MS was inferior to that of the other two techniques. In comparison with the soil matrix, detection limits obtained with the loaded sage matrix were poorer by similar factors for all the techniques studied (factors of 5.8, >6.5 and 4.0 for AMDIS, PFPD–MS and MS–MS, respectively). Based on the above results, the paper discusses the trade-offs between detectivity and identification level with the compared three techniques as well as other more traditional techniques and approaches.

Deconvolution gas chromatography/mass spectrometry of urinary organic acids--potential for pattern recognition and automated identification of metabolic disorders.

The National Institute of Standards and Technology (NIST) Automated Mass Spectral Deconvolution and Identification System (AMDIS) is applied to a selection of data files obtained from the gas chromatography/mass spectrometry (GC/MS) analysis of urinary organic acids. Mass spectra obtained after deconvolution are compared with a special user library containing both the mass spectra and retention indices of ethoxime-trimethylsilyl (EO-TMS) derivatives of a set of organic acids. Efficient identification of components is achieved and the potential of the procedure for automated diagnosis of inborn errors of metabolism and for related research is demonstrated.