Relationships in Gas Chromatography—Fourier Transform Infrared Spectroscopy—Comprehensive and Multilinear Analysis

Parallel coupling of gas chromatography to mass spectrometry and solid deposition Fourier transform infrared spectroscopy: an innovative approach to address challenging identifications

The request for novel hyphenated instruments and techniques, capable of affording exhaustive information and results, is a focus continuously watched out. In this context, the present work aimed at the development of an integrated system combining gas chromatographic (GC) separation with mass spectrometry (MS) and (solid deposition) Fourier transform infrared spectroscopy (FTIR) detection. An external transfer line was designed in the lab for the parallel coupling of the two detectors, in such a way to obtain complementary analytical information consisting of an MS spectrum, an IR spectrum and linear retention indices (LRI), within a single analysis. The instrument performance was demonstrated for the analysis of a commercial mixture consisting of 139 hydrocarbons, comprising linear, branched, unsaturated and aromatic compounds. A 100-m poly(dimethylsiloxane) column was employed for the separation, and the outlet flow was split 95:5 between the IR and MS detectors using two uncoated capillaries. The IR spectra were acquired from solid deposits on a zinc selenide disc (-90 °C), over a spot (detector area) of about 0.1 mm2, in the range of 4000-700 cm-1 and at a resolution of 4 cm-1. Final identification of the separated compounds by a library search was achieved by excluding incorrect results, sequentially using a three-filter approach (85% similarity against reference MS and IR library spectra and ±10 LRI unit tolerance). Based on these preliminary results, the GC-MS/sd-FTIR system is a promising tool for the characterization of complex matrix constituents, for which identification is cumbersome, by using only one detection technique.

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.

Nanoelectromechanical Infrared Spectroscopy with In Situ Separation by Thermal Desorption: NEMS-IR-TD

We present a novel method for the quantitative analysis of mixtures of semivolatile chemical compounds. For the first time, thermal desorption is integrated directly with nanoelectromechanical infrared spectroscopy (NEMS-IR-TD). In this new technique, an analyte mixture is deposited via nebulization on the surface of a NEMS sensor and subsequently desorbed using heating under vacuum. The desorption process is monitored in situ via infrared spectroscopy and thermogravimetric analysis. The resulting spectro-temporal maps allow for selective identification and analysis of the mixture. In addition, the corresponding thermogravimetric data allow for analysis of the desorption dynamics of the mixture components. As a demonstration, caffeine and theobromine were selectively identified and quantified from a mixture with a detection limit of less than 6 pg (about 30 fmol). With its exceptional sensitivity, NEMS-IR-TD allows for the analysis of low abundance and complex analytes with potential applications ranging from environmental sensing to life sciences.

High information spectroscopic detection techniques for gas chromatography

Gas chromatography has always been a simple and widely used technique for the separation of volatile compounds and their quantitation. However, the common detectors used with this technique are mostly universal and do not provide any specific qualitative information. There have been some attempts to combine the separation power of GC with the qualitative capabilities of "high-information" spectroscopic techniques including infrared spectroscopy, nuclear magnetic resonance spectroscopy, molecular rotational resonance spectroscopy, and vacuum ultraviolet spectroscopy. Some of these hyphenations have proven to be quite successful while others were less so. The history of such attempts, up to the most recent studies in this area, are discussed. Most recently, the hyphenation of GC with molecular rotational resonance spectroscopy which provides promising results and is a newly developed technique is reviewed and compared to previous high-information spectroscopic detection approaches. The history, description and features of each method along with their applications and challenges are discussed.

Structure Elucidation Using Gas Chromatography-Infrared Spectroscopy/Mass Spectrometry Supported by Quantum Chemical IR Spectrum Simulations

An improved strategy for compound identification incorporating gas chromatography hyphenated with Fourier transform infrared spectroscopy and mass spectroscopy (GC-FTIR/MS) is reported. (Over)reliance on MS may lead either to ambiguous identity or to incorrect identification of a compound. However, the MS result is useful to provide a cohort of possible compounds. The IR result for each tentative compound match was then simulated using molecular modeling, to provide functional group and isomer differentiation information, and then compared with the experimental FTIR result, offering identification based on both MS and IR. Several basis sets were evaluated for IR simulations; Def2-TZVPP was a suitable basis set and correlated well with experimental data. The approach was applied to industrial applications, confirming the isomers of 2,3-bis(thiosulfanyl)-but-2-enedinitrile, bromination products of 1-bromo-2,3-dimethylbenzene, and autoxidative degradation of phenyl-di-tert-butylphosphine.

13C NMR Dereplication Using MixONat Software: A Practical Guide to Decipher Natural Products Mixtures

The growing use of herbal medicines worldwide requires ensuring their quality, safety, and efficiency to consumers and patients. Quality controls of vegetal extracts are usually undertaken according to pharmacopeial monographs. Analyses may range from simple chemical experiments to more sophisticated but more accurate methods. Nowadays, metabolomic analyses allow a fast characterization of complex mixtures. In the field, besides mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR) has gained importance in the direct identification of natural products in complex herbal extracts. For a decade, automated dereplication processes based on ¹³C-NMR have been emerging to efficiently identify known major compounds in mixtures. Though less sensitive than MS, ¹³C-NMR has the advantage of being appropriate to discriminate stereoisomers. Since NMR spectrometers nowadays provide useful datasets in a reasonable time frame, we have recently made available MixONat, a software that processes ¹³C as well as distortionless enhancement by polarization transfer (DEPT)-135 and -90 data, allowing carbon multiplicity (i.e., CH₃, CH₂, CH, and C) filtering as a critical step. MixONat requires experimental or predicted chemical shifts (δ _C) databases and displays interactive results that can be refined based on the user's phytochemical knowledge. The present article provides step-by-step instructions to use MixONat starting from database creation with freely available and/or marketed δ _C datasets. Then, for training purposes, the reader is led through a 30 - 60 min procedure consisting of the ¹³C-NMR based dereplication of a peppermint essential oil.

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra

The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets. Here, we present results of a high-throughput computational method for predicting IR spectra of candidate compounds obtained from the PubChem database. Predicted spectra were ranked based on their similarity to gas-phase experimental IR spectra of test compounds obtained from the NIST. Our computational workflow (IRdentify) consists of a fast semiempirical quantum mechanical method for initial IR spectra prediction, ranking, and triaging, followed by a final IR spectra prediction and ranking using density functional theory. This approach resulted in the correct identification of 47% of 258 test compounds. On average, there were 2152 candidate structures evaluated for each test compound, giving a total of approximately 555,200 candidate structures evaluated. We discuss several variables that influenced the identification accuracy and then demonstrate the potential application of this approach in three areas: (1) combining IR and mass spectra rankings into a single composite rank score, (2) identifying the precursor and fragment ions using cryogenic ion vibrational spectroscopy, and (3) the incorporation of a trimethylsilyl derivatization step to extend the method compatibility to less-volatile compounds. Overall, our results suggest that matching computational with experimental IR spectra is a potentially powerful orthogonal option for adding significant high-throughput chemical structure discrimination when used with other non-targeted chemical structure identification methods.