Non-targeted discovery-based analysis for gas chromatography with mass spectrometry: A comparison of peak table, tile, and pixel-based Fisher ratio analysis

Region of interest selection in heterogeneous digital image: Wine age prediction by comprehensive two-dimensional gas chromatography

This study integrates genetic algorithm (GA) with partial least squares regression (PLSR) and various variable selection methods to identify impactful regions of interest (ROI) in heterogeneous 2D chromatogram images for predicting wine age. As wine quality and aroma evolve over time, transitioning from youthful fruitiness to mature, complex flavors, which leads to alterations in the composition of essential aroma-contributing compounds. Chromatograms are segmented into subimages, and the GA-PLSR algorithm optimizes combinations based on grayscale, red-green-blue (RGB), and hue-saturation-value (HSV) histograms. The selected subimage histograms are further refined through interval selection, highlighting the compounds with the most significant influence on wine aging. Experimental validation involving 38 wine samples demonstrates the effectiveness of this approach. Cross-validation reduces the PLS model error from 2.8 to 2.4 years within a 10 × 10 subset, and during prediction, the error decreases from 2.5 to 2.3 years. The study presents a novel approach utilizing the selection of ROI for efficient processing of 2D chromatograms focusing on predicting wine age.

A non-target geographical origin screening of botrytized wines through comprehensive two-dimensional gas chromatography coupled with high-resolution mass spectrometry

One of the most effective methods for gaining insight into the composition of trace-level volatile organic characteristics of wine products is through the use of a comprehensive two-dimensional gas chromatography-high resolution mass spectrometry (GC × GC-HRMS) technique. The vast amount of data generated by this method, however, can often be overwhelming requiring exhaustive and time-consuming analysis to identify significant statistical characteristics. The use of advanced chemometric software can achieve the same or even higher efficiency. This study aimed to identify differences based on geographical locations by analyzing the volatile organic compounds in the composition of botrytized wines from Slovakia, Hungary, France, and Austria. The volatile organic compounds were extracted by solid-phase microextraction and analyzed using GC × GC-HRMS. The data obtained from the analysis underwent Fisher-ratio (F-ratio) tile-based analysis to identify statistically significant differences. Principal component analysis demonstrated a significant distinction between wine samples based on geographical location, using only 10 statistically significant features with the highest F-ratio. In the samples, the following compounds were analyzed: methyl-octadecanoate, 2-cyanophenyl-β-phenylpropionate, α-ionone, n-octanoic acid, 1,2-dihydro-1,1,6-trimethyl-naphthalene, methyl-hexadecanoate, ethyl-pentadecanoate, ethyl-decanoate, and γ-nonalactone. These, all play an important role in cluster pattern observed on principal component analysis results. Additionally, hierarchical cluster analysis confirmed this.

Evaluation of different getter substrates using two-dimensional gas chromatography with time of flight mass spectrometry

While understanding hydrogen uptake by organic based getters such as 1,4-bis(phenylethynyl)benzene (DEB) combined with a palladium(0)bis(dibenzylideneacetone) (Pd(dba)₂) catalyst is essential, another crucial element to understand is the decomposition of the DEB, Pd(dba)₂, and/or substrate material. The breakdown of these materials may create unwanted volatiles, which may interact with and lead to deterioration of sensitive materials. Moreover, it is critical to understand if different substrates cause the getter and/or catalyst to degrade in different manners. Utilizing comprehensive two-dimensional gas chromatography (GC×GC) with time-of-flight mass spectrometry (TOFMS), the presence of volatiles located in the headspace of various DEB/Pd(dba)₂ getter substrates is examined. These samples include a getter infused silicone foam, a hydrogenated getter infused silicone foam, an activated carbon getter pellet, and a hydrogenated activated carbon getter pellet. Application of Fisher ratio (F-ratio) analyses lead to the identification of several compounds that are generated or consumed through the hydrogenation process. These include benzene derivatives such as bibenzyl, benzaldehyde, and vinyl benzoate in the activated carbon pellets and 1,5-diphenyl-3-pentanone, toluene, styrene, and 1-1'(2-pentene 1,5-diyl)bis benzene in the silicone foams, and alkane/alkene derivatives such undecane, 4-tridecene, and decane in the activated carbon pellets and 2,6-dimethyl undecane in the silicone foams. Further comparison of the different hydrogenated getter substrates (e.g. activated carbon pellet and silicone foam) indicates that the different substrates alter the decomposition products created from the degradation of the DEB and Pd(dba)₂.

Optimization of Parameters for ROI Data Compression for Nontargeted Analyses Using LC-HRMS

Nontargeted analyses of low-concentration analytes in the information-rich data collected by liquid chromatography with high-resolution mass spectrometry detection can be challenging to accomplish in an efficient and comprehensive manner. The aim of this study is to demonstrate a workflow involving targeted parameter optimization for entire chromatograms using region of interest (ROI) data compression uncoupled from a subsequent tile-based Fisher ratio (F-ratio) analysis, a supervised discovery-based method, for the discovery of low-concentration analytes. Soil samples spiked with 18 pesticides at nominal concentrations ranging from 0.1 to 50 ppb for a total of six sample classes served as challenging samples to demonstrate the overall workflow. Optimization of two parameters proved to be the most critical for ROI data compression: the signal threshold parameter and the admissible mass deviation parameter. The parameter optimization method workflow we introduce is based upon spiking known analytes into a representative sample and determining the number of detectable spikes and the Δppm for various combinations of the signal threshold and admissible mass deviation, where Δppm is the absolute value of the difference between the theoretical m/z and the ROI m/z. Once optimal parameters are determined providing the lowest average Δppm and the greatest number of detectable analytes, the optimized parameters can be utilized for the intended analysis. Herein, tile-based F-ratio analysis was performed on the ROI compressed data of all spiked soil samples first by applying ROI parameters recommended in the literature, referred to herein as the initial ROI parameters, and finally by the combination of the two optimized parameters. Using the initial ROI parameters, three pesticides were discovered, whereas all 18 spiked pesticides were discovered by optimizing both ROI parameters.

Radiolytic degradation of dodecane substituted with common energetic functional groups†

Explosives exist in and are expected to withstand a variety of harsh environments up to and including ionizing radiation, though little is known about the chemical consequences of exposing explosives to an ionizing radiation field. This study focused on the radiation-induced chemical changes to a variety of common energetic functional groups by utilizing a consistent molecular backbone. Dodecane was substituted with azide, nitro, nitrate ester, and nitramine functional groups and γ-irradiated with ⁶⁰Co in order to study how the functional group degraded along with what the relative stability to ionizing radiation was. Chemical changes were assessed using a combination of analysis techniques including: nuclear magnetic resonance (NMR) spectroscopy, gas chromatography of both the condensed and gas phases, Raman spectroscopy, and Fourier transform infrared (FTIR) spectroscopy. Results revealed that much of the damage to the molecules was on the energetic functional group and often concentrated on the trigger linkage, also known as the weakest bond in the molecule. The general trend from most to least susceptible to radiolytic damage was found to be D–ONO₂ → D–N₃ → D–NHNO₂ → D–NO₂. These results also appear to be in line with the relative stability of these functional groups to things such as photolysis, thermolysis, and explosive insults.

The relative radiolytic stability of dodecane functionalized with common energetic functional groups was explored with gamma irradiation and probed by various analytical techniques.

Investigation into the Decomposition Pathways of an Acetal-Based Plasticizer

Until now, it has been assumed that the primary decomposition pathway for the liquid plasticizer bis(2,2-dinitropropyl)acetal and bis(2,2-dinitropropyl)formal (BDNPA/F) was nitrous acid elimination (NAE). An ultrahigh-performance liquid chromatography (UHPLC) coupled to quadrupole time-of-flight mass spectrometry (QTOF) methodology was developed to discover and identify the degradation products of BDNPA/F. No evidence of NAE was found. However, two other degradation pathways were found: (1) hydrolysis of the acetal/formal functional group and (2) radical-based homolysis of the C-N bond, followed by hydrogen atom abstraction. Hydrolysis of BDNPA/F proceeds by the formation of 2,2-dinitropropanol (DNPOH) and 2,2-dinitropropyl hemiacetal/hemiformal, which further decompose into DNPOH and ethanal/methanal, respectively. Hydrolysis is the dominant decomposition pathway in all samples; however, at higher temperatures, C-N homolysis becomes more significant. Also, the solid PBX 9501 has different ratios of decomposition products than the liquid BDNPA/F due to the slower rate of diffusion through solids than liquids.

Chemical Evaluation and Performance Characterization of Pentaerythritol Tetranitrate (PETN) under Melt Conditions

Pentaerythritol tetranitrate (PETN) is a nitrate ester explosive commonly used in commercial detonators. Although its degradation properties have been studied extensively, very little information has been collected on its thermal stability in the molten state due to the fact that its melting point is only ∼20 °C below its onset of decomposition. Furthermore, studies that have been performed on PETN thermal degradation often do not fully characterize or quantify the decomposition products. In this study, we heat PETN to melt temperatures and identify thermal decomposition products, morphology changes, and mass loss by ultrahigh-pressure liquid chromatography coupled to quadrupole time of flight mass spectrometry, scanning electron microscopy, nuclear magnetic resonance spectroscopy, and differential scanning calorimetry. For the first time, we quantify several decomposition products using independently prepared standards and establish the resulting melting point depression after the first melt. We also estimate the amount of decomposition relative to sublimation that we measure through gas evolution and evaluate the performance behavior of the molten material in commercial detonator configurations.

Development of variance rank initiated-unsupervised sample indexing for gas chromatography-mass spectrometry analysis

Traditional non-targeted chemometric workflows for gas chromatography-mass spectrometry (GC-MS) data rely on using supervised methods, which requires a priori knowledge of sample class membership. Herein, we propose a simple, unsupervised chemometric workflow known as variance rank initiated-unsupervised sample indexing (VRI-USI). VRI-USI discovers analyte peaks exhibiting high relative variance across all samples, followed by k-means clustering on the individual peaks. Based upon how the samples cluster for a given peak, a sample index assignment is provided. Using a probabilistic argument, if the same sample index assignment appears for several discovered peaks, then this outcome strongly suggests that the samples are properly classified by that particular sample index assignment. Thus, relevant chemical differences between the samples have been discovered in an unsupervised fashion. The VRI-USI workflow is demonstrated on three, increasingly difficult datasets: simulations, yeast metabolomics, and human cancer metabolomics. For simulated GC-MS datasets, VRI-USI discovered 85-90% of analytes modeled to vary between sample classes. Nineteen out of 53 peaks in the peak table developed for the yeast metabolome dataset had the same sample index assignments, indicating that those indices are most likely due to class-distinguishing chemical differences. A t-test revealed that 22 out of 53 peaks were statistically significant (p < 0.05) when using those sample index assignments. Likewise, for the human cancer metabolomics study, VRI-USI discovered 25 analytes that were statistically different (p < 0.05) using the sample index assignments determined to highlight meaningful sample-based differences. For all datasets, the sample index assignments that were deduced from VRI-USI were the correct class-based difference when using prior knowledge. VRI-USI holds promise as an exploratory data analysis workflow for studies in which analysts do not readily have a priori class information or want to uncover the underlying nature of their dataset.

Analytical Determination of the Severity of Potato Taste Defect in Roasted East African Arabica Coffee

The quality of East African coffee beans has been significantly reduced by a flavor defect known as potato taste defect (PTD) due to the presence of 2-isopropyl-3-methoxypyrazine (IPMP) and 2-isobutyl-3-methoxypyrazine (IBMP). Therefore, the aims of this study were to determine the correlation between these methoxypyrazines and the severity of odor attributed to PTD and discover additional analytes that may be correlated with PTD using Fisher ratio analysis, a supervised discovery-based data analysis method. Specialty ground roasted coffees from East Africa were classified as clean (i.e., no off-odor), mild, medium, or strong PTD. For the samples examined, IPMP was found to discriminate between non-defective and defective samples, while IBMP did not do so. Samples affected by PTD exhibited a wide range of IPMP concentration (1.6-529.9 ng/g). Except for one sample, the IPMP concentration in defective samples was greater than the average IPMP concentration in the non-defective samples (2.0 ng/g). Also, an analysis of variance found that IPMP concentrations were significantly different based on the severity of odor attributed to PTD (p < 0.05). Fisher ratio analysis discovered 21 additional analytes whose concentrations were statistically different based on the severity of PTD odor (p < 0.05). Generally, analytes that were positively correlated with odor severity generally had unpleasant sensory descriptions, while analytes typically associated with desirable aromas were found to be negatively correlated with odor severity. These findings not only show that IPMP concentration can differentiate the severity of PTD but also that changes in the volatile analyte profile of coffee beans induced by PTD can contribute to odor severity.