Data analysis programs for comprehensive two-dimensional chromatography

Comprehensive Two-Dimensional Gas Chromatography in Petroleum Derived Samples: A Review on Advances in Source and Weathering Studies of Spilled Oil

Since its introduction comprehensive two-dimensional gas chromatography (GC × GC) has been widely applied to analyze complex samples due to its enhanced peak capacity and selectivity, thereby increasing the number of identifiable peaks and improving coelution issues. Even though it is still undergoing development, GC × GC provides many advantages in the analysis of petroleum-derived samples, whether in reservoir geochemistry applications or in environmental studies associated with spilled oils. In general, it facilitates more thorough fingerprinting and compositional evaluation. In environmental studies, it helps enhance understanding of weathering processes and the environmental behavior of hydrocarbons, as its chromatographic retention indices can robustly estimate liquid vapor pressures, aqueous solubility and other physical chemical properties. This review presents a brief history of GC × GC instrumentation, discussing recent and significant advances in petroleum applications, from data handling techniques to reservoir geochemistry and environmental forensics, as well as some specific advantages achieved and certain limitations that continue to be encountered.

Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification

A new tile-based pairwise analysis workflow, termed 1v1 analysis, is presented to discover and identify analytes that differentiate two chromatograms collected using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). Tile-based 1v1 analysis easily discovered all 18 non-native analytes spiked in diesel fuel within the top 30 hits, outperforming standard pairwise chromatographic analyses. However, eight spiked analytes could not be identified with multivariate curve resolution-alternating least-squares (MCR-ALS) nor parallel factor analysis (PARAFAC) due to background contamination. Analyte identification was achieved with class comparison enabled-mass spectrum purification (CCE-MSP), which obtains a pure analyte spectrum by normalizing the spectra to an interferent mass channel (m/z) identified from 1v1 analysis and subtracting the two spectra. This report also details the development of CCE-MSP assisted MCR-ALS, which removes the identified interferent m/z from the data prior to decomposition. In total, 17 out of 18 spiked analytes had a match value (MV) > 800 with both versions of CCE-MSP. For example, MCR-ALS and PARAFAC were unable to decompose the pure spectrum of methyl decanoate (MVs < 200) due to its low 2D chromatographic resolution (∼0.34) and high interferent-to-analyte signal ratio (∼30:1). By leveraging information gained from 1v1 analysis, CCE-MSP and CCE-MSP assisted MCR-ALS obtained a pure spectrum with an average MV of 908 and 964, respectively. Furthermore, tile-based 1v1 analysis was applied to track moisture damage in cacao beans, where 86 analytes with at least a 2-fold concentration change were discovered between the unmolded and molded samples. This 1v1 analysis workflow is beneficial for studies where multiple replicates are either unavailable or undesirable to save analysis time.

Research on Ethanol Coupling to Prepare C4 Olefins Based on BP Neural Network and Cluster Analysis

Ethanol, as a clean energy source, is an ideal raw material for the preparation of C4 olefins, but there are few studies on the preparation of C4 olefins by the coupling of ethanol. This research is based on the data from Question B of the 2021 Chinese Contemporary Undergraduate Mathematical Contest in Modeling. The researchers firstly perform data interpolation and visualization processing on the original data. Secondly, based on the two-dimensional visualization analysis, we cluster the different catalyst combinations; divide the influencing factors into temperature, Co/SiO2 and HAP loading ratio, Co loading, and ethanol concentration; and construct a quaternary linear regression equation that affects ethanol conversion rate and C4 olefin selectivity. Finally, according to the three-dimensional spatial visualization analysis and using the BP neural network model training data, we obtain that under the conditions of using loading method I, catalyst combination type 200 mg 0.5 wt% Co/SiO2-200 mg HAP-ethanol concentration 0.9 ml/min and temperature 450°C, the yield of C4 olefins can reach the maximum. This study provides new research ideas and methods for the preparation of C4 olefins from ethanol.

Analysis of Volatile Compounds by Advanced Analytical Techniques and Multivariate Chemometrics

Smelling is one of the five senses, which plays an important role in our everyday lives. Volatile compounds are, for example, characteristics of food where some of them can be perceivable by humans because of their aroma. They have a great influence on the decision making of consumers when they choose to use a product or not. In the case where a product has an offensive and strong aroma, many consumers might not appreciate it. On the contrary, soft and fresh natural aromas definitely increase the acceptance of a given product. These properties can drastically influence the economy; thus, it has been of great importance to manufacturers that the aroma of their food product is characterized by analytical means to provide a basis for further optimization processes. A lot of research has been devoted to this domain in order to link the quality of, e.g., a food to its aroma. By knowing the aromatic profile of a food, one can understand the nature of a given product leading to developing new products, which are more acceptable by consumers. There are two ways to analyze volatiles: one is to use human senses and/or sensory instruments, and the other is based on advanced analytical techniques. This work focuses on the latter. Although requirements are simple, low-cost technology is an attractive research target in this domain; most of the data are generated with very high-resolution analytical instruments. Such data gathered based on different analytical instruments normally have broad, overlapping sensitivity profiles and require substantial data analysis. In this review, we have addressed not only the question of the application of chemometrics for aroma analysis but also of the use of different analytical instruments in this field, highlighting the research needed for future focus.

Resolving the chemical heterogeneity of natural organic matter: new insights from comprehensive two-dimensional liquid chromatography

For the purpose of resolving the chemical heterogeneity of natural organic matter (NOM), comprehensive two-dimensional liquid chromatography (LC×LC) was employed for the first time to map the hydrophobicity versus molecular weight (MW) distribution of two well-known complex organic mixtures: Suwannee River Fulvic Acids (SR-FA) and Pony Lake Fulvic Acids (PL-FA). Two methods have been developed using either a conventional reversed-phase (RP) silica column or a mixed-mode hydrophilic interaction column operating under aqueous RP mode in the first dimension, and a size-exclusion column in the second dimension. The LC×LC fractions were screened on-line by UV at 254 nm, molecular fluorescence at excitation/emission wavelengths (λ(Exc)/λ(Em)) of 240/450 nm, and by evaporative light scattering. The MW distributions of these two NOM samples were further characterized by number (Mn) and weight (Mw) average MW, and by polydispersity (Mw/Mn). Findings suggest that the combination of two independent separation mechanisms is promising in extend the range of NOM separation. For the cases where NOM separation was accomplished, smaller Mw group fractions seem to be related to a more hydrophobic nature. Regardless of the detection method, the complete range of MW distribution provided by both comprehensive LC×LC methods was found to be lower than those reported in the literature.

Guidelines for bioanalytical 2D chromatography method development and implementation

2D chromatography is a rapidly evolving, very powerful tool for bioanalysis. Advances in the theory of 2D separations, instrument technology and data analysis strategies continue to complement each other and advance the state of the art. Separations of complex mixtures of biomolecules yielding several hundred peaks in practical analysis times (tens of minutes to several hours) are relatively common. However, this level of performance largely remains the domain of expert researchers and several practical limitations stand in the way of more widespread use of 2D separations among practitioners. While off-the-shelf instruments are increasing in number, the most effective 2D instruments are often home-built, and analysis of the extremely rich datasets resulting from these separations continues to be a serious bottleneck in the overall workflow. This review summarizes some of the most serious challenges in method development and describes best practices to help guide users in designing effective 2D separations for bioanalysis.