COMPARING GC×GC-TOFMS-BASED METABOLOMIC PROFILING AND WOOD ANATOMY FOR FORENSIC IDENTIFICATION OF FIVE MELIACEAE (MAHOGANY) SPECIES

Illegal logging and associated trade have increased worldwide. Such environmental crimes represent a major threat to forest ecosystems and society, causing distortions in market prices, economic instability, ecological deterioration, and poverty. To prevent illegal imports of forest products, there is a need to develop wood identification methods for identifying tree species regulated by the Convention on International Trade in Species of Wild Fauna andFlora in Trade (CITES) and other look-alike species. In this exploratory study, we applied metabolomic profiling of five species (Swietenia mahagoni, Swietenia macrophylla, Cedrela odorata, Khaya ivorensis, and Toona ciliata) using two-dimensional gas chromatog- raphy combined with time-of-flight mass spectrometry (GC3GC-TOFMS). We also performed qualitative, quantitative (based on the measurement of vessel area, tangential vessel lumina diameter,vessel element length, ray height, and ray width), and machine-vision aided (XyloTron) wood anatomy on a subsample of wood specimens to explore thepotential and limits of each approach. Fifty dried xylaria wood specimens were ground, extracted with methanol, and subsequently analyzed by GC3GC-TOFMS. In this study, the four genera could easily be identified using qualitative wood anatomy and chemical profiling. At the spe- cies level, Swietenia macrophylla and Swietenia mahagoni specimens were found to share many major metabolites and could only be differentiated after feature selection guided by cluster resolution (FS-CR) and visualization using Principal Component Analysis (PCA). Expectedly, specimens from the two Swiete- nia spp. could not be distinguished based on qualitative wood anatomy. However, significant differences in quantitative anatomical features were obtained for these two species. Excluding T. ciliata that was not included in the reference database of end grain images at the time of testing (2021), the XyloTron could successfully identify the majority of the specimens to the right genus and 50% of the specimens to the right species. The machine-vision tool was particularly successful at identifying Cedrela odorata samples, where all samples were correctly identified. Despite the limited number of specimens available for thisstudy, our preliminary results indicate that GC3GC-TOFMS-based metabolomic profiles could be used as comple- mentary method to differentiate CITES-regulated wood specimens at the genus and species levels.

Retos actuales de la farmacia

Retos actuales de la farmacia es un proyecto que está coordinado por Leobargo Manuel Gómez Oliván y un equipo de investigadores que forman parte del claustro de la Facultad de Química en el área de posgrado, ellos han incentivado el espíritu investigador y científico de los estudiantes adscritos al programa para adentrarse en el ámbito farmacéutico. Los capítulos que conforman esta edición son el reflejo de la actividad académica desarrollada en este posgrado en las diferentes áreas de acentuación que lo conforman: farmacia molecular, farmacia social y tecnología farmacéutica

Aplicabilidad de la cromatografía líquida y espectrometría vibracional para desarrollar modelos multivariantes para la detección y cuantificación de aceite de oliva en mezclas de aceites vegetales

Tesis Univ. Granada. Departamento de Química Analítica. Leída el 22 de febrero del 201

Region of Interest Selection for GC×GC-MS Data using a Pseudo Fisher Ratio Moving Window with Connected Components Segmentation

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) data present several challenges for analysis largely because chemical factors drift along the chromatographic modes across different chromatographic runs, and there is frequently a lack of reliable molecular ion measurements with which to align data across multiple samples. Tensor decomposition techniques such as Parallel Factor Analysis (PARAFAC2/PARAFAC2×N) allow analysts to deconvolve closely eluting signals for quantitative and qualitative purposes. These techniques make relatively few assumptions about chromatographic peak shapes or the relative abundance of noise and allow for highly accurate representations of the underlying chemical phenomena using well-characterized and scrutinized principles of chemometrics. However, expert intervention and supervision is required to select appropriate Regions of Interest (ROI) and numbers of chemical components present in each ROI. We previously reported an automated ROI selection algorithm for GC-MS data in Giebelhaus et al. where we observed the ratio of the first and second eigenvalues within a moving window across the entire chromatogram. Here, we present an extension of this work to automatically detect ROIs in GC×GC-MS chromatograms, while making no assumptions about peak shape. First we calculate the probabilities of each acquisition being in a ROI, then apply connected components segmentation to discretize the regions of interest. For sparse chromatograms we found the algorithm detected spurious peaks. To address this, we implemented an iterative ROI selection process where we autoscaled the moving window to the standard deviation of the noise from the previous iteration. Using three user-defined parameters, we generated informative ROIs on a wide range of GC×GC-TOFMS chromatograms

Limits of Detection and Quantification in Comprehensive Multidimensional Separations. 1. A Theoretical Look

Comprehensive multidimensional separations (e.g., GC×GC, LC×LC, etc.) are increasingly popular tools for the analysis of complex samples, due to their many advantages, such as vastly increased peak capacity, and improvements in sensitivity. The most well-established of these techniques, GC×GC, has revolutionized analytical separations in fields as diverse as petroleum, environmental research, food and flavors, and metabolic profiling. Using multidimensional approaches, analytes can be quantified at levels substantially lower than those possible by one-dimensional techniques. However, it has also been shown that the modulation process introduces a new source of error to the measurement. In this work, we present the results of a study into the limits of quantification and detection (LOQ and LOD) in comprehensive multidimensional separations using GC×GC and the more popular “two-step” integration algorithm as an example. Simulation of chromatographic data permits precise control of relevant parameters of peak geometry and modulation phase. Results are expressed in terms of the dimensionless parameter of signal-to-noise ratio of the base peak (<i>S</i>/<i>N</i>_BP) making them transportable to any result where quantification is performed using a two-step algorithm. Based on these results, the LOD is found to depend upon the modulation ratio used for the experiment and vary between a <i>S</i>/<i>N</i>_BP of 10–17, while the LOQ depends on both the modulation ratio and the phase of the modulation for the peak and ranges from a <i>S</i>/<i>N</i>_BP of 10 to 50, depending on the circumstances

PARAFAC2×\timesN: Coupled Decomposition of Multi-modal Data with Drift in N Modes

Reliable analysis of comprehensive two-dimensional gas chromatography - time-of-flight mass spectrometry (GC$\times$GC-TOFMS) data is considered to be a major bottleneck for its widespread application. For multiple samples, GC$\times$GC-TOFMS data for specific chromatographic regions manifests as a 4th order tensor of I mass spectral acquisitions, J mass channels, K modulations, and L samples. Chromatographic drift is common along both the first-dimension (modulations), and along the second-dimension (mass spectral acquisitions), while drift along the mass channel and sample dimensions is for all practical purposes nonexistent. A number of solutions to handling GC$\times$GC-TOFMS data have been proposed: these involve reshaping the data to make it amenable to either 2nd order decomposition techniques based on Multivariate Curve Resolution (MCR), or 3rd order decomposition techniques such as Parallel Factor Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic drift along one mode, which has enabled its use for robust decomposition of multiple GC-MS experiments. Although extensible, it is not straightforward to implement a PARAFAC2 model that accounts for drift along multiple modes. In this submission, we demonstrate a new approach and a general theory for modelling data with drift along multiple modes, for applications in multidimensional chromatography with multivariate detection

Discriminating Extra Virgin Olive Oils from Common Edible Oils: Comparable Performance of PLS-DA Models Trained on Low-Field and High-Field 1H NMR Data

Olive oil, the oil derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or mislabel cheaper oils to increase profitability. These other edible oils can have chemical profiles similar to extra virgin olive oil but can cause allergies in sensitive individuals. Given these consequences, there is a need for methods to rapidly authenticate olive oils. Nuclear magnetic resonance (NMR) has been used for this purpose, as it requires minimal sample preparation and is non-destructive. By utilizing NMR spectra of the samples and machine learning models trained on known olive oil and edible oils, oil samples can be classified and authenticated. While high-field NMRs are commonly used due to their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate, for routine screening purposes. Low-field benchtop NMR presents an affordable alternative. Here, we compared the predictive performance of partial least squares discrimination analysis (PLS-DA) models trained on low-field 60 MHz benchtop 1H NMR and high-field 400 MHz 1H NMR spectra. We demonstrated that PLS-DA models trained on low-field spectra perform comparably to those trained on high-field spectra