Gabriel Vivó-Truyols

Resolution assessment and performance of several organic modifiers in hybrid micellar liquid chromatography

The performance of four criteria that measure the elementary resolution (modified selectivity, modified RS, peak purity, and orthogonal valley-to-peak ratio) was critically assessed using as global resolution function, the product of elementary measurements. The peak purities and valley-to-peak criteria yielded the best description of the overall separation according to the shape of the resolution surfaces compared to the peak arrangements in the chromatograms, the capability of defining unambiguously the composition regions of complete resolution, and the resolution achieved in the predicted optimums. Peak purities were used to compare the effect of five organic modifiers (1-propanol, 1-butanol, 1-pentanol, acetonitrile and tetrahydrofuran) on the separation quality of micellar mobile phases of sodium dodecyl sulphate (SDS). Acetonitrile, a common solvent in reversed should read reversed-phase liquid chromatography but scarcely used in micellar liquid chromatography, allowed the most satisfactory resolution in an extensive composition region with very small overlapping and sufficiently low retention times. The enhanced resolution was produced by the improved selectivity, and larger efficiencies and asymmetries of the chromatographic peaks.

Selection of Column Dimensions and Gradient Conditions to Maximize the Peak-Production Rate in Comprehensive Off-Line Two-Dimensional Liquid Chromatography Using Monolithic Columns

The peak-production rate (peak capacity per unit time) in comprehensive off-line two-dimensional liquid chromatography (LC/x/LC) was optimized for the separation of peptides using poly(styrene-co-divinylbenzene) monolithic columns in the reversed-phase (RP) mode. A first-dimension ((1)D) separation was performed on a monolithic column operating at a pH of 8, followed by sequential analysis of all the (1)D fractions on a monolithic column operating at a pH of 2. To obtain the highest peak-production rate, effects of column length, gradient duration, and sampling time were examined. RP/x/RP was performed at undersampling conditions using a short 10 min (1)D gradient. The peak-production rate was highest using a 50 mm long (2)D column applying an 8-10 min (2)D gradient time and was almost a factor of two higher than when a 250 mm monolithic column was used. The best way to obtain a higher peak-production rate in off-line LC/x/LC proved to be an increase in the number of (1)D fractions collected. Increasing the (2)D gradient time was less effective. The potential of the optimized RP/x/RP method is demonstrated by analyzing proteomics samples of various complexities. Finally, the trade-off between peak capacity and analysis time is discussed in quantitative terms for both one-dimensional RP gradient-elution chromatography and the off-line two-dimensional (RP/x/RP) approach. At the conditions applied, the RP/x/RP approach provided a higher peak-production rate than the (1)D-LC approach when collecting three (1)D fractions, which corresponds to a total analysis time of 60 min.

Probability of failure of the watershed algorithm for peak detection in comprehensive two-dimensional chromatography

The watershed algorithm is the most common method used for peak detection and integration in two-dimensional chromatography. However, the retention time variability in the second dimension may render the algorithm to fail. A study calculating the probabilities of failure of the watershed algorithm was performed. The main objective was to calculate the maximum second-dimension retention time variability, Delta(2)t(R,crit), above which the algorithm fails. Several models to calculate Delta(2)t(R,crit) were developed and evaluated: (a) exact model; (b) simplified model and (c) simple-modified model. Model (c) gave the best performance and allowed to deduce an analytical expression for the probability of failure of the watershed algorithm as a function of experimental Delta(2)t(R), modulation time and peak width in the first and second dimensions. It could be demonstrated that the probability of failure of the watershed algorithm under normal conditions in GCxGC is around 15-20%. Small changes of Delta(2)t(R), modulation time and/or peak width in the first and second dimension could induce subtle changes in the probability of failure of the watershed algorithm. Theoretical equations were verified with experimental results from a diesel sample injected in GC x GC and were found to be in good agreement with the experiments.

Study on the performance of different types of three-dimensional chromatographic systems

The maximum achievable performance of possible types of three-dimensional chromatographic systems (LC×LC×LC) has been investigated. The Pareto-optimization approach was applied to establish a trade-off between three main objectives (total peak capacity, analysis time and dilution of the sample) and Pareto-front values were obtained. The performances of (x)LC×(x)LC×(x)LC (three-dimensional separation in space), (t)LC×(t)LC×(t)LC (three-dimensional separation in time) and the hybrid (x)LC×(x)LC×(t)LC system were compared mutually and with two-dimensional chromatographic systems. It was found that (x)LC×(x)LC×(x)LC performs best in terms of maximum achievable peak capacity in shortest analysis time. Based on current thin-layer-chromatography performance it should be possible to obtain a peak capacity of 50,000 within 20min. If contemporary column-packing standards can be upheld the achievable limit is approximately 50% higher. However, in an (x)LC×(x)LC×(x)LC chromatographic system analytes remain in the separation domain after the analysis, which complicates the detection. Use of an (x)LC×(x)LC×(t)LC system with elution in the last dimension alleviates the detection problem. The maximum achievable peak capacity in the same analysis time is lower for (x)LC×(x)LC×(t)LC than for (x)LC×(x)LC×(x)LC. Using the same (reasonable) length of the separation domain (e.g. a cube 200×200×200 mm) for both systems, it is possible to achieve peak capacities of 78,000 for (x)LC×(x)LC×(t)LC operated in the gradient mode, which is twice higher than for an (x)LC×(x)LC×(x)LC system. A three-dimensional (three-column) time-based (t)LC×(t)LC×(t)LC system does not greatly improve the performance of (t)LC×(t)LC in terms of (maximum) peak capacity and (minimum) analysis time. Dilution factors in (t)LC×(t)LC×(t)LC are very high. Decreasing the dilution has a detrimental influence on the peak capacity. The trade-off between these objectives is of crucial importance. The influence of several parameters (length of the separation domain, particle size, etc.) on the performance of chromatographic systems was investigated, optimal ranges were found.

Isotopic and elemental profiling of ammonium nitrate in forensic explosives investigations

Ammonium nitrate (AN) is frequently encountered in explosives in forensic casework. It is widely available as fertilizer and easy to implement in explosive devices, for example by mixing it with a fuel. Forensic profiling methods to determine whether material found on a crime scene and material retrieved from a suspect arise from the same source are becoming increasingly important. In this work, we have explored the possibility of using isotopic and elemental profiling to discriminate between different batches of AN. Variations within a production batch, between different batches from the same manufacturer, and between batches from different manufacturers were studied using a total of 103 samples from 19 different fertilizer manufacturers. Isotope-ratio mass spectrometry (IRMS) was used to analyze AN samples for their (15)N and (18)O isotopic composition. The trace-elemental composition of these samples was studied using inductively coupled plasma-mass spectrometry (ICP-MS). All samples were analyzed for the occurrence of 66 elements. 32 of these elements were useful for the differentiation of AN samples. These include magnesium (Mg), calcium (Ca), iron (Fe) and strontium (Sr). Samples with a similar elemental profile may be differentiated based on their isotopic composition. Linear discriminant analysis (LDA) was used to calculate likelihood ratios and demonstrated the power of combining elemental and isotopic profiling for discrimination between different sources of AN.

Probabilistic Model for Untargeted Peak Detection in LC–MS Using Bayesian Statistics

We introduce a novel Bayesian probabilistic peak detection algorithm for liquid chromatography-mass spectroscopy (LC-MS). The final probabilistic result allows the user to make a final decision about which points in a chromatogram are affected by a chromatographic peak and which ones are only affected by noise. The use of probabilities contrasts with the traditional method in which a binary answer is given, relying on a threshold. By contrast, with the Bayesian peak detection presented here, the values of probability can be further propagated into other preprocessing steps, which will increase (or decrease) the importance of chromatographic regions into the final results. The present work is based on the use of the statistical overlap theory of component overlap from Davis and Giddings (Davis, J. M.; Giddings, J. Anal. Chem. 1983, 55, 418-424) as prior probability in the Bayesian formulation. The algorithm was tested on LC-MS Orbitrap data and was able to successfully distinguish chemical noise from actual peaks without any data preprocessing.

Class-conditional feature modeling for ignitable liquid classification with substantial substrate contribution in fire debris analysis

Forensic chemical analysis of fire debris addresses the question of whether ignitable liquid residue is present in a sample and, if so, what type. Evidence evaluation regarding this question is complicated by interference from pyrolysis products of the substrate materials present in a fire. A method is developed to derive a set of class-conditional features for the evaluation of such complex samples. The use of a forensic reference collection allows characterization of the variation in complex mixtures of substrate materials and ignitable liquids even when the dominant feature is not specific to an ignitable liquid. Making use of a novel method for data imputation under complex mixing conditions, a distribution is modeled for the variation between pairs of samples containing similar ignitable liquid residues. Examining the covariance of variables within the different classes allows different weights to be placed on features more important in discerning the presence of a particular ignitable liquid residue. Performance of the method is evaluated using a database of total ion spectrum (TIS) measurements of ignitable liquid and fire debris samples. These measurements include 119 nominal masses measured by GC-MS and averaged across a chromatographic profile. Ignitable liquids are labeled using the American Society for Testing and Materials (ASTM) E1618 standard class definitions. Statistical analysis is performed in the class-conditional feature space wherein new forensic traces are represented based on their likeness to known samples contained in a forensic reference collection. The demonstrated method uses forensic reference data as the basis of probabilistic statements concerning the likelihood of the obtained analytical results given the presence of ignitable liquid residue of each of the ASTM classes (including a substrate only class). When prior probabilities of these classes can be assumed, these likelihoods can be connected to class probabilities. In order to compare the performance of this method to previous work, a uniform prior was assumed, resulting in an 81% accuracy for an independent test of 129 real burn samples.

Probabilistic peak detection for first-order chromatographic data

We present a novel algorithm for probabilistic peak detection in first-order chromatographic data. Unlike conventional methods that deliver a binary answer pertaining to the expected presence or absence of a chromatographic peak, our method calculates the probability of a point being affected by such a peak. The algorithm makes use of chromatographic information (i.e. the expected width of a single peak and the standard deviation of baseline noise). As prior information of the existence of a peak in a chromatographic run, we make use of the statistical overlap theory. We formulate an exhaustive set of mutually exclusive hypotheses concerning presence or absence of different peak configurations. These models are evaluated by fitting a segment of chromatographic data by least-squares. The evaluation of these competing hypotheses can be performed as a Bayesian inferential task. We outline the potential advantages of adopting this approach for peak detection and provide several examples of both improved performance and increased flexibility afforded by our approach.

Program for the interpretive optimization of two-dimensional resolution

The challenge of fully optimizing LC×LC separations is horrendous. Yet, it is essential to address this challenge if sophisticated LC×LC instruments are to be utilized to their full potential in an efficient manner. Currently, lengthy method development is a major obstacle to the proliferation of the technique, especially in industry. A program was developed for the rigorous optimization of LC×LC separations, using gradient-elution in both dimensions. The program establishes two linear retention models (one for each dimension) based on just two LC×LC experiments. It predicts LC×LC chromatograms using a simple van-Deemter model to generalize band-broadening. Various objectives (analysis time, resolution, orthogonality) can be implemented in a Pareto-optimization framework to establish the optimal conditions. The program was successfully applied to a separation of a complex mixture of 54 aged, authentic synthetic dyestuffs, separated by ion-exchange chromatography and ion pair chromatography. The main limitation experienced was the retention-time stability in the first (ion-exchange) dimension. Using the PIOTR program LC×LC method development can be greatly accelerated, typically from a few months to a few days.

Trend analysis of time-series data: A novel method for untargeted metabolite discovery

A new strategy for biomarker discovery is presented that uses time-series metabolomics data. Data sets from samples analysed at different time points after an intervention are searched for compounds that show a meaningful trend following the intervention. Obviously, this requires new data-analytical tools to distinguish such compounds from those showing only random variation. Two univariate methods, autocorrelation and curve-fitting, are used either as stand-alone methods or in combination to discover unknown metabolites in data sets originating from target-compound analysis. Both techniques reduce the long list of detected compounds in the kinetic sample set to include only those having a pre-defined interesting time profile. Thus, new metabolites may be discovered within data structures that are usually only used for target-compound analysis. The new strategy is tested on a sample set obtained from a gut fermentation study of a polyphenol-rich diet. For this study, the initial list of over 9000 potentially interesting features was reduced to less than 150, thus significantly reducing the expensive and time-consuming manual examination.