Multivariate Curve Resolution applied to Ion Mobility Spectra

Projecte final de Màster Oficial realitzat en col.laboració amb Universitat de Barcelona. Departament d’Electrònica.English: In this work, a Multivariate Curve Resolution (MCR) with Alternating Least Squares (ALS) method is described and used to identify the concentrations of a two-component (ethanol and acetone) mixture analysed with an Ion Mobility Spectrometer. Results allow us to distinguish qualitatively both components at lower concentrations, whereas fail to detect ethanol at higher concentrations. The impossibility of detecting etanol at higher concentrations is caused by higher acetone’s proton affinity

Data processing for Life Sciences measurements with hyphenated Gas Chromatography-Ion Mobility Spectrometry

[eng] Recent progress in analytical chemistry instrumentation has increased the amount of data available for analysis. This progress has been encompassed by computational improvements, that have enabled new possibilities to analyze larger amounts of data. These two factors have allowed to analyze more complex samples in multiple life science fields, such as biology, medicine, pharmacology, or food science. One of the techniques that has benefited from these improvements is Gas Chromatography - Ion Mobility Spectrometry (GC-IMS). This technique is useful for the detection of Volatile Organic Compounds (VOCs) in complex samples. Ion Mobility Spectrometry is an analytical technique for characterizing chemical substances based on the velocity of gas-phase ions in an electric field. It is able to detect trace levels of volatile chemicals reaching for some analytes ppb concentrations. While the instrument has moderate selectivity it is very fast in the analysis, as an ion mobility spectrum can be acquired in tenths of milliseconds. As it operates at ambient pressure, it is found not only as laboratory instrumentation but also in-site, to perform screening applications. For instance it is often used in airports for the detection of drugs and explosives. To enhance the selectivity of the IMS, especially for the analysis of complex samples, a gas chromatograph can be used for sample pre-separation at the expense of the length of the analysis. While there is better instrumentation and more computational power, better algorithms are still needed to exploit and extract all the information present in the samples. In particular, GC-IMS has not received much attention compared to other analytical techniques. In this work we address some of the data analysis issues for GC-IMS: With respect to the pre-processing, we explore several baseline estimation methods and we suggest a variation of Asymmetric Least Squares, a popular baseline estimation technique, that is able to cope with signals that present large peaks or large dynamic range. This baseline estimation method is used in Gas Chromatography - Mass Spectrometry signals as well, as it suits both techniques. Furthermore, we also characterize spectral misalignments in a several months long study, and propose an alignment method based on monotonic cubic splines for its correction. Based on the misalignment characterization we propose an optimal time span between consecutive calibrant samples. We the explore the usage of Multivariate Curve Resolution methods for the deconvolution of overlapped peaks and their extraction into pure components. We propose the use of a sliding window in the retention time axis to extract the pure components from smaller windows. The pure components are tracked through the windows. This approach is able to extract analytes with lower response with respect to MCR, compounds that have a low variance in the overall matrix Finally we apply some of these developments to real world applications, on a dataset for the prevention of fraud and quality control in the classification of olive oils, measured with GC-IMS, and on data for biomarker discovery of prostate cancer by analyzing the headspace of urine samples with a GC-MS instrument[cat] Els avenços recents en instrumentació química i el progrés en les capacitats computacionals obren noves possibilitats per l’anàlisi de dades provinents de diversos camps en l’àmbit de les ciències de la vida, com la biologia, la medicina o la ciència de l’alimentació. Una de les tècniques que s’ha beneficiat d’aquests avenços és la cromatografia de gasos – espectrometria de mobilitat d’ions (GC-IMS). Aquesta tècnica és útil per detectar compostos orgànics volàtils en mostres complexes. L’IMS és una tècnica analítica per caracteritzar substàncies químiques basada en la velocitat d’ions en fase gasosa en un camp elèctric, capaç de detectar traces d’alguns volàtils en concentracions de ppb ràpidament. Per augmentar-ne la selectivitat, un cromatògraf de gasos pot emprar-se per pre-separar la mostra, a expenses de la durada de l’anàlisi. Tot i disposar de millores en la instrumentació i més poder computacional, calen millors algoritmes per extreure tota la informació de les mostres. En particular, GC-IMS no ha rebut molta atenció en comparació amb altres tècniques analítiques. En aquest treball, tractem alguns problemes de l’anàlisi de dades de GC-IMS: Pel que fa al pre-processat, explorem algoritmes d’estimació de la línia de base i en proposem una millora, adaptada a les necessitats de l’instrument. Aquest algoritme també s’utilitza en mostres de cromatografia de gasos espectrometria de masses (GC-MS), en tant que s’adapta correctament a ambdues tècniques. Caracteritzem els desalineaments espectrals que es produeixen en un estudi de diversos mesos de durada, i proposem un mètode d’alineat basat en splines cúbics monotònics per a la seva correcció i un interval de temps òptim entre dues mostres calibrants. Explorem l’ús de mètodes de resolució multivariant de corbes (MCR) per a la deconvolució de pics solapats i la seva extracció en components purs. Proposem l’ús d’una finestra mòbil en el temps de retenció. Aquesta millora permet extreure més informació d’analits. Finalment utilitzem alguns d’aquests desenvolupaments a dues aplicacions: la prevenció de frau en la classificació d’olis d’oliva, mesurada amb GC-IMS i la cerca de biomarcadors de càncer de pròstata en volàtils de la orina, feta amb GC-MS

Synthesis using speaker adaptation from speech recognition DB

This paper deals with the creation of multiple voices from a Hidden Markov Model based speech synthesis system (HTS). More than 150 Catalan synthetic voices were built using Hidden Markov Models (HMM) and speaker adaptation techniques. Training data for building a Speaker-Independent (SI) model were selected from both a general purpose speech synthesis database (FestCat;) and a database design ed for training Automatic Speech Recognition (ASR) systems (Catalan SpeeCon database). The SpeeCon database was also used to adapt the SI model to different speakers. Using an ASR designed database for TTS purposes provided many different amateur voices, with few minutes of recordings not performed in studio conditions. This paper shows how speaker adaptation techniques provide the right tools to generate multiple voices with very few adaptation data. A subjective evaluation was carried out to assess the intelligibility and naturalness of the generated voices as well as the similarity of the adapted voices to both the original speaker and the average voice from the SI model.Peer ReviewedPostprint (published version

Recent work on the FESTCAT database for speech synthesis

This paper presents our work around the FESTCAT project, whose main goal was the development of voices for the Festival suite in Catalan. In the first year, we produced the corpus and the speech data needed for build 10 voices using the Clunits (unit selection) and the HTS (Markov models) methods. The resulting voices are freely available on the web page of the project and included in Linkat, a Catalan distribution of Linux. More recently, we have updated the voices using new versions of HTS, other technology (Multisyn) and we have produced a child voice. Furthermore, we have performed a prosodic labeling and analysis of the database using the break index labels proposed in the ToBI system aimed to improve the intonation of the synthetic speech.Postprint (published version

Adaptive Asymmetric Least Squares baseline estimation for analytical instruments

Automated signal processing in analytical instrumentation is today required for the analysis of highly complex biomedical samples. Baseline estimation techniques are often used to correct long term instrument contamination or degradation. They are essential for accurate peak area integration. Some methods approach the baseline estimation iteratively, trying to ignore peaks which do not belong to the baseline. The proposed method in this work consists of a modification of the Asymmetric Least Squares (ALS) baseline removal technique developed by Eilers and Boelens. The ALS technique suffers from bias in the presence of intense peaks (in relation to the noise level). This is typical of diverse instrumental techniques such as Gas Chromatography-Mass Spectrometry (GC-MS) or Gas Chromatography-Ion Mobility Spectrometry (GC-IMS). In this work, we propose a modification (named psalsa) to the asymmetry weights of the original ALS method in order to better reject large peaks above the baseline. Our method will be compared to several versions of the ALS algorithm using synthetic and real GC signals. Results show that our proposal improves previous versions being more robust to parameter variations and providing more accurate peak areas

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscop

Sliding window multi-curve resolution: application to gas chromatography - ion mobility spectrometry

Blind source separation (BSS) techniques aim to extract a set of source signals from a measured mixture in an unsupervised manner. In the chemical instrumentation domain source signals typically refer to time-varying analyte concentrations, while the measured mixture is the set of observed spectra. Several techniques exist to perform BSS on ion mobility spectrometry, being simple-to-use interactive self-modelling mixture analysis (SIMPLISMA) and multivariate curve resolution (MCR) the most commonly used. The addition of a multi-capillary gas chromatography column using the ion mobility spectrometer as detector has been proposed in the past to increase chemical resolution. Short chromatography times lead to high levels of co-elution, and ion mobility spectra are key to resolve them. For the first time, BSS techniques are used to deconvolve samples of the gas chromatography-ion mobility spectrometry tandem. We propose a method to extract spectra and concentration profiles based on the application of MCR in a sliding window. Our results provide clear concentration profiles and pure spectra, resolving peaks that were not detected by the conventional use of MCR. The proposed technique could also be applied to other hyphenated instruments with similar strong co-elutions

GCIMS: An R package for untargeted gas chromatography - Ion mobility spectrometry data processing

Gas-Chromatography coupled to Ion Mobility Spectrometry (GC-IMS) based metabolomics is an emerging technique for obtaining fast, reliable untargeted metabolic fingerprints of biofluids. The generated raw data is highly dimensional and complex, suffers from baseline problems, misalignments, long peak tails and strong non-linearities that must be corrected to extract chemically relevant features from samples. In this work, we present our GCIMS R package, which includes spectra loading, metadata handling, denoising, baseline correction, spectral and chromatographic alignment, peak detection, integration, and peak clustering to produce a peak table ready for multivariate data analysis. We discuss package design decisions, and, for illustration purposes, we show a case study of sex discrimination on the basis of the volatile compounds in urine samples. The GCIMS package provides a user-friendly workflow for non-code developers to process their raw data samples

Alpsnmr: an r package for signal processing of fully untargeted nmr-based metabolomics

Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines