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

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

Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing

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

Review of small-angle coronagraphic techniques in the wake of ground-based second-generation adaptive optics systems

Small-angle coronagraphy is technically and scientifically appealing because it enables the use of smaller telescopes, allows covering wider wavelength ranges, and potentially increases the yield and completeness of circumstellar environment - exoplanets and disks - detection and characterization campaigns. However, opening up this new parameter space is challenging. Here we will review the four posts of high contrast imaging and their intricate interactions at very small angles (within the first 4 resolution elements from the star). The four posts are: choice of coronagraph, optimized wavefront control, observing strategy, and post-processing methods. After detailing each of the four foundations, we will present the lessons learned from the 10+ years of operations of zeroth and first-generation adaptive optics systems. We will then tentatively show how informative the current integration of second-generation adaptive optics system is, and which lessons can already be drawn from this fresh experience. Then, we will review the current state of the art, by presenting world record contrasts obtained in the framework of technological demonstrations for space-based exoplanet imaging and characterization mission concepts. Finally, we will conclude by emphasizing the importance of the cross-breeding between techniques developed for both ground-based and space-based projects, which is relevant for future high contrast imaging instruments and facilities in space or on the ground.Comment: 21 pages, 7 figure

Supervised and Penalized Baseline Correction

Spectroscopic measurements can show distorted spectral shapes arising from a mixture of absorbing and scattering contributions. These distortions (or baselines) often manifest themselves as non-constant offsets or low-frequency oscillations. As a result, these baselines can adversely affect analytical and quantitative results. Baseline correction is an umbrella term where one applies pre-processing methods to obtain baseline spectra (the unwanted distortions) and then remove the distortions by differencing. However, current state-of-the art baseline correction methods do not utilize analyte concentrations even if they are available, or even if they contribute significantly to the observed spectral variability. We examine a class of state-of-the-art methods (penalized baseline correction) and modify them such that they can accommodate a priori analyte concentrations such that prediction can be enhanced. Performance will be assessed on two near infra-red data sets across both classical penalized baseline correction methods (without analyte information) and modified penalized baseline correction methods (leveraging analyte information).Comment: 27 pages; 9 figures; 2 tables; fixed typos; additional sanity checks for grammar and syntax; streamlined text and made minor cosmetic change