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Simple Summary: Colorectal cancer is the most frequent neoplasm in Western countries, the second most frequent neoplasm after breast cancer in women and the third most frequent neoplasm after prostate and lung cancer in men. Early diagnosis and disease screening are based on a fecal occult blood test, which, if positive, is complemented by colonoscopy. Currently, efforts are underway to find alternatives to the fecal occult blood test for various reasons. First, there is an ongoing attempt to increase the participation of the population to be screened. Second, there is a need to decrease the number of false positives to reduce the number of unnecessary colonoscopies. A urine test could be more widely accepted than a fecal test, and this is the scenario for which urinary metabolomics and volatilome studies are being developed. Our review provides the first exhaustive evaluation of metabolomics and volatilomics for the determination of colorectal cancer in urine. Abstract: To increase compliance with colorectal cancer screening programs and to reduce the recommended screening age, cheaper and easy non-invasiveness alternatives to the fecal immunochemical test should be provided. Following the PRISMA procedure of studies that evaluated the metabolome and volatilome signatures of colorectal cancer in human urine samples, an exhaustive search in PubMed, Web of Science, and Scopus found 28 studies that met the required criteria. There were no restrictions on the query for the type of study, leading to not only colorectal cancer samples versus control comparison but also polyps versus control and prospective studies of surgical effects, CRC staging and comparisons of CRC with other cancers. With this systematic review, we identified up to 244 compounds in urine samples (3 shared compounds between the volatilome and metabolome), and 10 of them were relevant in more than three articles. In the meta-analysis, nine studies met the criteria for inclusion, and the results combining the case-control and the pre-/post-surgery groups, eleven compounds were found to be relevant. Four upregulated metabolites were identified, 3-hydroxybutyric acid, L-dopa, L-histidinol, and N1, N12-diacetylspermine and seven downregulated compounds were identified, pyruvic acid, hydroquinone, tartaric acid, and hippuric acid as metabolites and butyraldehyde, ether, and 1,1,6-trimethyl-1,2-dihydronaphthalene as volatiles. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 798038

Mass spectrometry imaging (MSI) can reveal biochemical information directly from a tissue section. MSI generates a large quantity of complex spectral data which is still challenging to translate into relevant biochemical information. Here, we present rMSIproc, an open-source R package that implements a full data processing workflow for MSI experiments performed using TOF or FT-based mass spectrometers. The package provides a novel strategy for spectral alignment and recalibration, which allows to process multiple datasets simultaneously. This enables to perform a confident statistical analysis with multiple datasets from one or several experiments. rMSIproc is designed to work with files larger than the computer memory capacity and the algorithms are implemented using a multi-threading strategy. rMSIproc is a powerful tool able to take full advantage of modern computer systems to completely develop the whole MSI potential., This work has been supported by the Spanish Ministry of Economy and Competitiveness through project TEC2015-69076-P, PRs predoctoral grant no. BES-2013-065572, project BFU2017-89336-R, the Direcció General de Recerca of the Government of Catalonia through project 2017 SGR 1119 and IPT’s fellowship from the Spanish Ministry of Education, Culture and Sports grant no. FPU 14/04457.

HighlightsGC×GC-MS automated compound identification and quantification in metabolomics.Orthogonal signal deconvolution (OSD) for spectra deconvolution.PCA used as an alternative to the traditional least squares approach.OSD R package used for fast and automated compound identification. Comprehensive gas chromatography-mass spectrometry (GC×GC-MS) provides a different perspective in metabolomics profiling of samples. However, algorithms for GC×GC-MS data processing are needed in order to automatically process the data and extract the purest information about the compounds appearing in complex biological samples. This study shows the capability of independent component analysis-orthogonal signal deconvolution (ICA-OSD), an algorithm based on blind source separation and distributed in an R package called osd, to extract the spectra of the compounds appearing in GC×GC-MS chromatograms in an automated manner. We studied the performance of ICA-OSD by the quantification of 38 metabolites through a set of 20 Jurkat cell samples analyzed by GC×GC-MS. The quantification by ICA-OSD was compared with a supervised quantification by selective ions, and most of the R2 coefficients of determination were in good agreement (R2>0.90) while up to 24 cases exhibited an excellent linear relation (R2>0.95). We concluded that ICA-OSD can be used to resolve co-eluted compounds in GC×GC-MS.

Mass spectrometry imaging (MSI) is a molecular imaging technique that maps the distribution of molecules in biological tissues with high spatial resolution. The most widely used MSI modality is matrix-assisted laser desorption/ionization (MALDI), mainly due to the large variety of analyte classes amenable for MALDI analysis. However, the organic matrices used in classical MALDI may impact the quality of the molecular images due to limited lateral resolution and strong background noise in the low mass range, hindering its use in metabolomics. Here we present a matrix-free laser desorption/ionization (LDI) technique based on the deposition of gold nanolayers on tissue sections by means of sputter-coating. This gold coating method is quick, fully automated, reproducible, and allows growing highly controlled gold nanolayers, necessary for high quality and high resolution MS image acquisition. The performance of the developed method has been tested through the acquisition of MS images of brain tissues. The obtained spectra showed a high number of MS peaks in the low mass region (m/z below 1000 Da) with few background peaks, demonstrating the ability of the sputtered gold nanolayers of promoting the desorption/ionization of a wide range of metabolites. These results, together with the reliable MS spectrum calibration using gold peaks, make the developed method a valuable alternative for MSI applications.

Summary R platform provides some packages that are useful to process mass spectrometry imaging (MSI) data; however, none of them provide an easy to use graphical user interface (GUI). Here, we introduce rMSI, an R package for MSI data analysis focused on providing an efficient way to manage MSI data together with a GUI integrated in R environment. MS data is loaded in rMSI custom format optimized to minimize the memory footprint yet maintaining a fast spectra access. The rMSI GUI is designed for simple and effective data exploration and visualization. Moreover, rMSI is designed to be integrated in the R environment through a library of functions that can be used to share MS data across others R packages. The release of rMSI for R environment establishes a novel and flexible platform for MSI data analysis, completely free and open-source. Availability and implementation The code, the documentation, a tutorial and example data are available open-source at: github.com/prafols/rMSI. Contact jesus.brezmes@urv.cat. Supplementary information Supplementary data are available at Bioinformatics online.

Gas chromatography–mass spectrometry (GC–MS) produces large and complex datasets characterized by co-eluted compounds and at trace levels, and with a distinct compound ion-redundancy as a result of the high fragmentation by the electron impact ionization. Compounds in GC–MS can be resolved by taking advantage of the multivariate nature of GC–MS data by applying multivariate resolution methods. However, multivariate methods have to be applied in small regions of the chromatogram, and therefore chromatograms are segmented prior to the application of the algorithms. The automation of this segmentation process is a challenging task as it implies separating between informative data and noise from the chromatogram. This study demonstrates the capabilities of independent component analysis–orthogonal signal deconvolution (ICA–OSD) and multivariate curve resolution–alternating least squares (MCR–ALS) with an overlapping moving window implementation to avoid the typical hard chromatographic segmentation. Also, after being resolved, compounds are aligned across samples by an automated alignment algorithm. We evaluated the proposed methods through a quantitative analysis of GC–qTOF MS data from 25 serum samples. The quantitative performance of both moving window ICA–OSD and MCR–ALS-based implementations was compared with the quantification of 33 compounds by the XCMS package. Results shown that most of the R2 coefficients of determination exhibited a high correlation (R2 > 0.90) in both ICA–OSD and MCR–ALS moving window-based approaches.

Quality control of essential oils is an important topic in industrial processing of medicinal and aromatic plants. In this paper, the performance of Fuzzy Adaptive Resonant Theory Map (ARTMAP) and linear discriminant analysis (LDA) algorithms are compared in the specific task of quality classification of Rosa damascene essential oil samples (one of the most famous and valuable essential oils in the world) using an electronic nose (EN) system based on seven metal oxide semiconductor (MOS) sensors. First, with the aid of a GC-MS analysis, samples of Rosa damascene essential oils were classified into three different categories (low, middle, and high quality, classes C1, C2, and C3, respectively) based on the total percent of the most crucial qualitative compounds. An ad-hoc electronic nose (EN) system was implemented to sense the samples and acquire signals. Forty-nine features were extracted from the EN sensor matrix (seven parameters to describe each sensor curve response). The extracted features were ordered in relevance by the intra/inter variance criterion (Vr), also known as the Fisher discriminant. A leave-one-out cross validation technique was implemented for estimating the classification accuracy reached by both algorithms. Success rates were calculated using 10, 20, 30, and the entire selected features from the response of the sensor array. The results revealed a maximum classification accuracy of 99% when applying the Fuzzy ARTMAP algorithm and 82% for LDA, using the first 10 features in both cases. Further classification results explained that sub-optimal performance is likely to occur when all the response features are applied. It was found that an electronic nose system employing a Fuzzy ARTMAP classifier could become an accurate, easy, and inexpensive alternative tool for qualitative control in the production of Rosa damascene essential oil.

In this paper, the use of a new technique to obtain transient sensor information is introduced and its usefulness to improve the selectivity of metal oxide gas sensors is discussed. The method is based on modulating the flow of the carrier gas that brings the species to be measured into the sensor chamber. In such a way, the analytes’ concentration at the surface of the sensors is altered. As a result, reproducible patterns in the sensor response develop, which carry important information for helping the sensor system, not only to discriminate among the volatiles considered but also to semi-quantify them. This has been proved by extracting features from sensor dynamics using the discrete wavelet transform (DWT) and by building and validating support vector machine (SVM) classification models. The good results obtained (100% correct identification among 5 volatile compounds and nearly a 89% correct simultaneous identification and quantification of these volatiles), which clearly outperform those obtained when the steady-state response is used, prove the concept behind flow modulation.

The main goal of our study was to see whether an artificial olfactory system can be used as a nondestructive instrument to measure fruit maturity. In order to make an objective comparison, samples measured with our electronic nose prototype were later characterized using fruit quality techniques. The cultivars chosen for the study were peaches, nectarines, apples, and pears. With peaches and nectarines, a PCA analysis on the electronic nose measurements helped to guess optimal harvest dates that were in good agreement with the ones obtained with fruit quality techniques. A good correlation between sensor signals and some fruit quality indicators was also found. With pears, the study addressed the possibility of classifying samples regarding their ripeness state after different cold storage and shelf-life periods. A PCA analysis showed good separation between samples measured after a shelf-life period of seven days and samples with four or less days. Finally, the electronic nose monitored the shelf-life ripening of apples. A good correlation between electronic nose signals and firmness, starch index, and acidity parameters was found. These results prove that electronic noses have the potential of becoming a reliable instrument to assess fruit ripeness.

One of the main challenges in nuclear magnetic resonance (NMR) metabolomics is to obtain valuable metabolic information from large datasets of raw NMR spectra in a high throughput, automatic, and reproducible way. To date, established software packages used to match and quantify metabolites in NMR spectra remain mostly manually operated, leading to low resolution results and subject to inconsistencies not attributable to the NMR technique itself. Here, we introduce a new software package, called Dolphin, able to automatically quantify a set of target metabolites in multiple sample measurements using an approach based on 1D and 2D NMR techniques to overcome the inherent limitations of 1D (1)H-NMR spectra in metabolomics. Dolphin takes advantage of the 2D J-resolved NMR spectroscopy signal dispersion to avoid inconsistencies in signal position detection, enhancing the reliability and confidence in metabolite matching. Furthermore, in order to improve accuracy in quantification, Dolphin uses 2D NMR spectra to obtain additional information on all neighboring signals surrounding the target metabolite. We have compared the targeted profiling results of Dolphin, recorded from standard biological mixtures, with those of two well established approaches in NMR metabolomics. Overall, Dolphin produced more accurate results with the added advantage of being a fully automated and high throughput processing package.

High dimensionality is inherent to MS-based electronic nose applications where hundreds of variables per measurement (m/z fragments) - a significant number of them being highly correlated or noisy - are available. Feature selection is, therefore, an unavoidable pre-processing step if robust and parsimonious pattern classification models are to be developed. In this article, a new strategy for feature selection has been introduced and its good performance demonstrated using two MS e-nose databases. The feature selection is conducted in three steps. The first two steps are aimed at removing noisy, non-informative and highly collinear features (i.e., redundant), respectively. These two steps are computationally inexpensive and allow for dramatically reducing the number of variables (near 80% of initially available features are eliminated after the second step). The third step makes use of a stochastic variable selection method (simulated annealing) to further reduce the number of variables. For example, applying the method to an Iberian ham database has resulted in the number of features being reduced from 209 down to 14. Using the surviving m/z fragments, a fuzzy ARTMAP classifier was able to sort ham samples according to producer and quality (11-category classification) with a 97.24% success rate. The whole feature selection process runs in a few minutes in a Pentium IV PC platform. © 2006 Elsevier B.V. All rights reserved., This work was funded in part by CICYT under project no. TIC2003-06301, by the Thematic Network in Metabolism and Nutrition ref. C03/08 and by AECI under project no. 39/04/P/E

A new neural network paradigm based on fuzzy adaptive resonant theory, which yields quantitative results, is presented. The performance of this network in building calibration models for quantitative gas analysis is estimated and compared with other well-established methods. A faster training phase and higher accuracy in regression characterise the new paradigm.

Drift is one of the major problems experienced by gas analysers and electronic noses based on inexpensive metal oxide sensors. A novel drift counteraction technique is introduced, which allows for an on-line automated recalibration of the sensor system. The technique is demonstrated by discriminating three volatile compounds with 100% success rate using a sensor array.

A PSPICE model of quartz crystal microbalance (QCM) gas/odour sensors is presented which simulates the response of polymer-coated QCM devices to gases, taking into account the effects of ambient temperature and humidity. The model enables the associated drive circuitry to be rapidly prototyped and electronic nose equipment to be optimised.

DOI: 10.1007/s11306-017-1223-x https://link.springer.com/article/10.1007%2Fs11306-017-1223-x Filiació URV: SI Introduction: Current computational tools for gas chromatography—mass spectrometry (GC–MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual data reviewing. Metabolomics advent has fostered the development of public metabolite repositories containing mass spectra and retention indices, two orthogonal properties needed for metabolite identification. Such libraries can be used for library-driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC–MS non-targeted data analysis solutions that can eventually help to assess metabolite identities more efficiently. Results: This paper introduces Baitmet, an integrated open-source computational tool written in R enclosing a complete workflow to perform high-throughput library-driven GC–MS profiling in complex samples. Baitmet capabilities were assayed in a metabolomics study involving 182 human serum samples where a set of 61 metabolites were profiled given a reference library. Conclusions: Baitmet allows high-throughput and wide scope interrogation on the metabolic composition of complex samples analyzed using GC–MS via freely available spectral data. Baitmet is freely available at http://CRAN.R-project.org/package=baitmet.