Simultaneous Component Analysis Research Articles

Non-invasive VOCs detection to monitor the gut microbiota metabolism in-vitro

This work implemented a non-invasive volatile organic compounds (VOCs) monitoring approach to study how food components are metabolised by the gut microbiota in-vitro. The fermentability of a model food matrix rich in dietary fibre (oat bran), and a pure prebiotic (inulin), added to a minimal gut medium was compared by looking at global changes in the volatilome. The substrates were incubated with a stabilised human faecal inoculum over a 24-h period, and VOCs were monitored without interfering with biological processes. The fermentation was performed in nitrogen-filled vials, with controlled temperature, and tracked by automated headspace-solid-phase microextraction coupled with gas chromatography–mass spectrometry. To understand the molecular patterns over time, we applied a multivariate longitudinal statistical framework: repeated measurements—ANOVA simultaneous component analysis. The methodology was able to discriminate the studied groups by looking at VOCs temporal profiles. The volatilome showed a time-dependency that was more distinct after 12 h. Short to medium-chain fatty acids showed increased peak intensities, mainly for oat bran and for inulin, but with different kinetics. At the same time, alcohols, aldehydes, and esters showed distinct trends with discriminatory power. The proposed approach can be applied to study the intertwined pathways of gut microbiota food components interaction in-vitro.

Influence of regional and yearly weather patterns on multi-mycotoxin occurrence in Austrian wheat: a liquid chromatographic-tandem mass spectrometric and multivariate statistics approach.

Mycotoxin surveys play an essential role in our food safety system. The obtained occurrence data form the basis for the assessment of the exposure of humans and animals to these toxic fungal secondary metabolites. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become the gold standard for mycotoxin determination because it enables selective and sensitive multi-toxin analysis. Simultaneous determination of several hundreds of secondary fungal metabolites is feasible using this technique. In this study, we combined a targeted dilute-and-shoot LC-MS/MS-based multi-analyte approach with multivariate statistics for the analysis of Austrian wheat from two different years and different geographical origins. We quantified 47 secondary fungal metabolites, including regulated emerging and masked mycotoxins. The resulting multi-mycotoxin occurrence data were further analyzed using both multivariate and univariate statistics. Principal component analysis (PCA) and analysis of variance (ANOVA) simultaneous component analysis (ASCA) were employed to identify regional and yearly trends within the dataset and to quantify the variance in metabolite occurrence attributed to the different effects. In addition, secondary fungal metabolites significantly impacted by these factors were selected via ANOVA. Of the 47 secondary metabolites identified, 39 were affected by the year, region or a combined effect. Moreover, our findings show that 43 of the secondary fungal metabolites were significantly influenced by the weather conditions. The results presented in this study underline the added value of combining targeted LC-MS/MS with multivariate statistics for monitoring a broad spectrum of secondary fungal metabolites in food crops. Through multivariate statistics, trends associated with the year or region can be readily studied. The approach presented could pave the way for a better understanding of the impact of climate change on plant pathogenic fungi and its implications for food safety. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

On the complementary nature of ANOVA simultaneous component analysis (ASCA+) and Tucker3 tensor decompositions on designed multi‐way datasets

AbstractThe complementary nature of analysis of variance (ANOVA) Simultaneous Component Analysis (ASCA+) and Tucker3 tensor decompositions is demonstrated on designed datasets. We show how ASCA+ can be used to (a) identify statistically sufficient Tucker3 models; (b) identify statistically important triads making their interpretation easier; and (c) eliminate non‐significant triads making visualization and interpretation simpler. For multivariate datasets with an experimental design of at least two factors, the data matrix can be folded into a multi‐way tensor. ASCA+ can be used on the unfolded matrix, and Tucker3 modeling can be used on the folded matrix (tensor). Two novel strategies are reported to determine the statistical significance of Tucker3 models using a previously published dataset. A statistically sufficient model was created by adding factors to the Tucker3 model in a stepwise manner until no ASCA+ detectable structure was observed in the residuals. Bootstrap analysis of the Tucker3 model residuals was used to determine confidence intervals for the loadings and the individual elements of the core matrix and showed that 21 out of 63 core values of the 3 × 7 × 3 model were not significant at the 95% confidence level. Exploiting the mutual orthogonality of the 63 triads of the Tucker3 model, these 21 factors (triads) were removed from the model. An ASCA+ backward elimination strategy is reported to further simplify the Tucker3 3 × 7 × 3 model to 36 core values and associated triads. ASCA+ was also used to identify individual factors (triads) with selective responses on experimental factors A, B, or interactions, A × B, for improved model visualization and interpretation.

Investigation of Lactation Period and Technological Treatments on Mineral Composition and IR-Profiles of Donkey Milk by Chemometrics

Donkey milk represents an efficient substitute for human milk in infants’ diets being unlikely to cause allergic reactions. In this study, different donkey milks were collected at two lactation times (T0 and T1), subjected to freezing–thawing and freeze-drying, and analyzed by Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES) and ATR-FT-IR. The data collected on freeze–thaw (FT-) and reconstituted (R-)milks were investigated by ANOVA–Simultaneous Component Analysis (ASCA) and Principal Component Analysis (PCA). The following concentrations (µg/mL) for FT and R-milks, respectively, at T0, were found: Ca: 712 ± 71, 600 ± 72; Fe: 0.7 ± 0.3, 0.1 ± 0.1; K: 595 ± 49, 551 ± 59; Mg: 75 ± 5, 67 ± 4; Na: 117 ± 16, 114 ± 16; P: 403 ± 30, 404 ± 38; Zn: 1.6 ± 0.2, 1.6 ± 0.3. At T1, the concentrations (µg/mL for FT and R-milks, respectively) were: Ca: 692 ± 60, 583 ± 43; Fe: 0.13 ± 0.02, 0.13 ± 0.03; K: 641 ± 71, 574 ± 61; Mg: 72 ± 4, 63 ± 1; Na: 116 ± 9, 109 ± 8; P: 412 ± 30, 405 ± 24; Zn: 1.6 ± 0.3, 1.6 ± 0.3. ASCA demonstrated the treatment has a substantial effect, and PCA revealed that the largest quantities of metals, specifically Fe, Mg, and Ca for T0 and K, P, and Na for T1, are present in the FT-milk samples. The IR spectra of FT- and R-milks revealed no macroscopic changes among them or between lactation periods, indicating this technique may not suitably capture variability in lactation or conservation processes in donkey milk. Despite the relatively small sample size, this study offers insight on the mineral composition changes in donkey milk and emphasizes the significance of milk preprocessing and the lactation period on it.

Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA.

The intricate dependency structure of biological "omics" data, particularly those originating from longitudinal intervention studies with frequently sampled repeated measurements renders the analysis of such data challenging. The high-dimensionality, inter-relatedness of multiple outcomes, and heterogeneity in the studied systems all add to the difficulty in deriving meaningful information. In addition, the subtle differences in dynamics often deemed meaningful in nutritional intervention studies can be particularly challenging to quantify. In this work we demonstrate the use of quantitative longitudinal models within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to capture the dynamics in frequently sampled longitudinal data with multivariate outcomes. We illustrate the use of linear mixed models with polynomial and spline basis expansion of the time variable within RM-ASCA+ in order to quantify non-linear dynamics in a simulation study as well as in a metabolomics data set. We show that the proposed approach presents a convenient and interpretable way to systematically quantify and summarize multivariate outcomes in longitudinal studies while accounting for proper within subject dependency structures.

Redundancy analysis includes analysis of variance-simultaneous component analysis (ASCA) and outperforms its extensions

Chemometrics and statistical ecology share interest in the analysis of multivariate data. In ecology, unconstrained and constrained ordination are popular methods to analyze and visualize multivariate data, with principal component analysis (PCA) and redundancy analysis (RDA) as prototype methods. Constraints give more insight and power by focusing on the response of the variables to particular external predictors or experimental factors, after optional adjustment for covariates. In chemometrics, analysis of variance - simultaneous component analysis (ASCA) was proposed decades later, with particular emphasis on the multivariate main and interaction effects in factorial experiments. This paper shows the similarities and differences between ASCA, its extensions, and (partial) RDA, alias reduced-rank regression. ASCA and RDA (understood as a sequence of partial RDAs, just as ASCA uses a sequence of PCAs) are shown to be mathematically identical for equireplicated designed experiments. Differences appear with unequal replication. As a corollary we show that, with equal replication, a particularly attractive form of ASCA, which displays a main effect together with an interaction, is a special case of principal response curve analysis. RDA is a least-squares method and uses the optimal weights in the dimension reduction of the treatment effects, whereas ASCA extensions for unbalanced data use alternative, sub-optimal weights.

Primary Treatment Effects for High-Grade Serous Ovarian Carcinoma Evaluated by Changes in Serum Metabolites and Lipoproteins

High-grade serous ovarian carcinoma (HGSOC) is the most common and deadliest ovarian cancer subtype. Despite advances in treatment, the overall prognosis remains poor. Regardless of efforts to develop biomarkers to predict surgical outcome and recurrence risk and resistance, reproducible indicators are scarce. Exploring the complex tumor heterogeneity, serum profiling of metabolites and lipoprotein subfractions that reflect both systemic and local biological processes were utilized. Furthermore, the overall impact on the patient from the tumor and the treatment was investigated. The aim was to characterize the systemic metabolic effects of primary treatment in patients with advanced HGSOC. In total 28 metabolites and 112 lipoproteins were analyzed by nuclear magnetic resonance (NMR) spectroscopy in longitudinal serum samples (n = 112) from patients with advanced HGSOC (n = 24) from the IMPACT trial with linear mixed effect models and repeated measures ANOVA simultaneous component analysis. The serum profiling revealed treatment-induced changes in both lipoprotein subfractions and circulating metabolites. The development of a more atherogenic lipid profile throughout the treatment, which was more evident in patients with short time to recurrence, indicates an enhanced systemic inflammation and increased risk of cardiovascular disease after treatment. The findings suggest that treatment-induced changes in the metabolome reflect mechanisms behind the diversity in disease-related outcomes.

Miniaturized NIR Spectrometers in a Nutshell: Shining Light over Sources of Variance

The increasing portability and accessibility of miniaturized NIR spectrometers are promoting the spread of in-field and online applications. Alongside the successful outcomes, there are also several problems related to the acquisition strategies for each instrument and to experimental factors that can influence the collected signals. An insightful investigation of such factors is necessary and could lead to advancements in experimental set-up and data modelling. This work aimed to identify variation sources when using miniaturized NIR sensors and to propose a methodology to investigate such sources based on a multivariate method (ANOVA—Simultaneous Component Analysis) that considers the effects and interactions between them. Five different spectrometers were chosen for their different spectroscopic range and technical characteristics, and samples of worldwide interest were chosen as the case study. Comparing various portable sensors is interesting since results could significantly vary in the same application, justifying the idea that this kind of spectrometer is not to be treated as a general class of instruments.

Effect of Drying Technique on the Volatile Content of Ecuadorian Bulk and Fine-Flavor Cocoa.

Cocoa (Theobroma cacao L.) is one of the most widely consumed edible seeds in the world affected by on-farm processing. This study investigated the effect of different drying techniques, namely oven drying (OD), sun drying (SD), and a modification of sun drying using black plastic sheeting (SBPD), on the volatile profile of fine-flavor and bulk cocoa varieties analyzed by HS-SPME-GC-MS. A total of sixty-four volatile compounds were identified in fresh and dried cocoa. As expected, the volatile profile was clearly modified after the drying step, showing strong differences among cocoa varieties, this factor and its interaction with the drying technique having greater influence according to the ANOVA simultaneous component analysis. A principal component analysis revealed a close relationship between the volatile content of bulk cocoa samples dried by the OD and SD techniques, whereas slight volatile modifications were perceived among fine-flavor samples dried using the three different techniques under study. Overall, the results provide the basis for the potential application of the simplest inexpensive SBPD technique in order to accelerate the drying process of sun drying and produce cocoa with similar (fine-flavor cocoa) or improved (bulk cocoa) aromatic quality to that formed using the traditional SD or the small-scale OD.

Assessment of Variability Sources in Grape Ripening Parameters by Using FTIR and Multivariate Modelling.

The variability in grape ripening is associated with the fact that each grape berry undergoes its own biochemical processes. Traditional viticulture manages this by averaging the physicochemical values of hundreds of grapes to make decisions. However, to obtain accurate results it is necessary to evaluate the different sources of variability, so exhaustive sampling is essential. In this article, the factors "grape maturity over time" and "position of the grape" (both in the grapevine and in the bunch/cluster) were considered and studied by analyzing the grapes with a portable ATR-FTIR instrument and evaluating the spectra obtained with ANOVA-simultaneous component analysis (ASCA). Ripeness over time was the main factor affecting the characteristics of the grapes. Position in the vine and in the bunch (in that order) were also significantly important, and their effect on the grapes evolves over time. In addition, it was also possible to predict basic oenological parameters (TSS and pH with errors of 0.3 °Brix and 0.7, respectively). Finally, a quality control chart was built based on the spectra obtained in the optimal state of ripening, which could be used to decide which grapes are suitable for harvest.

Validating a novel tool for coding body worn camera footage of police‐community member interactions

AbstractObjectivesThis study aimed to validate a tool for coding police body‐worn camera (BWC) footage to measure the dynamics of police–community encounters, including items related to community members, officer performance, and situational outcomes.MethodsOver 1000 BWC videos were scored by five coders who participated in an iterative interrater reliability exercise to improve the agreement. Krippendorf's alphas and a multilevel simultaneous component analysis were used to assess interrater reliability and the component structure of ratings, respectively. Bootstrapped and multilevel exploratory and confirmatory factor analyses were conducted to ascertain any underlying dimensions supporting officer and community member behavior, and cluster analyses were performed to examine whether interactions could be grouped into coherent categories.ResultsThe lower bound of unweighted means of bootstrapped alphas ranged from 0.67 to 0.96 depending upon item type, and 21.60% of the total variance in raters was between‐subjects. Factor analyses revealed only a single factor (“defensiveness”) underlying four items related to hostility directed toward officers by community members. Cluster analyses described seven categories that were highly overlapping but conceptually plausible with cluster silhouette means ranging from −0.06 to 0.13.ConclusionsWe validated a collection of interval‐level coding metrics on a random sample of over 1000 interactions between police officers and community members recorded through BWCs. In doing so, we provide a roadmap for researchers and practitioners to effectively measure the dynamics of police–community encounters and officer performance within those encounters and a framework for addressing the validity and reliability of items in future studies involving the measurement of BWC footage.

Methodologies based on ASCA to elucidate the influence of a subprocess: Vinification as a case of study

AbstractIn food manufacturing and processing, food matrix complexity usually makes it difficult to detect unwanted subprocesses, which can impact the quality of the final product. In the case of wine alcoholic fermentation, the main process is the conversion of sugars into ethanol and carbon dioxide, but the presence of some unwanted microorganisms could lead to wine contamination by production of undesired minor compounds. In the study we present, an intentional contamination of the vinification process by the addition of acetic acid bacteria was studied using a portable Fourier transform infrared (FT‐IR) spectrometer. ANOVA simultaneous component analysis (ASCA) was used to unravel these minor variability sources. However, as the subprocess is two orders of magnitude lower in concentration than the main process, different methodologies were used to enhance the ASCA results, such as to select a specific spectral region related to acetic acid bacteria metabolism, to divide the process in time intervals related to the different phases, or to unfold the data matrix in different ways. In addition, spectral preprocessing was optimized to scale up small peaks related to the subprocess. Our results show that several methodologies to build ASCA models can be applied to emphasize and better characterize bacteria contamination subprocesses.

Saffron Characterization by a Multidisciplinary Approach

Saffron is a spice obtained from the drying process of the stigmas of the flower Crocus sativus Linnaeus. It is well known that the organoleptic characteristics of this spice are closely linked to the production area and harvesting year. The present work aims to evaluate whether saffron samples produced in different years and origins present sensibly different crocin profiles. To achieve this goal, 120 saffron samples were harvested between 2016 and 2020 in four different Italian areas. The crocins were analysed, identified, and quantified by high-performance liquid chromatography-electrospray-tandem mass spectrometry (HPLC-ESI-MS/MS) in multiple reaction monitoring mode (MRM). Subsequently, ANOVA-simultaneous component analysis (ASCA) was used to evaluate whether the origin and annuity significantly affected the composition of the crocins. ASCA confirmed the relevance of these effects. Eventually, soft independent modelling by class analogy (SIMCA) models were created for each of the four different origins. Mixtures of saffron from different areas were also prepared to test the robustness of the models. SIMCA provided satisfying results; in fact, models provided 100% sensitivity for three origins (Cascia, Sardinia, and Città della Pieve) on the external test set (48 samples) and 88% (sensitivity on the external test set) for the Spoleto class.

Variable-selection ANOVA Simultaneous Component Analysis (VASCA)

ANOVA Simultaneous Component Analysis (ASCA) is a popular method for the analysis of multivariate data yielded by designed experiments. Meaningful associations between factors/interactions of the experimental design and measured variables in the dataset are typically identified via significance testing, with permutation tests being the standard go-to choice. However, in settings with large numbers of variables, like omics (genomics, transcriptomics, proteomics and metabolomics) experiments, the 'holistic' testing approach of ASCA (all variables considered) often overlooks statistically significant effects encoded by only a few variables (biomarkers). We hereby propose Variable-selection ASCA (VASCA), a method that generalizes ASCA through variable selection, augmenting its statistical power without inflating the Type-I error risk. The method is evaluated with simulations and with a real dataset from a multi-omic clinical experiment. We show that VASCA is more powerful than both ASCA and the widely adopted false discovery rate controlling procedure; the latter is used as a benchmark for variable selection based on multiple significance testing. We further illustrate the usefulness of VASCA for exploratory data analysis in comparison to the popular partial least squares discriminant analysis method and its sparse counterpart. The code for VASCA is available in the MEDA Toolbox at https://github.com/josecamachop/MEDA-Toolbox (release v1.3). The simulation results and motivating example can be reproduced using the repository at https://github.com/josecamachop/VASCA/tree/v1.0.0 (DOI 10.5281/zenodo.7410623). Supplementary data are available at Bioinformatics online.

Simultaneous component analysis as an interesting preliminary data analysis method in GC-MS – An example of headspace screening of Polish grasses

Abstract70 species of grasses family (Poaceae), coming from genera: Agrostis, Alopecurus, Anthoxanthum, Apera, Arrhenatherum, Avena, Brachypodium, Briza, Bromus, Calamagrostis, Corynephorus, Cynosurus, Dactylis, Danthonia, Deschampsia, Digitaria, Echinochloa, Elymus, Eragrostis, Festuca, Glyceria, Helictotrichon, Hierochloe, Holcus, Hordeum, Koeleria, Leymus, Lolium, Milium, Molinia, Nardus, Panicum, Phalaris, Phleum, Phragmites, Poa, Saccharum and Setaria, collected mostly from natural stands in Poland during 2020 season, were subjected to GC-MS fingerprinting of headspace volatile fraction above dried material. Obtained mass spectrometry data were analyzed by means of principal component analysis (PCA) and hierarchical cluster analysis (HCA). Five species: Glyceria maxima (Hartm.) Holmb., Lolium multiflorum Lam., Hordeum jubatum L., Bromus tectorum L. and Bromus secalinus L. were identified as outliers, which is consistent with our earlier analysis by thin layer chromatography. These species deserve further look and their outliance is orthogonal to coumarin content, which was independently observed for odorant species of grasses.

ALASCA: An R package for longitudinal and cross-sectional analysis of multivariate data by ASCA-based methods.

The increasing availability of multivariate data within biomedical research calls for appropriate statistical methods that can describe and model complex relationships between variables. The extended ANOVA simultaneous component analysis (ASCA+) framework combines general linear models and principal component analysis (PCA) to decompose and visualize the separate effects of experimental factors. It has recently been demonstrated how linear mixed models can be included in the framework to analyze data from longitudinal experimental designs with repeated measurements (RM-ASCA+). The ALASCA package for R makes the ASCA+ framework accessible for general use and includes multiple methods for validation and visualization. The package is especially useful for longitudinal data and the ability to easily adjust for covariates is an important strength. This paper demonstrates how the ALASCA package can be applied to gain insights into multivariate data from interventional as well as observational designs. Publicly available data sets from four studies are used to demonstrate the methods available (proteomics, metabolomics, and transcriptomics).

Effect of high-fat diet and exercise in the morning or evening on lipid subfractions among men with overweight/obesity: a randomised trial

Abstract Background Although main lipid fractions in the blood, such as HDL and LDL, are associated with risk of cardiovascular disease (CVD), the risk associated with different subfractions of these particles varies. For example, small dense LDL particles are associated with significantly increased CVD risk while larger LDL particles are not. Purpose To investigate the effect of a high-fat diet on serum lipid subfractions in men with overweight/obesity and to determine whether morning or evening exercise affected the lipid subfraction profile. Methods In a three-armed randomised trial, 24 men (aged 36±4 years, body mass index 31.2±2.3 kg/m2) consumed a high-fat diet (HFD; 65% energy from fat) for 11 days. One group of participants (n=8, control) did not exercise, while one group (n=8) exercised in the morning (06:30 hours) and one group (n=8) in the evening (18:30 hours) on days 6–10. We measured the effect of HFD, and that of morning or evening exercise, on fasting circulating lipoprotein subclass profiles determined using nuclear magnetic resonance spectroscopy. We performed a principal component analysis and partial least squares discriminant analyses to investigate patterns of changes in lipid subfractions after 5 days of HFD and a repeated measures ANOVA simultaneous component analysis (RM-ASCA) to determine multivariate changes in lipoprotein profiles between the three groups. To determine univariate effects of HFD on lipoprotein subclasses, we used Benjamini-Hochberg-adjusted paired samples t-tests. We used linear mixed models to determine between-group differences in lipoprotein subfractions after exercise in the morning or evening, compared to no exercise. Results Lipid subfraction profiles changed substantially after 5 days of HFD, with significant changes in 31 of 100 subfraction variables (adjusted p-values <0.05). Total serum cholesterol in LDL increased from 113±26 mg/dL to 122±26 mg/dL after 5 days of HFD (adjusted p=0.039) due to increased cholesterol in larger LDL particles (LDL-2 and LDL-3, Figure 1). Exercise induced a systematic change in lipid subfraction profiles, with small overall differences between morning versus evening exercise. Morning and evening exercise reduced serum concentrations of 20% and 24% of fasting lipid subfraction variables, respectively, compared to the control group (Figure 2). Evening exercise reduced fasting cholesterol concentrations in three LDL subfractions by 20%, 26% and 86%, respectively, while morning exercise only reduced concentration in the largest LDL particles by 12% (adjusted p-values <0.05 compared to the control group for all). Discussion and conclusion We found a marked change in lipid subfraction profiles after only five days of HFD among men with overweight/obesity. Exercise, either in the morning or in the evening, induced a substantial change in lipid subfraction profile compared with no exercise during an HFD. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Novo Nordisk Foundation

Influence of measurement procedure on the use of a handheld NIR spectrophotometer

The development of portable NIR instruments facilitates widespread use among non-specialists. However, untrained operators may follow non-optimal measurement procedures. This work investigates how different factors in the measurement procedure influence the spectra of pig feed samples produced by SCiO, a handheld NIR. Measurement conditions were studied by means of Design of Experiments and evaluated with analysis of variance – simultaneous component analysis (ANOVA–SCA or ASCA). We quantified and visualized how measurement distance, angle, background lighting, the use of plastic lids and different devices interactively affect the resulting spectra. The samples could be distinguished with 100% accuracy with Partial Least Squares-Discriminant Analysis (PLS-DA) a scanning distance of 0.5 cm. Replication of the experiment with special attention to reproducing the conditions still lead to some differences, which highlights both the challenges in controlling conditions and the importance of considering them. Based on the results, generalizable guidelines for acceptance of spectra were proposed for this case study. Of main importance are performing measurements at distances of 0.5 cm or at least in an environment without background lighting. Overall, the provided guidelines for measurement conditions and a methodology to investigate this for other devices are a key enabler to spreading handheld spectrometry to a non-expert audience.

Breath response following a nutritional challenge monitored by secondary electrospray ionization high-resolution mass spectrometry

On-line breath analysis using secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS) is a sensitive method for biomarker discovery. The strengths of this technology have already been demonstrated in the clinical environment. For the first time, this study demonstrates the application of SESI-HRMS in the field of nutritional science using a standardized nutritional intervention, consisting of a high-energy shake (950 kcal, 8% protein, 35% sugar and 57% fat). Eleven subjects underwent the intervention on three separate days and their exhaled breath was monitored up to six hours postprandially. In addition, sampling was performed during equivalent fasting conditions for selected subjects. To estimate the impact of inter- and intra-individual variability, analysis of variance simultaneous component analysis was conducted, revealing that the inter-individual variability accounted for 30% of the data variation. To distinguish the effect of the intervention from fasting conditions, partial least squares discriminant analysis was performed. Candidate compound annotation was performed with pathway analysis and collision-induced dissociation (CID) experiments. Pathway analysis highlighted, among others, features associated with the metabolism of linoleate, butanoate and amino sugars. Tentative compounds annotated through CID measurements include fatty acids, amino acids, and amino acid derivatives, some of them likely derived from nutrients by the gut microbiome (e.g. propanoate, indoles), as well as organic acids from the Krebs cycle. Time-series clustering showed an overlap of observed kinetic trends with those reported previously in blood plasma.

Effects of sulfamethoxazole exposure on mussels (Mytilus galloprovincialis) metabolome using retrospective non-target high-resolution mass spectrometry and chemometric tools

In this work, the Regions of Interest-Multivariate Curve Resolution (ROIMCR) method is proposed for the analysis of non-target metabolomics data. Samples from marine mussels (Mytilus galloprovincialis) exposed to a sublethal concentration (10 μg/L) of sulfamethoxazole (SMX) during 4 days in different seasonal conditions (summer and winter) were analyzed by High-Performance Liquid Chromatography – High-Resolution Mass Spectrometry (HPLC-HRMS) to study the effect of their exposure to SMX and the different seasonal conditions. The Regions of Interest (ROI) procedure has been applied for data filtering, compression, preprocessing and storage steps. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is then applied to the previously MS ROI preprocessed data sets to resolve the elution profiles and spectral fingerprints of the chemical constituents of the analyzed samples. The peak areas of the elution profiles of the chemical constituents resolved by the combined ROIMCR procedure were analyzed by Principal Component Analysis (PCA) and samples were clustered according to their experimental seasonal and SMX exposure. The effects of the two investigated factors and of their interaction on the concentrations of the metabolites were statistically assessed by ANOVA simultaneous component analysis (ASCA). Both types of analyses, PCA clustering and ASCA, confirmed that the seasonal conditions (summer versus winter) produced larger effects than those produced by the exposure to SMX and by the interaction of these two factors. The concentration changes of 16 identified endogenous metabolites were validated individually using a Wilcoxon statistical test, which confirmed the presence of significant disturbances in the levels of some of these metabolites (free fatty acids, amino acids and nucleic acids), and indicated the possible alteration of six different biological pathways, affected by the investigated seasonal and SMX exposure factors.