ANOVA-simultaneous Component Analysis Research Articles

Perfluoroalkylated Substance Effects in Xenopus laevis A6 Kidney Epithelial Cells Determined by ATR-FTIR Spectroscopy and Chemometric Analysis.

The effects of four perfluoroalkylated substances (PFASs), namely, perfluorobutanesulfonate (PFBS), perfluorooctanoic acid (PFOA), perfluorooctanesulfonate (PFOS), and perfluorononanoic acid (PFNA) were assessed in Xenopus laevis A6 kidney epithelial cells by attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometric analysis. Principal component analysis–linear discriminant analysis (PCA-LDA) was used to visualize wavenumber-related alterations and ANOVA-simultaneous component analysis (ASCA) allowed data processing considering the underlying experimental design. Both analyses evidenced a higher impact of low-dose PFAS-treatments (10–9 M) on A6 cells forming monolayers, while there was a larger influence of high-dose PFAS-treatments (10–5 M) on A6 cells differentiated into dome structures. The observed dose–response PFAS-induced effects were to some extent related to their cytotoxicity: the EC50-values of most influential PFAS-treatments increased (PFOS < PFNA < PFOA ≪ PFBS), and higher-doses of these chemicals induced a larger impact. Major spectral alterations were mainly attributed to DNA/RNA, secondary protein structure, lipids, and fatty acids. Finally, PFOS and PFOA caused a decrease in A6 cell numbers compared to controls, whereas PFBS and PFNA did not significantly change cell population levels. Overall, this work highlights the ability of PFASs to alter A6 cells, whether forming monolayers or differentiated into dome structures, and the potential of PFOS and PFOA to induce cell death.

Metabolic Profile and Root Development of Hypericum perforatum L. In vitro Roots under Stress Conditions Due to Chitosan Treatment and Culture Time

The responses of Hypericum perforatum root cultures to chitosan elicitation had been investigated through 1H-NMR-based metabolomics associated with morpho-anatomical analyses. The root metabolome was influenced by two factors, i.e., time of culture (associated with biomass growth and related “overcrowding stress”) and chitosan elicitation. ANOVA simultaneous component analysis (ASCA) modeling showed that these factors act independently. In response to the increase of biomass density over time, a decrease in the synthesis of isoleucine, valine, pyruvate, methylamine, etanolamine, trigonelline, glutamine and fatty acids, and an increase in the synthesis of phenolic compounds, such as xanthones, epicatechin, gallic, and shikimic acid were observed. Among the xanthones, brasilixanthone B has been identified for the first time in chitosan-elicited root cultures of H. perforatum. Chitosan treatment associated to a slowdown of root biomass growth caused an increase in DMAPP and a decrease in stigmasterol, shikimic acid, and tryptophan levels. The histological analysis of chitosan-treated roots revealed a marked swelling of the root apex, mainly due to the hypertrophy of the first two sub-epidermal cell layers. In addition, periclinal divisions in hypertrophic cortical cells, resulting in an increase of cortical layers, were frequently observed. Most of the metabolic variations as well as the morpho-anatomical alterations occurred within 72 h from the elicitation, suggesting an early response of H. perforatum roots to chitosan elicitation. The obtained results improve the knowledge of the root responses to biotic stress and provide useful information to optimize the biotechnological production of plant compounds of industrial interest.

Identification of Analytical Factors Affecting Complex Proteomics Profiles Acquired in a Factorial Design Study with Analysis of Variance: Simultaneous Component Analysis.

Complex shotgun proteomics peptide profiles obtained in quantitative differential protein expression studies, such as in biomarker discovery, may be affected by multiple experimental factors. These preanalytical factors may affect the measured protein abundances which in turn influence the outcome of the associated statistical analysis and validation. It is therefore important to determine which factors influence the abundance of peptides in a complex proteomics experiment and to identify those peptides that are most influenced by these factors. In the current study we analyzed depleted human serum samples to evaluate experimental factors that may influence the resulting peptide profile such as the residence time in the autosampler at 4 °C, stopping or not stopping the trypsin digestion with acid, the type of blood collection tube, different hemolysis levels, differences in clotting times, the number of freeze-thaw cycles, and different trypsin/protein ratios. To this end we used a two-level fractional factorial design of resolution IV (2(IV)(7-3)). The design required analysis of 16 samples in which the main effects were not confounded by two-factor interactions. Data preprocessing using the Threshold Avoiding Proteomics Pipeline (Suits, F.; Hoekman, B.; Rosenling, T.; Bischoff, R.; Horvatovich, P. Anal. Chem. 2011, 83, 7786-7794, ref 1) produced a data-matrix containing quantitative information on 2,559 peaks. The intensity of the peaks was log-transformed, and peaks having intensities of a low t-test significance (p-value > 0.05) and a low absolute fold ratio (<2) between the two levels of each factor were removed. The remaining peaks were subjected to analysis of variance (ANOVA)-simultaneous component analysis (ASCA). Permutation tests were used to identify which of the preanalytical factors influenced the abundance of the measured peptides most significantly. The most important preanalytical factors affecting peptide intensity were (1) the hemolysis level, (2) stopping trypsin digestion with acid, and (3) the trypsin/protein ratio. This provides guidelines for the experimentalist to keep the ratio of trypsin/protein constant and to control the trypsin reaction by stopping it with acid at an accurately set pH. The hemolysis level cannot be controlled tightly as it depends on the status of a patient's blood (e.g., red blood cells are more fragile in patients undergoing chemotherapy) and the care with which blood was sampled (e.g., by avoiding shear stress). However, its level can be determined with a simple UV spectrophotometric measurement and samples with extreme levels or the peaks affected by hemolysis can be discarded from further analysis. The loadings of the ASCA model led to peptide peaks that were most affected by a given factor, for example, to hemoglobin-derived peptides in the case of the hemolysis level. Peak intensity differences for these peptides were assessed by means of extracted ion chromatograms confirming the results of the ASCA model.

Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait.

IntroductionPast studies on plant metabolomes have highlighted the influence of growing environments and varietal differences in variation of levels of metabolites yet there remains continued interest in evaluating the effect of genetic modification (GM).ObjectivesHere we test the hypothesis that metabolomics differences in grain from maize hybrids derived from a series of GM (NK603, herbicide tolerance) inbreds and corresponding negative segregants can arise from residual genetic variation associated with backcrossing and that the effect of insertion of the GM trait is negligible.MethodsFour NK603-positive and negative segregant inbred males were crossed with two different females (testers). The resultant hybrids, as well as conventional comparator hybrids, were then grown at three replicated field sites in Illinois, Minnesota, and Nebraska during the 2013 season. Metabolomics data acquisition using gas chromatography–time of flight-mass spectrometry (GC–TOF-MS) allowed the measurement of 367 unique metabolite features in harvested grain, of which 153 were identified with small molecule standards. Multivariate analyses of these data included multi-block principal component analysis and ANOVA-simultaneous component analysis. Univariate analyses of all 153 identified metabolites was conducted based on significance testing (α = 0.05), effect size evaluation (assessing magnitudes of differences), and variance component analysis.ResultsResults demonstrated that the largest effects on metabolomic variation were associated with different growing locations and the female tester. They further demonstrated that differences observed between GM and non-GM comparators, even in stringent tests utilizing near-isogenic positive and negative segregants, can simply reflect minor genomic differences associated with conventional back-crossing practices.ConclusionThe effect of GM on metabolomics variation was determined to be negligible and supports that there is no scientific rationale for prioritizing GM as a source of variation.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-016-1017-6) contains supplementary material, which is available to authorized users.

Dynamic Metabolic Disruption in Rats Perinatally Exposed to Low Doses of Bisphenol-A.

Along with the well-established effects on fertility and fecundity, perinatal exposure to endocrine disrupting chemicals, and notably to xeno-estrogens, is strongly suspected of modulating general metabolism. The metabolism of a perinatally exposed individual may be durably altered leading to a higher susceptibility of developing metabolic disorders such as obesity and diabetes; however, experimental designs involving the long term study of these dynamic changes in the metabolome raise novel challenges. 1H-NMR-based metabolomics was applied to study the effects of bisphenol-A (BPA, 0; 0.25; 2.5, 25 and 250 μg/kg BW/day) in rats exposed perinatally. Serum and liver samples of exposed animals were analyzed on days 21, 50, 90, 140 and 200 in order to explore whether maternal exposure to BPA alters metabolism. Partial Least Squares-Discriminant Analysis (PLS-DA) was independently applied to each time point, demonstrating a significant pair-wise discrimination for liver as well as serum samples at all time-points, and highlighting unequivocal metabolic shifts in rats perinatally exposed to BPA, including those exposed to lower doses. In BPA exposed animals, metabolism of glucose, lactate and fatty acids was modified over time. To further explore dynamic variation, ANOVA-Simultaneous Component Analysis (A-SCA) was used to separate data into blocks corresponding to the different sources of variation (Time, Dose and Time*Dose interaction). A-SCA enabled the demonstration of a dynamic, time/age dependent shift of serum metabolome throughout the rats’ lifetimes. Variables responsible for the discrimination between groups clearly indicate that BPA modulates energy metabolism, and suggest alterations of neurotransmitter signaling, the latter finding being compatible with the neurodevelopmental effect of this xenoestrogen. In conclusion, long lasting metabolic effects of BPA could be characterized over 200 days, despite physiological (and thus metabolic) changes connected with sexual maturation and aging.

AoV-PLS: a new method for the analysis of multivariate data depending on several factors

A new method for the analysis of a multivariate dataset depending on several factors is proposed. It is called AoV-PLS (Analysis of Variance-PLS). It is based on the decomposition of the dataset into the main effects, the interactions effects and possibly the residual matrix using a model akin to analysis of variance (ANOVA). Each effect is considered in turn and assessed through the use of a Partial Least Square regression (PLS-regression). The connection of AoV-PLS to competing methods such as ANOVA-PCA and ANOVA-Simultaneous Component Analysis (ASCA) is emphasized and these methods are compared on the basis of a dataset pertaining to metabolomics field.

Combined effects of reduced malaxation oxygen levels and storage time on extra-virgin olive oil volatiles investigated by a novel chemometric approach

Combined effects of oxygen level reduction in the malaxation headspace and storage time up to 6months on the volatile composition of a monovarietal extra-virgin olive oil (EVOO), obtained from cv. Carboncella olives, were investigated by applying a full factorial design approach (4 oxygen levels×4 storage times) on EVOOs extracted on an industrial scale in two mills, equipped with “two-phase” and “three-phase” centrifugation systems, respectively. The outcoming data were analysed by the chemometric technique called ANOVA-simultaneous component analysis (ASCA).Both reduction of oxygen malaxation levels and storage time significantly affected the volatile profile of the extracted EVOOs. Reduction of oxygen malaxation levels hindered the formation of lipoxygenase derived volatiles (hexanal, 1-hexanol, (Z)-2-hexenal, (E)-2-hexen-1-ol, (Z)-2-penten-1-ol, 2,4-hexadienals), whereas prolonged storage times were associated with increased levels of autoxidation products (octane, hexanal, C10 hydrocarbons) and other compounds that could originate from exogenous microbial activity (1-octen-3-ol, 6-methyl-5-hepten-2-one, benzaldehyde, methyl salicylate).

Scaling in ANOVA-simultaneous component analysis.

In omics research often high-dimensional data is collected according to an experimental design. Typically, the manipulations involved yield differential effects on subsets of variables. An effective approach to identify those effects is ANOVA-simultaneous component analysis (ASCA), which combines analysis of variance with principal component analysis. So far, pre-treatment in ASCA received hardly any attention, whereas its effects can be huge. In this paper, we describe various strategies for scaling, and identify a rational approach. We present the approaches in matrix algebra terms and illustrate them with an insightful simulated example. We show that scaling directly influences which data aspects are stressed in the analysis, and hence become apparent in the solution. Therefore, the cornerstone for proper scaling is to use a scaling factor that is free from the effect of interest. This implies that proper scaling depends on the effect(s) of interest, and that different types of scaling may be proper for the different effect matrices. We illustrate that different scaling approaches can greatly affect the ASCA interpretation with a real-life example from nutritional research. The principle that scaling factors should be free from the effect of interest generalizes to other statistical methods that involve scaling, as classification methods.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-015-0785-8) contains supplementary material, which is available to authorized users.

1H NMR-based urinary metabolic profiling reveals changes in nicotinamide pathway intermediates due to postnatal stress model in rat.

The maternal separation protocol in rodents is a widely recognized model of early life stress allowing acute and chronic physiological consequences to be studied. An (1)H NMR-based metabolomic approach was applied to urines to evaluate the systemic metabolic consequences of maternal separation stress in female rats after the beginning of weaning and 4 weeks later when the rats were reaching adulthood. Furthermore, because maternal separation is considered as a model mimicking the inflammatory bowel syndrome, the lactulose/mannitol test was used to evaluate the influence of postnatal maternal separation on gut permeability and mucosal barrier function by (1)H NMR spectroscopy analysis of urine. The results showed no statistical differences in gut permeability due to maternal separation. The application of ANOVA simultaneous component analysis allowed the contributions of physiological adaptations to the animal's development to be separated from the metabolic consequences due to postnatal stress. Systemic metabolic differences in the maternally separated pups were mainly due to the tryptophan/NAD pathway intermediate levels and to the methyladenosine level. Urinary NMR-based metabolic profiling allowed us to disentangle the metabolic adaptive response of the rats to postnatal stress during the animal's growth, highlighting the metabolic changes induced by weaning, gut closure, and maturity.

Chilling-related cell damage of apple (Malus × domestica Borkh.) fruit cortical tissue impacts antioxidant, lipid and phenolic metabolism.

'Soggy breakdown' (SB) is an internal flesh disorder of 'Honeycrisp' apple (Malus × domestica Borkh.) fruit that occurs during low temperature storage. The disorder is a chilling injury (CI) in which visible symptoms typically appear after several weeks of storage, but information about the underlying metabolism associated with its induction and development is lacking. The metabolic profile of flesh tissue from wholly healthy fruit and brown and healthy tissues from fruit with SB was characterized using gas chromatography-mass spectrometry (GC-MS) and liquid chromatograph-mass spectrometry (LC-MS). Partial least squares discriminant analysis (PLS-DA) and correlation networks revealed correlation among ester volatile compounds by composition and differences in phytosterol, phenolic and putative triacylglycerides (TAGs) metabolism among the tissues. anova-simultaneous component analysis (ASCA) was used to test the significance of metabolic changes linked with tissue health status. ASCA-significant components included antioxidant compounds, TAGs, and phytosterol conjugates. Relative to entirely healthy tissues, elevated metabolite levels in symptomatic tissue included γ-amino butyric acid, glycerol, sitosteryl (6'-O-palmitoyl) β-d-glucoside and sitosteryl (6'-O-stearate) β-d-glucoside, and TAGs containing combinations of 16:0, 18:3, 18:2 and 18:1 fatty acids. Reduced metabolite levels in SB tissue included 5-caffeoyl quinate, β-carotene, catechin, epicatechin, α-tocopherol, violaxanthin and sitosteryl β-d glucoside. Pathway analysis indicated aspects of primary metabolism differed according to tissue condition, although differences in metabolites involved were more subtle than those of some secondary metabolites. The results implicate oxidative stress and membrane disruption processes in SB development and constitute a diagnostic metabolic profile for the disorder.

A non-targeted metabolomics approach to evaluate the effects of biomass growth and chitosan elicitation on primary and secondary metabolism of Hypericum perforatum in vitro roots

Hypericum perforatum L. is a medicinal plant commonly used worldwide for the treatment of mild and moderate depression due to its wide range of bioactive compounds. H. perforatum regenerated roots have been proposed as an efficacious in vitro system to biosynthesize pharmaceutically useful secondary metabolites. In the present study, a metabolomic platform, which integrates an nuclear magnetic resonance (NMR)-based metabolic profiling and analysis of variance-simultaneous component analysis (ASCA), has been applied in order to characterize the changes of the primary and secondary metabolism of H. perforatum regenerated roots induced by an achieved high biomass density in a confined growth environment or in response to chitosan treatment.The ASCA modelling applied to NMR-based metabolic profiling allowed to recognize the effects due to biomass growth rate changes and chitosan treatment. With an high biomass density, associated to a decelerating biomass growth rate, the levels of tryptophan, fructose, shikimic acid, and epicatechin increased, whereas γ-aminobutyric acid and histidine decreased. In response to chitosan elicitation, the biomass growth was arrested and valine, isoleucine, glutamine, γ-aminobutyric acid, fructose, sucrose, polyunsaturated fatty acids, epicatechin, xanthones, dimethylallyl-pyrophosphate, and stigmasterol levels increased, while histidine levels decreased. The metabolic profiling of regenerated roots shows how the cultures respond to different stress conditions: production of epicatechin in response to high biomass density and production of epicatechin, xanthones and isoprenoids in response to chitosan-treatment. This approach can be applied to define suitable protocols to produce the desired secondary metabolites with different bioactivities.

Lmdme: Linear Models on Designed Multivariate Experiments inR

The lmdme package decomposes analysis of variance (ANOVA) through linear models on designed multivariate experiments, allowing ANOVA-principal component analysis (APCA) and ANOVA-simultaneous component analysis (ASCA) in R. It also extends both methods with the application of partial least squares (PLS) through the specification of a desired output matrix. The package is freely available from Bioconductor and licensed under the GNU General Public License. ANOVA decomposition methods for designed multivariate experiments are becoming popular in “omics” experiments (transcriptomics, metabolomics, etc.), where measurements are performed according to a predefined experimental design, with several experimental factors or including subject-specific clinical covariates, such as those present in current clinical genomic studies. ANOVA-PCA and ASCA are well-suited methods for studying interaction patterns on multidimensional datasets. However, currently an R implementation of APCA is only available for Spectra data in the ChemoSpec package, whereas ASCA is based on average calculations on the indices of up to three design matrices. Thus, no statistical inference on estimated effects is provided. Moreover, ASCA is not available in an R package. Here, we present an R implementation for ANOVA decomposition with PCA/PLS analysis that allows the user to specify (through a flexible formula interface), almost any linear model with the associated inference on the estimated effects, as well as to display functions to explore results both of PCA and PLS. We describe the model, its implementation and two high-throughput microarray examples: one applied to interaction pattern analysis and the other to quality assessment.

Nutri-Metabolomics: Subtle Serum Metabolic Differences in Healthy Subjects by NMR-Based Metabolomics after a Short-Term Nutritional Intervention with Two Tomato Sauces

Postgenomics research and development is witnessing novel intersections of omics data intensive technology and applications in health and personalized nutrition. Chief among these is the nascent field of nutri-metabolomics that harnesses metabolomics platforms to discern person-to-person variations in nutritional responses. To this end, differences in the origin and ripening stage of fruits might have a strong impact on their phytochemical composition, and consequently, on their potential nutri-metabolomics effects on health. The objective of the present study was to evaluate the effects of a 4-week cross-over nutritional intervention on the metabolic status of 24 young healthy subjects. The intervention was carried out with two tomato sauces differing in their natural lycopene content, which was achieved by using tomatoes harvested at different times. Blood samples were drawn from each subject before and after each intervention period. Aqueous and lipid extracts from serum samples were analyzed by 1H-NMR metabolic profiling combined with analysis of variance simultaneous component analysis (ASCA) and multilevel simultaneous component analysis (MSCA). These methods allowed the interpretation of the variation induced by the main factors of the study design (sauce treatment and time). The levels of creatine, creatinine, leucine, choline, methionine, and acetate in aqueous extracts were increased after the intervention with the high-lycopene content sauce, while those of ascorbic acid, lactate, pyruvate, isoleucine, alanine were increased after the normal-lycopene content sauce. In conclusion, NMR-based metabolomics of aqueous and lipid extracts allowed the detection of different metabolic changes after the nutritional intervention. This outcome might partly be due to the different ripening state of the fruits used in production of the tomato sauces. The findings presented herein collectively attest to the emergence of the field of nutri-metabolomics as a novel subspecialty of postgenomics integrative biology.

Lactobacillus acidophilus La5 and Bifidobacterium lactis Bb12 Induce Different Age-Related Metabolic Profiles Revealed by 1H-NMR Spectroscopy in Urine and Feces of Mice1–3

Age-related dysbioses of intestinal microbiota and decline in the overall metabolic homeostasis are frequently found in the elderly. Probiotic supplementation may represent a way to prevent or reduce the senescence-associated metabolic disorders. The present study evaluated the metabolic impact of Lactobacillus acidophilus La5 and Bifidobacterium lactis Bb12 supplementation in relation to age by analyzing urine and feces metabolic profiles using 1H-nuclear magnetic resonance spectroscopy and multivariate analysis. Adult (3 mo old) and aged (16 mo old) mice received an oral supplementation of the 2 probiotics (1 × 109 colony-forming units/d each) or phosphate buffered saline (control) daily for 30 d. Urine and feces were collected for 48 h before the end of the study. Partial least squares–discriminant analysis showed that the urinary discriminant metabolites for the probiotic treatment included higher dimethylglycine in adult and aged mice, lower sarcosine and nicotinate in adult mice, higher N-methylnicotinamide in adult mice and lower N-methylnicotinamide in aged mice compared with their controls. These results indicate a probiotic-induced modulation of homocysteine and NAD metabolism pathways, which have important implications because these pathways are involved in essential cellular processes that can be altered in senescence. The probiotic supplementation also modified the fecal metabolic profiles, inducing in both adult and aged mice higher 4-hydroxyphenylacetate and lower xylose in treated mice compared with their control mice, whereas valerate was greater in treated adult mice and lower in treated aged mice compared with their controls. The ANOVA simultaneous component analysis on urinary and fecal metabolic profiling showed an age × treatment interaction (P < 0.05), confirming the age-related modulation of the metabolic response to probiotic supplementation. The results suggest that L. acidophilus and B. lactis may prevent or reduce age-related metabolic dysfunction.

Quantitative and predictive study of the evolution of wine quality parameters during high hydrostatic pressure processing

Abstract The evolution of main quality parameters of Nero D'avola Syrah red wine from the vintage of 2010 during high hydrostatic pressure (HHP) processing at various conditions was studied. ANOVA-simultaneous component analysis (ASCA) was used to interpret the effects of pressure (250–650 MPa) and pressure holding time (15–120 min) on the determined wine quality parameters. The results of ASCA indicated that both factors significantly (p Industrial relevance HHP at a high pressure can be used to modify the physicochemical properties of wine and subsequently affect their organoleptic properties for the wine industry. Moreover, the UV-vis spectroscopy can be used to determine the phenolic composition of wine rapidly during HHP processing.

A LC–MS metabolomics approach to investigate the effect of raw apple intake in the rat plasma metabolome

Fruit and vegetable consumption has been associated with several health benefits; however the mechanisms are largely unknown at the biochemical level. Our research aims to investigate whether plasma metabolome profiling can reflect biological effects after feeding rats with raw apple by using an untargeted UPLC–ESI–TOF–MS based metabolomics approach in both positive and negative mode. Eighty young male rats were randomised into groups receiving daily 0, 5 or 10 g fresh apple slices, respectively, for 13 weeks. During weeks 3–6 some of the animals were receiving 4 mg/ml 1,2-dimethylhydrazine dihydrochloride (DMH) once a week. Plasma samples were taken at the end of the intervention and among all groups, about half the animals were 12 h fasted. An initial ANOVA-simultaneous component analysis with a three-factor or two-factor design was employed in order to isolate potential metabolic variations related to the consumption of fresh apples. Partial least squares-discriminant analysis was then applied in order to select discriminative features between plasma metabolites in control versus apple fed rats and partial least squares modelling to reveal possible dose response. The findings indicate that in laboratory rats apple feeding may alter the microbial amino acid fermentation, lowering toxic metabolites from amino acids metabolism and increasing metabolism into more protective products. It may also delay lipid and amino acid catabolism, gluconeogenesis, affect other features of the transition from the postprandial to the fasting state and affect steroid metabolism by suppressing the plasma level of stress corticosteroids, certain mineralocorticoids and oxidised bile acid metabolites. Several new hypotheses regarding the cause of health effects from apple intake can be generated from this study for further testing in humans.

Exhaled breath profiling in diagnosing wheezy preschool children

Although wheeze is common in preschool children, the underlying pathophysiology has not yet been disentangled. Volatile organic compounds (VOCs) in exhaled breath may serve as noninvasive markers of early wheeze. We aimed to assess the feasibility of VOC collection in preschool children, and to study whether a VOC profile can differentiate between children with and without recurrent wheeze. We included children (mean (range) age 3.3 (1.9-4.5) yrs) with (n=202) and without (n=50) recurrent wheeze. Exhaled VOCs were analysed by gas chromatography-time-of-flight mass spectrometry. VOC profiles were generated by ANOVA simultaneous component analysis (ASCA) and sparse logistic regression (SLR). Exhaled breath collection was possible in 98% of the children. In total, 913 different VOCs were detected. The signal-to-noise ratio improved after correction for age, sex and season using ASCA pre-processing. An SLR model with 28 VOCs correctly classified 83% of the children (84% sensitivity, 80% specificity). After six-fold cross-validation, 73% were correctly classified (79% sensitivity, 50% specificity). Assessment of VOCs in exhaled breath is feasible in young children. VOC profiles are able to distinguish children with and without recurrent wheeze with a reasonable accuracy. This proof of principle paves the way for additional research on VOCs in preschool wheezing.

Use of multivariate chemometric algorithms on 1H NMR data to assess a soluble fiber (Plantago ovata husk) nutritional intervention

The study of nutritional interventions in humans is difficult to assess because the induced metabolic changes are lower than the natural biological variability between subjects. Due to its holistic approach, 1H NMR is one of the preferred technologies for this type of studies, even though it has a very low sensitivity. This work shows how the use of several chemometric algorithms on the measured data compensates for these drawbacks and allows the study of the effects of the nutritional intervention isolating them from the natural variability inherent to human studies. Mild to moderate hypercholesterolemic patients received either placebo or soluble fiber in a low saturated fat diet. Plasma samples were collected at week 0 and week 8. Spectra obtained with NMR equipment were processed with ANOVA simultaneous component analysis (ASCA). The application of clustering techniques revealed different responses based on the patient's basal state, which allowed the identification of responders from non-responders. Results showed a triglyceride level reduction of up to 15% (p=0.0032), with a higher reduction for those patients with a higher initial lipid profile. Moreover, line-shape fitting techniques applied to the NMR spectra allowed the conclusion that LDL (and VLDL) lipoprotein particles, and more noticeably triglycerides, moved to a profile configuration associated with lower cardiovascular risk. Results shed light on some of the metabolic modifications that husk fiber induces in humans which could not be seen with more conventional data analysis approaches. Our conclusion is that by using the right chemometric techniques it is possible to assess nutritional intervention effects in human NMR human studies despite the low sensitivity and selectivity that the technique offers today.

Application of near infrared (NIR) spectroscopy coupled to chemometrics for dried egg-pasta characterization and egg content quantification

Dried egg pasta is an important and traditional food in the Italian cuisine, and the eggs in pasta improve its nutritional value and organoleptic properties. For this reason the percentage of eggs present in the products sold as “egg pasta” has to always be clearly reported in the label. In this respect, the present research addresses the possibility of developing a method which would allow fast, simple and economic determination of egg content in dried egg-pasta, using near-infrared spectroscopy and chemometric analysis. However, as it is very likely that the spectroscopic fingerprint can also be affected by the manufacturing process of this product, in particular by drying temperature and time, the effect of the manufacturing process on the spectral profile of egg-pasta samples was thoroughly investigated, using experimental design coupled to a multivariate exploratory data analytical technique called ANOVA–Simultaneous Component Analysis (ASCA). Moreover, once confirmed the significance of the drying effect on spectral shape, with the aim of building a calibration model to quantify the egg content in pasta samples irrespective of the manufacturing protocol adopted, a non-linear approach based on local regression, namely LWR-PLS, was investigated. This method allowed the determination of the egg content in external validation samples with low error (RMSEP=1.25), resulting in predictions more accurate and precise than those obtained by a global PLS model.

Different metabolic and absorption patterns of betaine in response to dietary intake of whole-wheat grain, wheat aleurone or rye aleurone in catheterized pigs

In the present study, a novel method of measuring the uptake of nutrients by 1H NMR spectroscopy was performed to determine dietary effects. Six pigs catheterized in the portal vein and mesenteric artery were fed breads made from whole-wheat grain (WWG), wheat aleurone flour (WAF; 19 % aleurone of bread) or rye aleurone flour (RAF; 15 % aleurone of bread) in a repeated 3 × 3 crossover design. Three meals were provided daily (at 0, 5 and 10 h), and each period comprised of 1 week. Portal and arterial blood samples were collected at fasting (−30 min) on day 4–7, and on day 7, pooled blood samples were collected at 0–2.5, 2.5–5, 5–7.5 and 7.5–10 h after first (0 h) daily meal. Using the arterial-venous difference and ANOVA–simultaneous component analysis, plasma betaine was found to accumulate during the experimental period. On day 7, plasma betaine concentrations was 20–40 % higher than when feeding the WAF diet compared with RAF and WWG (P = 0.001), whereas the content of betaine in the WAF diet was about twice as high. The arterial-venous difference showed a tendency (P = 0.078) for the diet–time interaction of betaine absorption in the absorption phases but there was no diet effect (P = 0.31), which indicates a steady-state absorption has been reached on day 7. Furthermore, high plasma betaine levels correlated with low plasma creatine of endogenous origin. In conclusion, the explorative method revealed that the plasma concentration and absorption of betaine were differentially modified by the diets.