Quantitative and Predictive Modelling of Lipid Oxidation in Mayonnaise.

Food emulsions with high amounts of unsaturated fats, such as mayonnaise, are prone to lipid oxidation. In the food industry, typically accelerated shelf life tests are applied to assess the oxidative stability of different formulations. Here, the appearance of aldehydes at the so-called onset time, typically weeks, is considered a measure for oxidative stability of food emulsions, such as mayonnaise. To enable earlier assessment of compromised shelf-life, a predictive model for volatile off-flavor generation is developed. The model is based on the formation kinetics of hydroperoxides, which are early oxidation products and precursors of volatile aldehydes, responsible for off-flavor. Under accelerated shelf-life conditions (50 °C), hydroperoxide (LOOH) concentration over time shows a sigmoidal curvature followed by an acceleration phase that occurs at a LOOH-concentration between 38-50 mmol/kg, here interpreted as a critical LOOH concentration (CCLOOH). We hypothesize that the time at which CCLOOH was reached is related to the onset of aldehyde generation and that the characterization of the LOOH-generation curvature could be based on reaction kinetics in the first days. These hypotheses are tested using semi-empirical models to describe the autocatalytic character of hydroperoxide formation in combination with the CCLOOH. The Foubert function is selected as best describing the LOOH-curvature and is hence used to accurately predict onset of aldehyde generation, in most cases within several days of shelf-life. Furthermore, we find that the defining parameters of this model could be used to recognize antioxidant mechanisms at play.

Quantification of food polysaccharide mixtures by 1H NMR.

Polysaccharides are food ingredients that critically determine rheological properties and shelf life. A qualitative and quantitative assessment on food-specific polysaccharide mixtures by 1H NMR is presented. The method is based on the identification of intact polysaccharides, combined with a quantitative analysis of their monosaccharide constituents. Identification of the polysaccharides is achieved by 1H NMR line shape fitting with pure compound spectra. The monomeric composition was determined using the Saeman hydrolysis procedure, followed by direct monosaccharide quantification by 1H NMR. In the quantification, both the monosaccharide degradation during hydrolysis, as well as a correction for the non-instantaneous polysaccharide dissolution were taken into account. These factors were particularly important for the quantification of pectins. The method showed overall good repeatability (RSDr=4.1±0.9%) and within-laboratory reproducibility (RSDR=6.1±1.4%) for various food polysaccharides. Polysaccharide mixtures were quantitatively resolved by a non-negative least squares estimation, using identified polysaccharides and their molar monosaccharide stoichiometry as prior knowledge. The accuracy and precision of the presented method make it applicable to a wide range of food polysaccharide mixtures with complex and overlapping 1H NMR spectra.

Strategy to identify and quantify polysaccharide gums in gelled food concentrates.

A strategy for the unambiguous identification and selective quantification of xanthan gum and locust bean gum (LBG) in gelled food concentrates is presented. DNA detection by polymerase chain reaction (PCR) showed to be a fast, sensitive, and selective method that can be used as a first screening tool in intact gelled food concentrates. An efficient isolation procedure is described removing components that may interfere with subsequent analyses. NMR spectroscopy enabled the direct identification of xanthan gum and the discrimination between different galactomannans in the isolated polysaccharide fraction. An enzymatic fingerprinting method using endo-ß-mannanase, in addition to being used to differentiate between galactomannans, was developed into a selective, quantitative method for LBG, whereas monosaccharide analysis was used to quantify xanthan gum. Recoveries for xanthan gum and LBG were 87% and 70%, respectively, with in-between day relative standard deviations below 20% for xanthan gum and below 10% for LBG.

A lipidomic analysis approach to evaluate the response to cholesterol-lowering food intake.

Plant sterols (PS) are well known to reduce serum levels of total cholesterol and LDL-cholesterol. Lipidomics potentially provides detailed information on a wide range of individual serum lipid metabolites, which may further add to our understanding of the biological effects of PS. In this study, lipidomics analysis was applied to serum samples from a placebo-controlled, parallel human intervention study (n = 97) of 4-week consumption of two PS-enriched, yoghurt drinks differing in fat content (based on 0.1% vs. 1.5% dairy fat). A comprehensive data analysis strategy was developed and implemented to assess and compare effects of two different PS-treatments and placebo treatment. The combination of univariate and multivariate data analysis approaches allowed to show significant effects of PS intake on the serum lipidome, and helped to distinguish them from fat content and non-specific effects. The PS-enriched 0.1% dairy fat yoghurt drink had a stronger impact on the lipidome than the 1.5% dairy fat yoghurt drink, despite similar LDL-cholesterol lowering effects. The PS-enriched 0.1% dairy fat yoghurt drink reduced levels of several sphingomyelins which correlated well with the reduction in LDL-cholesterol and can be explained by co-localization of sphingomyelins and cholesterol on the surface of LDL lipoprotein. Statistically significant reductions in serum levels of two lysophosphatidylcholines (LPC(16:1), LPC(20:1)) and cholesteryl arachidonate may suggest reduced inflammation and atherogenic potential. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0384-2) contains supplementary material, which is available to authorized users.

Impact of short-term intake of red wine and grape polyphenol extract on the human metabolome.

Red wine and grape polyphenols are considered to promote cardiovascular health and are involved in multiple biological functions. Their overall impact on the human metabolome is not known. Therefore, exogenous and endogenous metabolic effects were determined in fasting plasma and 24 h urine from healthy male adults consuming a mix of red wine and grape juice extracts (WGM) for 4 days in a placebo-controlled, crossover study. Syringic acid, 3-hydroxyhippuric acid, pyrogallol, 3-hydroxyphenylacetic acid, and 3-hydroxyphenylpropionic acid were confirmed as the strongest urinary markers of WGM intake. Overall, WGM had a mild impact on the endogenous metabolism. Most noticeable were changes in several amino acids deriving from tyrosine and tryptophan. Reductions in the microbial metabolites p-cresol sulfate and 3-indoxylsulfuric acid and increases in indole-3-lactic acid and nicotinic acid were observed in urine. In plasma, tyrosine was reduced. The results suggest that short-term intake of WGM altered microbial protein fermentation and/or amino acid metabolism.

Metabolic fate of polyphenols in the human superorganism.

Dietary polyphenols are components of many foods such as tea, fruit, and vegetables and are associated with several beneficial health effects although, so far, largely based on epidemiological studies. The intact forms of complex dietary polyphenols have limited bioavailability, with low circulating levels in plasma. A major part of the polyphenols persists in the colon, where the resident microbiota produce metabolites that can undergo further metabolism upon entering systemic circulation. Unraveling the complex metabolic fate of polyphenols in this human superorganism requires joint deployment of in vitro and humanized mouse models and human intervention trials. Within these systems, the variation in diversity and functionality of the colonic microbiota can increasingly be captured by rapidly developing microbiomics and metabolomics technologies. Furthermore, metabolomics is coming to grips with the large biological variation superimposed on relatively subtle effects of dietary interventions. In particular when metabolomics is deployed in conjunction with a longitudinal study design, quantitative nutrikinetic signatures can be obtained. These signatures can be used to define nutritional phenotypes with different kinetic characteristics for the bioconversion capacity for polyphenols. Bottom-up as well as top-down approaches need to be pursued to link gut microbial diversity to functionality in nutritional phenotypes and, ultimately, to bioactivity of polyphenols. This approach will pave the way for personalization of nutrition based on gut microbial functionality of individuals or populations.

Multivariate paired data analysis: multilevel PLSDA versus OPLSDA.

Metabolomics data obtained from (human) nutritional intervention studies can have a rather complex structure that depends on the underlying experimental design. In this paper we discuss the complex structure in data caused by a cross-over designed experiment. In such a design, each subject in the study population acts as his or her own control and makes the data paired. For a single univariate response a paired t-test or repeated measures ANOVA can be used to test the differences between the paired observations. The same principle holds for multivariate data. In the current paper we compare a method that exploits the paired data structure in cross-over multivariate data (multilevel PLSDA) with a method that is often used by default but that ignores the paired structure (OPLSDA). The results from both methods have been evaluated in a small simulated example as well as in a genuine data set from a cross-over designed nutritional metabolomics study. It is shown that exploiting the paired data structure underlying the cross-over design considerably improves the power and the interpretability of the multivariate solution. Furthermore, the multilevel approach provides complementary information about (I) the diversity and abundance of the treatment effects within the different (subsets of) subjects across the study population, and (II) the intrinsic differences between these study subjects.

The metabolic fate of red wine and grape juice polyphenols in humans assessed by metabolomics.

The metabolic impact of polyphenol-rich red wine and grape juice consumption in humans was studied using a metabolomics approach. Fifty-eight men and women participated in a placebo-controlled, double-crossover study in which they consumed during a period of 4 wk, either a polyphenol-rich 2:1 dry mix of red wine and red grape juice extracts (MIX) or only a grape juice extract (GJX). Twenty-four-hour urine samples were collected after each intervention. (1)H NMR spectroscopy was applied for global metabolite profiling, while GC-MS was used for focused profiling of urinary phenolic acids. Urine metabolic profiles after intake of both polyphenol-rich extracts were significantly differentiated from placebo using multilevel partial least squares discriminant analysis. A significant 35% increase in hippuric acid excretion (p<0.001) in urine was measured after the MIX consumption as) or only a red grape juice dry extract (GJX). 24-h urine samples were collected after each intervention. 1H-NMR spectroscopy was applied for global metabolite profiling, while gas chromatography-mass spectrometry (GC-MS) was used for focused profiling of urinary phenolic acids. Urine metabolic profiles after intake of both polyphenol-rich extracts were significantly differentiated from placebo using multilevel partial least squares discriminant analysis (ML-PLS-DA). A significant 35% increase in hippuric acid excretion (p<0.001) in urine was measured after the MIX consumption compared with placebo, whereas no change was found after GJX consumption. GC-MS-based metabolomics of urine allowed identification of 18 different phenolic acids, which were significantly elevated following intake of either extract. Syringic acid, 3- and 4-hydroxyhippuric acid and 4-hydroxymandelic acid were the strongest urinary markers for both extracts. MIX and GJX consumption had a slightly different effect on the excreted phenolic acid profile and on endogenous metabolite excretion, possibly reflecting their different polyphenol composition.

Phenotyping tea consumers by nutrikinetic analysis of polyphenolic end-metabolites.

An integration of metabolomics and pharmacokinetics (or nutrikinetics) is introduced as a concept to describe a human study population with different metabolic phenotypes following a nutritional intervention. The approach facilitates an unbiased analysis of the time-response of body fluid metabolites from crossover designed intervention trials without prior knowledge of the underlying metabolic pathways. The method is explained for the case of a human intervention study in which the nutrikinetic analysis of polyphenol-rich black tea consumption was performed in urine over a period of 48 h. First, multilevel PLS-DA analysis was applied to the urinary 1H NMR profiles to select the most differentiating biomarkers between the verum and placebo samples. Then, a one-compartment nutrikinetic model with first-order excretion, a lag time, and a baseline function was fitted to the time courses of these selected biomarkers. The nutrikinetic model used here fully exploits the crossover structure in the data by fitting the data from both the treatment period and the placebo period simultaneously. To demonstrate the procedure, a selected set of urinary biomarkers was used in the model fitting. These metabolites include hippuric acid, 4-hydroxyhippuric acid and 1,3-dihydroxyphenyl-2-O-sulfate and derived from microbial fermentation of polyphenols in the gut. Variations in urinary excretion between- and within the subjects were observed, and used to provide a phenotypic description of the test population.

Multilevel data analysis of a crossover designed human nutritional intervention study.

A new method is introduced for the analysis of 'omics' data derived from crossover designed drug or nutritional intervention studies. The method aims at finding systematic variations in metabolic profiles after a drug or nutritional challenge and takes advantage of the crossover design in the data. The method, which can be considered as a multivariate extension of a paired t test, generates different multivariate submodels for the between- and the within-subject variation in the data. A major advantage of this variation splitting is that each submodel can be analyzed separately without being confounded with the other variation sources. The power of the multilevel approach is demonstrated in a human nutritional intervention study which used NMR-based metabolomics to assess the metabolic impact of grape/wine extract consumption. The variations in the urine metabolic profiles are studied between and within the human subjects using the multilevel analysis. After variation splitting, multilevel PCA is used to investigate the experimental and biological differences between the subjects, whereas a multilevel PLS-DA model is used to reveal the net treatment effect within the subjects. The observed treatment effect is validated with cross model validation and permutations. It is shown that the statistical significance of the multilevel classification model ( p << 0.0002) is a major improvement compared to a ordinary PLS-DA model ( p = 0.058) without variation splitting. Finally, rank products are used to determine which NMR signals are most important in the multilevel classification model.

GC-MS methods for metabolic profiling of microbial fermentation products of dietary polyphenols in human and in vitro intervention studies.

Flavonoids, a subclass of polyphenols, are major constituents of many plant-based foods and beverages, including tea, wine and chocolate. Epidemiological studies have shown that a flavonoid-rich diet is associated with reduced risk of cardiovascular diseases. The majority of the flavonoids survive intact until they reach the colon where they are then extensively metabolized into smaller fragments. Here, we describe the development of GC-MS-based methods for the profiling of phenolic microbial fermentation products in urine, plasma, and fecal water. Furthermore, the methods are applicable for profiling products obtained from in vitro batch culture fermentation models. The methods incorporate enzymatic deconjugation, liquid-liquid extraction, derivatization, and subsequent analysis by GC-MS. At the level of individual compounds, the methods gave recoveries better than 80% with inter-day precision being better than 20%, depending on the matrix. Limits of detection were below 0.1 microg/ml for most phenolic acids. The newly developed methods were successfully applied to samples from human and in-vitro intervention trials, studying the metabolic impact of flavonoid intake. In conclusion, the methods presented are robust and generally applicable to diverse biological fluids. Its profiling character is useful to investigate on a large scale the gut microbiome-mediated bioavailability of flavonoids.