Magnetic resonance in food science : the multivariate challenge

Intact Food Analysis by Means of HRMAS-NMR Spectroscopy
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She has 15 year's experience as a course leader in multivariate data analysis with The Unscrambler. He then moved to finance and the newsroom of Associated Press Television News where he was leading a team trying to decipher the masses of financial data generated every day during news gathering. He later moved to a post-doctoral position in Montpellier, France where he expanded his research in industrial chemometrics and Process Analytical Technologies.

During his time in Montpellier he successfully worked on large site to site NIR calibration transfers and on using Chemometric methods to remove the effect of process or recipe changes on in-line PAT method performance. During his time in GSK he worked on a multitude of products and processes including Oral Solid Dose and Respiratory products, PAT methods, regulatory submissions and continuous improvement projects. Recent research projects, based at the University of Cambridge focused largely on the identification of biomarkers of COPD and diabetes, with continuing emphasis on development of data analysis methods to aid this work.

Recent consultancy projects include multivariate statistical analysis of combined transciptomic and metabolomic data, tablet texture profiles, and axion potential data. She has 50 publications in peer reviewed journals. Hussain has a M. Sc in Applied Statistics, M. His strengths include analytical thinking and creative problem solving skills. Hideyuki Shinzawa, research fellow at National Institute of Advanced Industrial Science and Technology AIST , has actively carried out research and consulting activities in the areas of vibrational spectroscopy and multivariate data analysis.

Shinzawa holds a M. Sahni is a chemical engineer and received a Ph. He has 10 years experience in research and development both in industry and academia, dealing with multi variate analysis related to medical diagnostics.

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His expertise areas include analysis of data originating from gene expression technology, proteomics and bio-spectroscopy. He has held several positions including Manager Data Analysis Group, Diagenic ASA, Oslo , Norway to develop, evaluate and integrate statistical methods for analysis of gene expression data.

His current interest is the application of NIR spectroscopy to allow non-destructive, in-forest, rapid phenotyping of thousands of individuals within tree breeding trials to provide tree breeders with additional measures of wood quality other than growth and density to improve breeding selection. Jaco has 15 years extensive experience in research and laboratory management in the Agriculture, Biotechnology, Chemistry, Clinical Biochemistry, Food and Beverage and Pharmaceutical fields.

Jaco Minnaar founded Timmerman Analytical in with the main objective to promote the use of multivariate data analysis and design of experiments DoE in South African Academic, Research and Private Industries.

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Kim H. Still, currently used dietary biomarkers do not always correlate well with the nutrients or foods they are intended to indicate and they fail to reflect the complex matrix of an overall diet [ 3 ]. Providing accurate and reliable measurements of dietary exposure constitutes one of the most challenging problems in nutrition research today [ 7 ].

Using metabolomics, food-derived metabolites and the change in endogenous metabolites can be identified including amino acids, alkaloids, polyphenols and metabolites of microbial origin. However, further studies are needed to deepen the understanding of the food metabolome and to identify additional potential nutritional biomarkers for food items and complex diets.

Controlled dietary intervention studies, where true consumption can be monitored in a supervised fashion, provide an opportunity to investigate the metabolic response of different foods or diets using metabolomics. In clinical studies that use metabolomics to explore the response to different exposures, it is not self-explanatory what method to apply regarding data analysis. Multivariate methods based on projections to latent structures PLS with different extensions are frequently applied, and orthogonal projections to latent structures with discriminant analysis OPLS-DA has become a standard method in metabolomics [ 11 , 12 , 13 ].

These multivariate methods are in their standard form suitable only for independent data, as they do not by themselves separate within- from between-individual effects but rather the average effect between two or more sample classes [ 14 ]. However, clinical studies may entail sample dependency from repeated measures on the same investigated unit.

Using an independent test on dependent data generates less robust models and potentially both false positive and negative discriminatory metabolites. Using OPLS-EP the variation within and between subjects is separated and intrinsic differences in treatment effects between individuals can be identified [ 15 ]. However, a drawback lies in that only one measurement per treatment can be used and, consequently, averages must be used from repeated measurements on the same individual instead of concurrently examining all samples.

ANOVA decompositioning of multivariate data offers a means to subtract study factors from measured variables, thus providing the possibility to focus on the reproducible within-person effect i. This was performed in a nutritional cross-over intervention study with the aim to investigate the serum metabolic response to two isocaloric breakfast meals using 1 H NMR metabolomics.

We have previously described the urine metabolome of the same diet [ 18 ]. Before entering the study participants provided written informed consent. Screening included a three-day weighed-food diary a short lifestyle questionnaire that included questions regarding food and alcohol consumption, use of nicotine, drugs, herbal remedies and supplements and level of physical activity.

Body composition was measured with bioimpedance ImpediMed Bioimp Version 5. The CB consisted of orange juice, oat puffs with milk, and a rye bread sandwich with hard cheese and fresh tomato. The EHB consisted of orange juice, scrambled eggs, white beans in tomato sauce, fried pork loin, tomato and toasted white bread with orange marmalade.

The two breakfast meals had similar composition of protein, fat and carbohydrates. Two weeks before and during the intervention, study participants were asked to refrain from using dietary supplements and occasional medications. Volunteers did not have any other restrictions regarding food consumption. Volunteers were instructed to drink water for the evening meal, not eat anything further and only drink water before arriving to the test kitchen between During the intervention, volunteers noted health status, occasional medications, and exact time of evening meal together with water intake during the overnight fast.

Study design of clinical intervention, Monday evening to Friday lunch during two consecutive weeks. In total, samples were collected. For quality control three samples with pooled serum from four individuals in the dataset and three buffer samples were used on each 96 sample rack.

The acquisition time was 2. All data processing was performed with TopSpin 3. TSP-d 4 was used for referencing. The spectral widths were Chenomx NMR suite 8. In total peaks were integrated within chemical shift range of 0. PCA models were used to explore clustering patterns of observations, trends in the data and outliers.


Two samples were removed due to poor data quality. In total, samples were included in further data analysis. Two variables were removed from the data set imidazole pH indicator. In total, variables were included in the model for further analysis. However, OPLS-DA does not account for dependent data, such as repeated measures on the same individual which is generated in cross-over studies [ 22 , 23 ].

OPLS-Effect Projections EP is a newly developed multivariate analysis that considers pairwise dependent samples in cross-over studies [ 15 ]. In the OPLS-DA and OPLS-EP models, four additional, highly abundant signals from unidentified lipids were removed since they, as a consequence of using Pareto scaling, influenced the model merely on account of the magnitude of the peaks. Furthermore, the use of T2-filtered NMR experiment on water samples negated any identification of individual hydrophobic lipids and fatty acids.

Hence, lipid variables did not contribute to the biological understanding of the data and OPLS models were not discernably affected by the removal of these variables. The numbers of latent variables LV in the models were determined using cross validation and Q 2. Separation of classes and variables related to separation in the data according to classification of breakfast meals was evaluated using OPLS-DA. Prior to modeling, data were centered and Pareto scaled. Cross validation groups were set to 24 i.

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Thevuthasan, J. Hu, X. Wang, M. Food Anal Methods. Lindon, J. In response, the European Commission produced guidelines for the analysis of wines to detect frauds and irregularities, and to avert dangers that could arise from the use of consumable products. Lea, and P.

The effect matrix, i. Prior to modeling in Simca, all data were Pareto scaled ParN but not centered, and cross validation groups were set to 7 default.

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S-plot was used for selection of variables of interest for annotation. Multilevel methods, such as OPLS-EP, are capable of managing sample dependency in a cross-over design, but are in this case limited to comparing mean values between treatments.

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Following the ANOVA-PLS approach [ 17 ], residuals were then added back to the breakfast type data, and analyzed by PLS to investigate systematic differences in the metabolome as a consequence of breakfast type. This supervised model was constructed in a repeated double cross validation procedure rdCV [ 24 , 25 ], and incorporated with unbiased variable selection obtained by recursive feature elimination in the inner rdCV loop [ 26 ].

All samples per individual were co-sampled into the same cross validation segments to avoid overfitting to dependent samples. This approach has previously proven successful for supervised multivariate modelling [ 27 , 28 ] to produce robust predictive modelling with effective variable selection and minimized risk of false positive discovery and model overfitting. These analyses were performed in R v. These calculations were performed in MatLab Ra. Model included variables and postprandial observations from 24 healthy volunteers. The magnitude of the predicted effect for each volunteer is given by the height of the corresponding black bar.

Model included 24 observations equal to number of individuals and variables. Labeled metabolites denote selected discriminating metabolites in the different models. In total serum samples from 24 individuals were included in the model, 90 samples from volunteers who had consumed the EHB and 92 samples from volunteers who had consumed the CB. On average four samples per individual and breakfast meal.

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PDF | The scope of applications of magnetic resonance to food science continues to expand. Recently, the focus has turned to the way in which the interpretation. Magnetic Resonance in Food Science: The Multivariate Challenge is ideal for graduates and researchers as well as for academics and.

Further, the influence of a single meal on the postprandial metabolic profile the following day was studied by the difference in metabolic profiles between the same breakfast meal in relation to the breakfast meal served the day before. In total, seven unique metabolites were identified that were selected responsible for class separation in all models. Tyrosine, proline, and N-acetylated amino acid were found in significantly higher concentrations after consumption of the CB.

In contrast, alanine, methanol, creatine, and isoleucine were found in significantly higher concentrations after consumption of the EHB. The ANOVA-PLS model unbiasedly selected two components and 48 variables corresponding to 12 metabolites as top predictors driving the separation between classes with three and nine metabolites responsible for class separation in postprandial samples from the CB and the EHB respectively.

Moreover, the OPLS-DA model showed an overall difference in metabolic profiles between breakfast meals with a high degree of intra-individual similarity, although inter-individual variability was clearly discernible Fig. Similar to these findings, Lenz et al. Likely, the confounding effect of inter-individual variability depends on the effect size of the intervention.

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OPLS-EP takes pairwise sample dependency into account by modelling the effect matrix rather than the original data [ 15 ]. This confirms the confounding effect of inter-individual variability Fig. Although not shown here, it can be shown that multilevel approaches are special cases of ANOVA decomposition; the latter being the broader framework.

In fact, their relation is conceptually similar to the difference between a paired t-test and a classical ANOVA. This approach provides a means to investigate contributions of the factors to the total variance by comparing sums of squares. In our data, the factor Individual was by far the major contributor to systematic variability, although overshadowed by the residual variability. This clearly indicates between sample fluctuations as the major source of variability, although the source of such fluctuations was not investigated. It is likely, however, that such variability is composed of both biological variation in the individual, pre-analytical sample management and instrumental variability [ 31 ].

It should be noted that the sums-of-squares in the currently used function were calculated sequentially i. Type I sums-of-squares and are thus sensitive to the order of factors if the design is not balanced. Sensitivity analysis, however, revealed only minor effect of the order of factors on both sums-of-squares and modelling outcomes in the present case. This clearly shows the advantages of filtering out inter-individual variability prior to analysis to be able to focus on systematic differences between treatments. Using this approach, all samples were maintained in the analysis, leading to higher resolution in the multivariate model compared to OPLS-EP Fig.

This also provided an opportunity to investigate whether effects were robust even with residual variability from multiple samples. ANOVA-PLS thus effectively combined the best aspects of discriminant and standard multilevel such as Effect Projections analyses for analysing complex cross-over data structures.

Permutation analysis showed that the ANOVA-PLS was highly significant and devoid of overfitting Additional file 6 , since the permutation distribution median corresponded exactly to the expected value of 91 misclassifications for randomly permuted observations in a two-class problem. However, in our study the breakfast meals contained the same fat to carbohydrate ratio and this might explain why the postprandial response did not seem to be influenced by the type of breakfast consumed the day before. However, the effect was not captured in serum, and this is in line with findings in the present study.

The cross-over design has the advantage of comparing each individual to themselves after the two different breakfasts. Given the use of proper statistical tools factors such as age [ 34 ], gender [ 35 ], BMI [ 36 ], insulin sensitivity [ 37 ], habitual diet [ 38 ], and habitual sleep [ 39 ], have minimal impact on the results.