Véronique Cariou

Analysis of multiblock datasets using ComDim: Overview and extension to the analysis of (K + 1) datasets

ComDim analysis was designed to assess the relationships between individuals and variables within a multiblock setting where several variables, organized in blocks, are measured on the same individuals. An overview of this method is presented together with some of its properties. Furthermore, we discuss a new extension of the method of analysis to the case of (K+1) datasets. More precisely, the aim is to explore the relationships between a response dataset and K other datasets. An illustration of this latter strategy of analysis on the basis of a case study involving Time Domain ‐ Nuclear Magnetic Resonance data is outlined and the outcomes are compared with those of Multiblock Partial Least Squares regression.

Quadratic PLS1 regression revisited

Within the framework of nonlinear partial least squares (PLS), the quadratic PLS regression approach, involving both linear and quadratic terms in the criterion, is discussed. A new algorithm for the determination of the components is proposed, and its advantages over the original algorithm are outlined. The approach of analysis is illustrated on the basis of simulated and real data. Copyright

Breast Milk Lipidome Is Associated with Early Growth Trajectory in Preterm Infants

Human milk is recommended for feeding preterm infants. The current pilot study aims to determine whether breast-milk lipidome had any impact on the early growth-pattern of preterm infants fed their own mother’s milk. A prospective-monocentric-observational birth-cohort was established, enrolling 138 preterm infants, who received their own mother’s breast-milk throughout hospital stay. All infants were ranked according to the change in weight Z-score between birth and hospital discharge. Then, we selected infants who experienced “slower” (n = 15, −1.54 ± 0.42 Z-score) or “faster” (n = 11, −0.48 ± 0.19 Z-score) growth; as expected, although groups did not differ regarding gestational age, birth weight Z-score was lower in the “faster-growth” group (0.56 ± 0.72 vs. −1.59 ± 0.96). Liquid chromatography–mass spectrometry lipidomic signatures combined with multivariate analyses made it possible to identify breast-milk lipid species that allowed clear-cut discrimination between groups. Validation of the selected biomarkers was performed using multidimensional statistical, false-discovery-rate and ROC (Receiver Operating Characteristic) tools. Breast-milk associated with faster growth contained more medium-chain saturated fatty acid and sphingomyelin, dihomo-γ-linolenic acid (DGLA)-containing phosphethanolamine, and less oleic acid-containing triglyceride and DGLA-oxylipin. The ability of such biomarkers to predict early-growth was validated in presence of confounding clinical factors but remains to be ascertained in larger cohort studies.

Effect of Salt Reduction on Children's Acceptance of Bread: Salt-reduced breads liking by children…

Salt reduction is becoming a major concern for public authorities, especially in cereal products. As childhood is important for the development of healthy eating habits, this study aimed to formulate salt-reduced breads with satisfying sensory properties for children. Sourdough and an artisanal bread-making process were used to compensate the flavor loss due to salt reduction. French breads (FBs) made with sourdough and artisanal processing were compared with white breads (WBs). Two salt levels were applied (1.2 and 1.8 g /100 g flour). To determine their acceptability and characterization, the four breads were assessed (i) by an adult panel (n = 39) according to cohesiveness, overall odor intensity, overall aroma in the mouth and saltiness intensity and (ii) a panel of children (n = 100, aged 6 to 11 years) according to overall liking and saltiness intensity. Finally, consumption by children (n = 89, aged 6 to 11 years) was measured during school lunch to evaluate the acceptability of salt reduction in a real consumption context. Both formulation and salt level induced physical and sensory changes in breads perceived by adults. They described WB as less dense, cohesive, and aromatic but more odorant than FB. Saltiness differences were perceived by adults but not by children. Children showed a preference for the saltiest breads and the FB but these drivers of preference were not confirmed during consumption measurements. These results shed new light on how natural solutions to enhance the flavor of bread can reduce its salt level while maintaining acceptability. PRACTICAL APPLICATION Salt reduction in bread could be compensated by the use of sourdough and an artisanal bread-making process. These methods allow an improvement of the nutritional quality of breads while maintaining their acceptance by young consumers by favoring the development of appealing organoleptic characteristics (aroma, texture). These methods are natural, easy to implement, and could be adapted to other fermented products in order to improve their nutritional quality.

Hierarchical classification of microorganisms based on high-dimensional phenotypic data

The classification of microorganisms by high-dimensional phenotyping methods such as FTIR spectroscopy is often a complicated process due to the complexity of microbial phylogenetic taxonomy. A hierarchical structure developed for such data can often facilitate the classification analysis. The hierarchical tree structure can either be imposed to a given set of phenotypic data by integrating the phylogenetic taxonomic structure or set up by revealing the inherent clusters in the phenotypic data. In this study, we wanted to compare different approaches to hierarchical classification of microorganisms based on high-dimensional phenotypic data. A set of 19 different species of molds (filamentous fungi) obtained from the mycological strain collection of the Norwegian Veterinary Institute (Oslo, Norway) is used for the study. Hierarchical cluster analysis is performed for setting up the classification trees. Classification algorithms such as artificial neural networks (ANN), partial least-squared discriminant analysis and random forest (RF) are used and compared. The 2 methods ANN and RF outperformed all the other approaches even though they did not utilize predefined hierarchical structure. To our knowledge, the RF approach is used here for the first time to classify microorganisms by FTIR spectroscopy.

On the Use of Self-Organising Maps to Analyse Spectral Data

Self-organizing maps (SOM) have been widely used in different data analysis fields for both their clustering and visualisation properties. However, dealing with spectral data, artificial neural networks (ANN) have generally been applied within a supervised context rather than unsupervised one. In this chapter, we present how the use of self-organizing maps may help end-users to visualise spectral data. While representing the Kohonen map, external characteristics associated with spectra can be projected on the map. Dealing with high-dimensional data, a dimension reduction is proposed to provide synthetic representation of the map to the most relevant variables.

Comparison of three hypothesis testing approaches for the selection of the appropriate number of clusters of variables

Within the scope of cluster analysis of variables, the selection of the appropriate number of clusters is of paramount interest. The strategy of determination of the appropriate number of clusters adopted herein is based on a hypothesis testing approach. It consists in testing whether the variation of a partition quality criterion between two consecutive partitions is far removed from the expected variation under the null-hypothesis stipulating a lack of structure. Three hypothesis testing strategies are detailed and compared in the scope of clustering of variables: Gap, Weighted Gap and a statistic associated with CLV methodology. Finally, an illustration is presented based on data from a preference study.