Evelyne Vigneau

Effect of Apple Particle State on the Release of Volatile Compounds in a New Artificial Mouth Device

Varying the crushing parameters in a model mouth apparatus gave different crushed apple samples, which were compared to apples crushed in the human mouth by six people. An image analysis method was developed to measure the similarity between apple particles after crushing in the artificial mouth and in the human mouth. Thus, experimental conditions were determined that produced fruit in a state closest to that obtained after mastication in a human mouth. The influence of these different conditions on the quantity of released volatile compounds was then studied.

APPLICATION OF LATENT ROOT REGRESSION FOR CALIBRATION IN NEAR-INFRARED SPECTROSCOPY. COMPARISON WITH PRINCIPAL COMPONENT REGRESSION AND PARTIAL LEAST SQUARES

Abstract Several analytical applications of spectroscopy are based on the assessment of a linear model, linking laboratory values to spectral data. Among various procedures, the following three methods have been used, i.e. principal component regression (PCR), partial least squares (PLS) and latent root regression (LRR). These methods can be applied in order to tackle the high collinearity commonly observed with spectral data. A collection of 99 near-infrared spectra, each including 351 data points, was used for the comparison of the 3 methods. The dependent variable was the specific production of pelleting. The spectral collection was divided into 49 and 50 observations for calibration and validation, respectively. The main elements of comparison were the minimum error observed on the verification set, the number of regressors introduced in the models and the stability of the errors around the minimum values. The minimum errors were 3.29, 3.13 and 3.07 for PCR, PLS and LRR, respectively. LRR required a large number of regressors in order to obtain the minimum error. Nevertheless, it gave very stable results, and the errors were not markedly increased when an arbitrary large number of regressors was introduced into the LRR model.

Statistical strategies for relating metabolomics and proteomics data: a real case study in nutrition research area

The current investigations were carried out in the context of a nutritional case study aiming at assessing the postnatal impact of maternal dietary protein restriction during pregnancy and lactation on rat offspring plasma metabolome and hypothalamic proteome. Although data generated by different “Omics” technologies are usually considered and analyzed separately, their interrelation may offer a valuable opportunity for assessing the emerging ‘integrated biology’ concept. The overall strategy of analysis first investigated data pretreatment and variable selection for each dataset. Then, three multivariate analyses were applied to investigate the links between the abundance of metabolites and the expression of proteins collected on the same samples. Unfold principal component analysis and regularized canonical correlation analysis did not take into account the presence of groups of individuals related to the intervention study. On the contrary, the predictive MultiBlock Partial Least Squares method used this information. Regularized canonical correlation analysis appeared as a relevant approach to investigate of the relationships between the two datasets. However, in order to highlight the molecular compounds, proteins and metabolites, associated in interacting or common metabolic pathways for the experimental groups, MultiBlock partial least squares was the most appropriate method in the present nutritional case study.