An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Production of whey protein-based aggregates under ohmic heating

Formation of whey protein isolate protein aggregates under the influence of moderate electric fields upon ohmic heating (OH) has been monitored through evaluation of molecular protein unfolding, loss of its solubility, and aggregation. To shed more light on the microstructure of the protein aggregates produced by OH, samples were assayed by transmission electron microscopy (TEM). Results show that during early steps of an OH thermal treatment, aggregation of whey proteins can be reduced with a concomitant reduction of the heating chargeby reducing the come-up time (CUT) needed to reach a target temperatureand increase of the electric field applied (from 6 to 12 V cm1). Exposure of reactive free thiol groups involved in molecular unfolding of -lactoglobulin (-lg) can be reduced from 10 to 20 %, when a CUT of 10 s is combined with an electric field of 12 V cm1. Kinetic and multivariate analysis evidenced that the presence of an electric field during heating contributes to a change in the amplitude of aggregation, as well as in the shape of the produced aggregates. TEM discloses the appearance of small fibrillar aggregates upon the influence of OH, which have recognized potential in the functionalization of food protein networks. This study demonstrated that OH technology can be used to tailor denaturation and aggregation behavior of whey proteins due to the presence of a constant electric field together with the ability to provide a very fast heating, thus overcoming heat transfer limitations that naturally occur during conventional thermal treatments.

Electronic tongue: a versatile tool for mineral and fruit-flavored waters recognition

Natural mineral waters (still), effervescent natural mineral waters (sparkling) and aromatized waters with fruit-flavors (still or sparkling) are an emerging market. In this work, the capability of a potentiometric electronic tongue, comprised with lipid polymeric membranes, to quantitatively estimate routinely quality physicochemical parameters (pH and conductivity) as well as to qualitatively classify water samples according to the type of water was evaluated. The study showed that a linear discriminant model, based on 21 sensors selected by the simulated annealing algorithm, could correctly classify 100 % of the water samples (leave-one out cross-validation). This potential was further demonstrated by applying a repeated K-fold cross-validation (guaranteeing that at least 15 % of independent samples were only used for internal-validation) for which 96 % of correct classifications were attained. The satisfactory recognition performance of the E-tongue could be attributed to the pH, conductivity, sugars and organic acids contents of the studied waters, which turned out in significant differences of sweetness perception indexes and total acid flavor. Moreover, the E-tongue combined with multivariate linear regression models, based on sub-sets of sensors selected by the simulated annealing algorithm, could accurately estimate waters pH (25 sensors: R 2 equal to 0.99 and 0.97 for leave-one-out or repeated K-folds cross-validation) and conductivity (23 sensors: R 2 equal to 0.997 and 0.99 for leave-one-out or repeated K-folds cross-validation). So, the overall satisfactory results achieved, allow envisaging a potential future application of electronic tongue devices for bottled water analysis and classification.