Analysis of Body Composition and Food Habits of Spanish Celiac Women.

The purpose of the present work was both to analyze composition of Spanish celiac women and to study the food habits and gluten-free diet of these celiac patients, in order to determine whether they achieve a balanced and healthy diet as well as to highlight nutritional qualitative and/or quantitative differences. 54 adult celiac women (34 ± 13 years) took part in the six-month study. Height, weight and body composition were measured. An analysis of energy consumption and of the macronutrient distribution of their diet was carried out. Their fulfillment of micronutrient intake recommendations was verified. Participants showed a Body Mass Index of 21.6 ± 2.4 kg/m2. Energy Intake was slightly lower than the Dietary Reference Intakes. Excessive protein apart from over-consumption of fat was observed. More than three quarters of participants consumed meat in excess. Carbohydrate consumption along with that of fiber was below recommended levels. Vitamin D, iron, and iodine had a low percentage of recommendation compliance. In general, participants followed the recommendations of dairy products and fruit intake whereas vegetable consumption was not enough for the vast majority. We conclude that although the diet of celiac women does not differ much from the diet of general population, some considerations, such as reducing fat and protein consumption and increasing fiber intake, must be taken into account.

Estimation of parameters in sewage sludge by near-infrared reflectance spectroscopy (NIRS) using several regression tools.

Sewage sludge application to agricultural soils is a common practice in several countries in the European Union. Nevertheless, the application dose constitutes an essential aspect that must be taken into account in order to minimize environmental impacts. In this study, near infrared reflectance spectroscopy (NIRS) was used to estimate in sewage sludge samples several parameters related to agronomic and environmental issues, such as the contents in organic matter, nitrogen and other nutrients, metals and carbon fractions, among others. In our study (using 380 biosolid samples), two regression models were fitted: the common partial least square regression (PLSR) and the penalized signal regression (PSR). Using PLSR, NIRS became a feasible tool to estimate several parameters with good goodness of fit, such as total organic matter, total organic carbon, total nitrogen, water-soluble carbon, extractable organic carbon, fulvic acid-like carbon, electrical conductivity, Mg, Fe and Cr, among other parameters, in sewage sludge samples. For parameters such as C/N ratio, humic acid-like carbon, humification index, the percentage of humic acid-like carbon, the polymerization ratio, P, K, Cu, Pb, Zn, Ni and Hg, the performance of NIRS calibrations developed with PLSR was not sufficiently good. Nevertheless, the use of PSR provided successful calibrations for all parameters.

Study of the evolution of organic matter during composting of winery and distillery residues by classical and chemometric analysis.

The aim of the present paper is to evaluate the changes of organic matter during the composting process of fresh winery and distillery residues (WDR) by means of classical and chemometric analysis of (13)C cross-polarization magic angle spinning (CPMAS) NMR and Fourier transform infrared (FT-IR) spectra. (13)C NMR spectroscopy displayed a preferential biodegradation of carbohydrates as well as an accumulation of aliphatic chains (cutin- and suberin-like substances). This preferential biodegradation of the organic fractions reduces the landfill emission potential. Although the composition of the input mixture strongly affects the shape of the infrared (IR) spectra, typical bands of components can be selected and used to follow the composting process; that is, changes in the relative absorbances of the band of nitrate (at 1384 cm(-1)) and in the band of carbohydrates (at 1037 cm(-1)) have been observed. In addition, different chemometric tools, such as partial least-squares (PLS), interval PLS (iPLS), backward iPLS (biPLS), and genetic algorithm (GA), have been used to find the most relevant spectral region during the composting process. Chemometric analysis based on the combined and sequential use of iPLS and GA has been revealed as a very powerful tool for the detection in samples of the most relevant spectral region related to the composting process. From the obtained results, it can be concluded that CPMAS (13)C NMR supported by FT-IR could provide information about the evolution and characteristics of the organic matter during the composting process in order to avoid contamination problems after its use as amendment in agriculture or after landfilling.