Potentially toxic elements capture by an active living wall in indoor environments: Effect of species in air phytoremediation.

Indoor air pollution is a serious health problem throughout the world. Plants are known to be able to reduce the effect of air pollution and improve indoor air quality (IAQ). The aim of the present study was to compare the effectiveness of four plant species (Tradescantia zebrina hort. ex Bosse, Philodendron scandens K. Koch & Sello, Ficus pumila L. and Chlorophtytum comosum (Thunb.) Jacques) planted in an active living wall (ALW) for capturing particle pollutants. The ALW was introduced in a glass chamber and exposed to large (10-40 µm) and fine (1.2-10 µm) airborne particles containing a fixed concentration of potentially toxic elements (Al, B, Cd, Co, Cr, Cu, Ni and Pb). The surface particle deposition (sPM) was estimated in the leaves from the four species and the potentially toxic element concentration in the particulate matter (PM) was measured in plants, medium culture and in the ALW support system. The distribution of different particle size fractions differed between species. The capacity to trap particles on leaf surfaces was similar among the species (4.7-13 ng cm-2) except when comparing Tradescantia and Chlorophytum with Ficus, being higher in the latter species. Differences in toxic elements accumulation capacity were observed between species depending on the elements considered. The percentage of reduction in indoor pollution using an ALW was in a range of 65-79% being similar between species. Plants were the most important component of the ALW in terms of accumulation of indoor potentially toxic elements. The data presented here could be used to model the effectiveness of ALW systems schemes in improving IAQ.

Authentication of Iberian pork official quality categories using a portable near infrared spectroscopy (NIRS) instrument.

A portable near infrared spectroscopy (NIRS) instrument was evaluated for the discrimination of individual Iberian pig carcasses into the four official quality categories (defined by a combination of genotype and feeding regime). Spectra were obtained scanning four anatomical locations (live animal skin, carcass surface, fresh meat and subcutaneous fat samples) at a commercial abattoir, using a handheld micro electro mechanical system instrument. The best assignments into official quality categories with the NIRS measurements in the carcass surface and subcutaneous fat were able to correctly classify 75.9% and 73.8% of the carcasses, respectively. Moreover, 93.2% and 93.4% of carcasses were correctly classified according to feeding regimes by using the spectra from fresh meat and subcutaneous fat samples. The results suggest that, using subcutaneous fat samples, a portable NIRS could be used in commercial abattoirs as a tool to support the control of official quality category assignment in Iberian pig carcasses.

Near Infrared Spectroscopy (NIRS) for the determination of the milk fat fatty acid profile of goats.

Milk fatty acid (FA) composition is important for the goat dairy industry because of its influence on cheese properties and human health. The aim of the present work was to evaluate the feasibility of NIRS reflectance (oven-dried milk using the DESIR method) and transflectance (liquid milk) analysis to predict milk FA profile and groups of fats in milk samples from individual goats. NIRS analysis of milk samples allowed to estimate FA contents and their ratios and indexes in fat with high precision and accuracy. In general, transflectance analysis gave better or similar results than reflectance mode. Interestingly, NIRS analysis allowed direct prediction of the Atherogenicity and Thrombogenicity indexes, which are useful for the interpretation of the nutritional value of goat milk. Therefore, the calibrations obtained in the present work confirm the viability of NIRS as a fast, reliable and effective analytical method to provide nutritional information of milk samples.

Direct prediction of bioethanol yield in sugar beet pulp using near infrared spectroscopy.

Sugar beets are a raw material for the production of sugar and ethanol. The decision on which end product to pursue could be facilitated by fast and reliable means of predicting the potential ethanol yield from the beets. A Near Infrared (NIR) Spectroscopy-based approach was tested for the direct prediction of the potential bioethanol production from sugar beets. A modified partial least squares (MPLS) regression model was applied to 125 samples, ranging from 21.9 to 31.0 gL(-1) of bioethanol in sugar beet brei. The samples were analyzed in reflectance mode in a Direct Contact Food Analyser (DCFA) FOSS-NIRSystems 6500 monochromator, with standard error of cross validation (SECV), standard error of prediction (SEP), coefficient of determination (r(2)) and coefficient of variation (CV) of 0.51, 0.49, 0.91 and 1.9 gL(-1), respectively. The NIR technique allowed direct prediction of the ethanol yield from sugar beet brei (i.e. the product obtained after sawing beets with a proper machine) in less than 3 min.

Association between the polymorphism of the goat stearoyl-CoA desaturase 1 (SCD1) gene and milk fatty acid composition in Murciano-Granadina goats.

Genetic variability of the caprine stearoyl-CoA desaturase 1 (SCD1) gene has been investigated by sequencing a 4.7-kb cDNA in 6 goats from the Murciano-Granadina and Malagueña breeds. Sequence alignment revealed the existence of one synonymous polymorphism at exon 5 (c.732C>T) and one nucleotide substitution (c.*3504G>A) at exon 6 that encodes the 3' untranslated region (UTR). Moreover, the existence of a previously reported 3'UTR polymorphism involving a 3-bp indel (c.*1902_1904delTGT) was confirmed. Single nucleotide polymorphism and haplotype-based association analyses revealed suggestive associations between genetic variability of the SCD1 locus and lactose, stearic, polyunsaturated, and conjugated linoleic fatty acid contents. Associations with milk fatty acid composition might be explained by the global effects that SCD1 exerts on mammary gland lipid metabolism through the down-modulation of key transcription factors. Interestingly, the performance of an in silico analysis revealed that the c.*1902_1904delTGT polymorphism involves a considerable change in the secondary structure of the SCD1 mRNA. Gene reporter assays and quantitative PCR analysis would be needed to assess if this mutation has a causal effect on milk polyunsaturated and conjugated linoleic fatty acid levels by altering the amount of SCD1 transcripts in mammary epithelial cells.

Polymorphism of the caprine malic enzyme 1 ( ME1) gene and its association with milk quality traits in Murciano-Granadina goats.

Malic enzyme 1 plays a fundamental role in lipid metabolism because it yields a significant amount of the NADPH necessary for fatty acid biosynthesis. In ruminants, however, its contribution to this biochemical process seems to be less relevant than in other livestock species. In this study, we have partially sequenced the goat ME1 gene with the aim of identifying polymorphic sites. Structural analysis of the goat ME1 amino acid partial sequence evidenced the existence of two dinucleotide-binding motifs, located at positions 158 to 163 (GLGDLG) and 301 to 306 (GAGEAA), and three amino acid residues (E245, D246 and D269) involved in the binding of Mn2+ and strongly conserved among mammalian species. Moreover, multiple sequence alignment allowed us to identify four single nucleotide polymorphisms at exons 5 (c.483C > T), 6 (c.667G > A), 9 (c.927C > T) and 11 (c.1200G > A). The effects of ME1 genotype on milk production and composition traits were investigated in a Murciano-Granadina goat population. None of the associations found reached statistical significance after applying Benjamini-Hochberg correction. At most, associations with uncorrected P-values below 0.01 were observed for C16:0, C18:1n-9t and total conjugated linoleic acids. These negative results reinforce the notion that ME1 plays an ancillary role in ruminant lipogenesis.

Nutritive evaluation of olive tree leaves by near-infrared spectroscopy: effect of soil contamination and correction with spectral pretreatments.

Olive leaves obtained as a byproduct in the Mediterranean region could play an important role in the nutrition of extensive ruminant systems. However, the reported variation in their nutritive value, among other reasons due to discrepancies in mineral content, is considered an important obstacle for their common use. Near-infrared spectroscopy (NIRS) could fulfill the requirements of these productive systems, providing analytical information in a rapid and economic way. In this work, the effect of soil contamination on NIR spectra has been studied, as well as its correction with some of the most commonly used spectral pretreatments (derivatives, multiplicative scatter correction, auto scaling, detrending, and a combination of the last two transforms). Effects were evaluated by visual inspection of the transformed spectra and comparison of the calibration statistics obtained to estimate acid insoluble ash and total ash contents and in vitro pepsin cellulase digestibility of organic and dry matter. The incidence of spectral curvature effects caused by soil contamination that can be conveniently corrected with pretreatments such as derivatives was confirmed.

Evaluation of pretreatment strategies for near-infrared spectroscopy calibration development of unground and ground compound feedingstuffs.

Chemometric procedures are usually applied to near-infrared (NIR) spectra in order to obtain prediction models. These procedures include the application of different combinations of spectral mathematical pretreatments for the improvement of calibrations and the selection of the best model on the basis of validation results. In this work, we used an automatic routine to obtain calibrations for unground and ground compound feedingstuffs (N=354 samples), including 49 combinations of pretreatments (first and second derivatives, an auto scaling procedure, detrending and two versions of multiplicative scatter correction). Calibrations for crude fiber and crude protein were developed without elimination of outliers and with 2 or 9 maximum passes of elimination of outliers. Validation statistics were highly influenced by the pretreatments used, as a combined result of their ability to improve the detection of outliers and the model adjustment. The standard error of prediction (SEP) values ranged from 0.61 to 1.27 for crude protein (CP) and from 0.74 to 1.33 for crude fiber (CF). In spite of the fact that validation statistics did not show a clear distribution pattern, some combinations of pretreatments provided consistently better results.