Comparison of mass spectrometry-based electronic nose and solid phase microextraction gas chromatography-mass spectrometry technique to assess infant formula oxidation

Two headspace techniques based on mass spectrometry detection (MS), electronic nose, and solid phase microextraction coupled to gas chromatography-mass spectrometry (SPME-GC/MS) were evaluated for their ability to differentiate various infant formula powders based on changes of their volatiles upon storage. The electronic nose gave unresolved MS fingerprints of the samples gas phases that were further submitted to principal component analysis (PCA). Such direct MS recording combined to multivariate treatment enabled a rapid differentiation of the infant formulas over a 4 week storage test. Although MS-based electronic nose advantages are its easy-to-use aspect and its meaningful data interpretation obtained with a high throughput (100 samples per 24 h), its greatest disadvantage is that the present compounds could not be identified and quantified. For these reasons, a SPME-GC/MS measurement was also investigated. This technique allowed the identification of saturated aldehydes as the main volatiles present in the headspace of infant milk powders. An isotope dilution assay was further developed to quantitate hexanal as a potential indicator of infant milk powder oxidation. Thus, hexanal content was found to vary from roughly 500 and 3500 microg/kg for relatively non-oxidized and oxidized infant formulas, respectively.     

Considerations regarding the use of hyperspectral imaging data in classifications of food products, exemplified by analysis of maize kernels

Development of robust analytical procedures is critical when using hyperspectral imaging technology in food technology and agriculture. This study used near-isogenic inbred corn lines to address two basic questions: (1) To what extent is classification accuracy increased by grinding maize kernels? (2) Can the classification accuracy of two near-isogenic inbred lines be increased by using a spectral filter to classify only certain hyperspectral profiles from each image cube? Whole kernels and ground kernels in two particle intervals, 0.250-0.354 mm (size 1) and 0.354-0.841 mm (size 2), were examined. Spectral profiles acquired from ground kernels had higher spectral repeatability than data collected from whole kernels. The classification error of discriminant functions from whole kernels was >3 times lower than that of size 1 ground particles. Applying a spectral filter to input data had negligible effect on classifications of hyperspectral profiles from whole kernels and size 2 ground particles, but for size 1 ground particles a considerable increase in accuracy was observed. Independent validation confirmed that distinction between wild type and mutant inbred maize lines could be conducted with >80% accuracy after the proposed spectral filter had been applied to hyperspectral profiles of size 1 ground particles. A combination of discriminant analysis and regression analysis could be used to accurately predict mixture ratios of the two inbred lines. The use of spectral filtering to increase the level of spectral repeatability and the use of hyperspectral imaging technology in large-scale commercial operations are discussed.     

The influence of soil properties on the structure of bacterial and fungal communities across land-use types

Land-use change can have significant impacts on soil conditions and microbial communities are likely to respond to these changes. However, such responses are poorly characterized as few studies have examined how specific changes in edaphic characteristics do, or do not, influence the composition of soil bacterial and fungal communities across land-use types. Soil samples were collected from four replicated (n = 3) land-use types (hardwood and pine forests, cultivated and livestock pasture lands) in the southeastern US to assess the effects of land-use change on microbial community structure and distribution. We used quantitative PCR to estimate bacterial–fungal ratios and clone libraries targeting small-subunit rRNA genes to independently characterize the bacterial and fungal communities. Although some soil properties (soil texture and nutrient status) did significantly differ across land-use types, other edaphic factors (e.g., pH) did not vary consistently with land-use. Bacterial–fungal ratios were not significantly different across the land-uses and distinct land-use types did not necessarily harbor distinct soil fungal or bacterial communities. Rather, the composition of bacterial and fungal communities was most strongly correlated with specific soil properties. Soil pH was the best predictor of bacterial community composition across this landscape while fungal community composition was most closely associated with changes in soil nutrient status. Together these results suggest that specific changes in edaphic properties, not necessarily land-use type itself, may best predict shifts in microbial community composition across a given landscape. In addition, our results demonstrate the utility of using sequence-based approaches to concurrently analyze bacterial and fungal communities as such analyses provide detailed phylogenetic information on individual communities and permit the robust assessment of the biogeographical patterns exhibited by soil microbial communities.     

The ecosystem services concept: a new Esperanto to facilitate participatory planning processes?

Landscape Ecology - Several case studies investigated the role of ecosystem services in participatory planning processes. However, no systematic study exists that cuts across a large number of...     

A multi-method approach to delineate and validate migratory corridors

Context

Managers are faced with numerous methods for delineating wildlife movement corridors, and often must make decisions with limited data. Delineated corridors should be robust to different data and models.

Objectives

We present a multi-method approach for delineating and validating wildlife corridors using multiple data sources, which can be used conserve landscape connectivity. We used this approach to delineate and validate migration corridors for wildebeest (Connochaetes taurinus) in the Tarangire Ecosystem of northern Tanzania.

Methods

We used two types of locational data (distance sampling detections and GPS collar locations), and three modeling methods (negative binomial regression, logistic regression, and Maxent), to generate resource selection functions (RSFs) and define resistance surfaces. We compared two corridor detection algorithms (cost-distance and circuit theory), to delineate corridors. We validated corridors by comparing random and wildebeest locations that fell within corridors, and cross-validated by data type.

Results

Both data types produced similar RSFs. Wildebeest consistently selected migration habitat in flatter terrain farther from human settlements. Validation indicated three of the combinations of data type, modeling, and corridor detection algorithms (detection data with Maxent modeling, GPS collar data with logistic regression modeling, and GPS collar data with Maxent modeling, all using cost-distance) far outperformed the other seven. We merged the predictive corridors from these three data-method combinations to reveal habitat with highest probability of use.

Conclusions

The use of multiple methods ensures that planning is able to prioritize conservation of migration corridors based on all available information.

     

Occupancy pattern of a long-horned beetle in a variegated forest landscape: linkages between tree quality and forest cover across spatial scales

Context

Interactions between landscape-scale processes and fine-grained habitat heterogeneity are usually invoked to explain species occupancy in fragmented landscapes. In variegated landscapes, however, organisms face continuous variation in micro-habitat features, which makes necessary to consider ecologically meaningful estimates of habitat quality at different spatial scales.

Objectives

We evaluated the spatial scales at which forest cover and tree quality make the greatest contribution to the occupancy of the long-horned beetle Microplophorus magellanicus (Coleoptera: Cerambycidae) in a variegated forest landscape.

Methods

We used averaged data of tree quality (as derived from remote sensing estimates of the decay stage of single trees) and spatially independent pheromone-baited traps to model the occurrence probability as a function of multiple cross-scale combinations between forest cover and tree quality (with scales ranging between 50 and 400 m).

Results

Model support and performance increased monotonically with the increasing scale at which tree quality was measured. Forest cover was not significant, and did not exhibit scale-specific effects on the occurrence probability of M. magellanicus. The interactive effect between tree quality and forest cover was stronger than the independent (additive) effects of tree quality and particularly forest cover. Significant interactions included tree quality measured at spatial scales ≥200 m, but cross-scale interactions occurred only in four of the seven best-supported models.

Conclusions

M. magellanicus respond to the high-quality trees available in the landscape rather than to the amount of forest per se. Conservation of viable metapopulations of M. magellanicus should consider the quality of trees at spatial scales >200 m.

     

Efficiency of SNP and SSR-based analysis of genetic diversity,population structure,and relationships among cowpea (<Emphasis Type="Italic">Vigna unguiculata</Emphasis> (L.) Walp.) germplasm from East Africa and IITA inbred lines

The extent of genetic diversity and relatedness of cowpea germplasm from East Africa are poorly understood. A set of 13 microsatellites (SSR) and 151 single nucleotide polymorphisms (SNPs) markers were applied to assess the levels of genetic diversity in a sample of 95 accessions of local cowpea germplasm and inbred lines of Vigna unguiculata. The average genetic diversity (D), as quantified by the expected heterozygosity, was higher for SSR loci (0.52) than for SNPs (0.34). The polymorphic information content was 0.48 for SSR and 0.28 for SNP while the fixation index was 0.095 for SSR and 0.15 for SNPs showing moderate differentiation and high gene flow among cowpea accessions from East African countries. The results of data analysis of both SSR and SNP markers showed similar clustering patterns suggesting a substantial degree of association between origin and genotype. Principal coordinate analysis (PCoA) with SSR and SNP markers showed that accessions were grouped into two and three broad groups across the first two axes, respectively. Our study found that SNP markers were more effective than SSR in determining the genetic relationship among East African local cowpea accessions and IITA inbred lines. Based on this analysis, five local cowpea accessions Tvu-13490, Tvu-6378, Tvu-13448, Tvu-16073, and 2305675 were identified to be tightly clustered sharing several common alleles with the drought tolerant variety Danila when analyzed with SSR and SNP markers. The findings will assist and contribute to future genetic diversity studies aimed at the genetic improvement of local Eastern Africa cowpea accessions for improved overall agronomic performance in general and breeding for drought tolerant in particular.