Comparison of different clustering methods for investigating individual differences using choice experiments.

Different strategies for investigating individual differences among consumers using choice experiments are compared. The paper is based on a consumer study of iced coffee in Norway. Consumers (n = 102) performed a choice task of twenty different iced coffee profiles varying in coffee type, production origin, calorie content and price following an orthogonal design. Consumer factors, such as socio-demographics, attitudes and habits, were also collected. Choice data will be analysed using two different clustering strategies. Strategy one is the most classical approach called Latent Class Logit (LCL) model, while Strategy two uses Mixed Logit (ML) model combined with Principal Component Analysis (PCA) for visual segmentation or with automatic clustering detection using Fuzzy C Means clustering (FCM). The clusters obtained can be interpreted using external consumer factors by using the Partial Least Square - Discrimination Analysis (PLS-DA) model. The different approaches are compared in terms of data analysis methodologies, modelling, outcomes, interpretation, flexibility, practical issues and user friendliness.

Abrupt temporal fluctuations in the chicken fecal microbiota are explained by its gastrointestinal origin.

One of the main challenges in understanding the composition of fecal microbiota is that it can consist of microbial mixtures originating from different gastrointestinal (GI) segments. Here, we addressed this challenge for broiler chicken feces using a direct 16S rRNA gene-sequencing approach combined with multivariate statistical analyses. Broiler feces were chosen because of easy sampling and the importance for pathogen transmission to the human food chain. Feces were sampled daily for 16 days from chickens with and without a feed structure-induced stimulation of the gastric barrier function. Overall, we found four dominant microbial phylogroups in the feces. Two of the phylogroups were related to clostridia, one to lactobacilli, and one to Escherichia/Shigella. The relative composition of these phylogroups showed apparent stochastic temporal fluctuations in feces. Analyses of dissected chickens at the end of the experiment, however, showed that the two clostridial phylogroups were correlated to the microbiota in the cecum/colon and the small intestine, while the upper gut (crop and gizzard) microbiota was correlated to the lactobacillus phylogroup. In addition, chickens with a stimulated gizzard also showed less of the proximate GI dominating bacterial group in the feces, supporting the importance of the gastric barrier function. In conclusion, our results suggest that GI origin is a main determinant for the chicken fecal microbiota composition. This knowledge will be important for future understanding of factors affecting shedding of both harmful and beneficial gastrointestinal bacteria through feces.

Acute maternal social dysfunction, health perception and psychological distress after ultrasonographic detection of a fetal structural anomaly.

OBJECTIVES: To predict acute psychological distress in pregnant women following detection of a fetal structural anomaly by ultrasonography, and to relate these findings to a comparison group. DESIGN: A prospective, observational study. SETTING: Tertiary referral centre for fetal medicine. POPULATION: One hundred and eighty pregnant women with a fetal structural anomaly detected by ultrasound (study group) and 111 with normal ultrasound findings (comparison group) were included within a week following sonographic examination after gestational age 12 weeks (inclusion period: May 2006 to February 2009). METHODS: Social dysfunction and health perception were assessed by the corresponding subscales of the General Health Questionnaire (GHQ-28). Psychological distress was assessed using the Impact of Events Scale (IES-22), Edinburgh Postnatal Depression Scale (EPDS) and the anxiety and depression subscales of the GHQ-28. Fetal anomalies were classified according to severity and diagnostic or prognostic ambiguity at the time of assessment. MAIN OUTCOME MEASURES: Social dysfunction, health perception and psychological distress (intrusion, avoidance, arousal, anxiety, depression). RESULTS: The least severe anomalies with no diagnostic or prognostic ambiguity induced the lowest levels of IES intrusive distress (P = 0.025). Women included after 22 weeks of gestation (24%) reported significantly higher GHQ distress than women included earlier in pregnancy (P = 0.003). The study group had significantly higher levels of psychosocial distress than the comparison group on all psychometric endpoints. CONCLUSIONS: Psychological distress was predicted by gestational age at the time of assessment, severity of the fetal anomaly, and ambiguity concerning diagnosis or prognosis.

Use of multivariate statistics for 16S rRNA gene analysis of microbial communities.

Understanding dynamic processes and diversity in microbial communities is of key importance for combating pathogens and for stimulating beneficial bacteria. We have addressed these challenges utilising multivariate statistics for analyses of microbial community structures. We based our microbial community analyses on 16S rRNA gene data. This gene is by far the most widely applied genetic marker for phylogenetic and microbial community studies. Both probe and clone library data were analysed. We analysed the clone library data using a newly developed coordinate-based phylogenetic approach. By using coordinates, we avoid both DNA sequence alignments and the need for definition of operational taxonomic units (OTUs). The basic principle is to transform the sequence data to frequencies of multimers (short sequences of n=2 to 6), and then to use principal component analyses (PCA) for data compression into an orthogonal coordinate space. We used our coordinate method for global 16S rRNA gene analyses of prokaryotes. When comparing microbial communities, it is often important to determine the relationship between the microflora and knowledge about the samples analysed. We used partial least square regression (PLSR) to relate physical/chemical properties to microbial community composition. This was done by analysing both probe and clone library data using the effect of modified atmosphere packaging (MAP) on fish microflora as an example. We are currently investigating approaches to describe dynamic microbial community interactions. Our ultimate goal is to understand and model the main dynamic interactions in complete microbial communities.

Prediction of sensory characteristics of beef by near-infrared spectroscopy.

Sensory hardness, tenderness and juiciness of M. Longissimus dorsi muscles from 10 beef carcasses at three ageing stages were predicted by near-infrared (NIR) spectroscopic analysis in the reflection (NIRR) and transmission modes (NIRT) during 14 days ageing at 2°C. Predicting the sensory variables hardness and tenderness from NIRR measurements using principal component regression (PCR), yielded correlation coefficients in the range 0·80-0·90. The root mean square errors of prediction for the predictions of hardness and tenderness were in the range 0·5-0·7, given in sensory assessment units. Juiciness was not well predicted. Prediction of sensory variables from NIRT measurements did not give satisfactory results. Including samples from all carcasses, cows and young bulls in the models resulted in good predictions from NIRR measurements of frozen and thawed samples. However, the best prediction results were generally obtained from separate calibrations of the samples from the bulls. The potential of NIR spectroscopy in the prediction of sensory variables in whole meat needs to be further investigated on a larger number of samples with different breeds, animals and process treatments included.