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, modeling, outcomes, interpretation, flexibility, practical issues and user friendliness

Comparison of two different strategies for investigating individual differences among consumers in choice experiments. A case study based on preferences for iced coffee in Norway

Two different strategies for investigating individual differences among consumers in choice experiments using the Mixed Logit Model are compared. The study is based on a consumer study of iced coffees 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 attributes, such as socio-demographics, attitudes and habits, were also collected. Choice data were first analyzed using the Mixed Logit Model and then two different approaches were adopted for investigating consumer attributes. The first strategy, called one-step strategy, includes the consumer attributes directly in the Mixed Logit Model. The second strategy, called multi-step strategy, combines different methods of analysis such as Mixed Logit Model based on the design factors only, followed by Principal Component Analysis and Partial Least Squares regression to study consumer attributes. The two approaches are compared in terms of data analysis methodologies, outcomes, practical issues, user friendliness, and interpretation. Overall, we think the multi-step strategy is the one to be preferred in most practical applications because of its flexibility and stronger exploratory capabilities

Comparison of rating-based and choice-based conjoint analysis models. A case study based on preferences for iced coffee in Norway

The authors compare two conjoint analysis approaches eliciting consumer preferences among different product profiles of iced coffees in Norway: rating-based and choice-based conjoint experiments. In the conjoint experiments, stimuli were presented in the form of mock-up pictures of iced coffees varying in coffee type, production origin, calorie content and price, following an orthogonal design. One group of participants (n = 101) performed a rating task of 12 iced coffees whereas another group (n = 102) performed a choice task on 20 iced coffees presented in eight triads. Then, all participants performed self-explicated rating and ranking evaluations of the iced coffee attributes. The rating data were analyzed by a Mixed Model ANOVA while the choice data were analyzed by a Mixed Logit Model. Both models include conjoint factors, demographic variables and their interactions. Results show that the two approaches share similar main results, where consumers prefer low calorie and low price iced coffee products. However, additional effects are detected within each of the two approaches. Further, self-explicated measures indicate that coffee type is the primary attribute for consumers’ selection of iced coffee. The two conjoint approaches are compared and discussed in terms of experimental designs, data analysis methodologies, outcomes, user-friendliness of the results interpretation, estimation power and practical issues

Undersøkelser for å styrke modeller knyttet til beslutningsstøtte for tiltak mot forurensede sedimenter i Grenlandsfjordene

Årsliste 2009I forbindelse med problemstillingene om sedimenttiltak i Grenlandsfjordene, er undersøkelser utført for å styrke beslutningsgrunnlaget for eventuelle tiltak mot dioksinforurensede sedimenter i fjordområdet. Dette arbeidet har bestått i en avansert kalibrering av den såkalte SedFlex-modellen for å styrke usikkerhetsestimatene i modellsimuleringene. Betydningen av skipsoppvirvling ved Herøya for dioksinutviklingen i Frierfjorden er modellert. Videre er tykkelsen på det aktive, bioturberte overflatelaget på 39 stasjoner i Grenlandsfjordene bestemt ved hjelp av SPI-sedimentkameraet. Tre stasjoner for ”overvåket naturlig forbedring” er blitt opprettet hvor den feltmessige variansen er beskrevet og utsagnskraften i et overvåkingsprogram beregnet. Transport av dioksiner fra Frierfjorden til fjordområdet utenfor er beregnet både gjennom direkte målinger ved bruk av passive prøvetakere og ved simuleringer i SedFlex-modellen.Norsk Hydr

Analyzing sensory data using non-linear preference learning with feature subset selection

15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004The quality of food can be assessed from different points of view. In this paper, we deal with those aspects that can be appreciated through sensory impressions. When we are aiming to induce a function that maps object descriptions into ratings, we must consider that consumers’ ratings are just a way to express their preferences about the products presented in the same testing session. Therefore, we postulate to learn from consumers’ preference judgments instead of using an approach based on regression. This requires the use of special purpose kernels and feature subset selection methods. We illustrate the benefits of our approach in two families of real-world data base

Overvåking av sjøresipienten for avløp fra Åremma avfallsdeponi ved Rynes i Vefsnfjorden

Søndre Helgeland Miljøverk (SHMIL) har et avfallsdeponi i Åremma med utleding av sigevann til Vefsnfjorden ved Rynes. Her er det gjennomført undersøkelser av miljøgifter i sedimentet i 2003 og 2006 samt undersøkelser av miljøgifter i vannmasser og sedimenter, alger og dyr i fjæra og sammen­setningen av dyrelivet på bløtbunn i 2009/2010. Miljøforholdene ved Rynes er stort sett gode. Vannmassene og sedimentene er noe påvirket av tidligere høye utslipp av PAH til fjorden. Strandsonen er preget av få arter på grunn av den naturlige ferskvannstilførslen til Vefsnfjorden. Det kan ikke påvises noen endring i miljøgiftinnholdet i sedimentene fra 2003 til 2009.Søndre Helgeland Miljøverk IKS (SHMIL

A spectroscopy approach to the study of virus infection in the endophytic fungus Epichloë festucae

<p>Abstract</p> <p>Background</p> <p>In this work we propose a rapid method based on visible and near-infrared (Vis-NIR) spectroscopy to determine the occurrence of double-stranded RNA (dsRNA) viruses in <it>Epichloë festucae </it>strains isolated from <it>Festuca rubra </it>plants. In addition, we examined the incidence of infections by <it>E. festucae </it>in populations of <it>F. rubra </it>collected in natural grasslands of Western Spain.</p> <p>Methods</p> <p>Vis-NIR spectra (400-2498 nm) from 124 virus-infected and virus-free <it>E. festucae </it>isolates were recorded directly from ground and freeze-dried mycelium. To estimate how well the spectra for uninfected and infected fungal samples could be differentiated, we used partial least-squares discriminant analysis (PLS1-DA) and several data pre-treatments to develop calibration models.</p> <p>Results</p> <p>Applying the best regression model, obtained with two sampling years and using standard normal variate (SNV) combined with first derivative transformation to a new validating data set (42 samples), we obtained a correct classification for 75% of the uninfected isolates and up to 86% of the infected isolates.</p> <p>Conclusions</p> <p>The results obtained suggest that Vis-NIR spectroscopy is a promising technology for detection of viral infections in fungal samples when an alternative faster approach is desirable. It provides a tool adequately exact and more time- and cost-saving than the conventional reference analysis.</p