Garmt Dijksterhuis

Dynamic methods of sensory analysis

Abstract Human perception systems are generally geared towards detecting change. Constant stimulation is of low survival value, hence uninteresting. Most sensory methods focus on static judgements, but there is a class of methods especially adapted to measuring perceived change in stimulation from food. Most processes involved in eating, e.g. mastication and salivation, are dynamic processes, so methods acknowledging dynamic properties of eating are likely to produce results more valid than static methods. Food components as texturing agents, flavour systems, etc., have an impact on the dynamics of food breakdown and flavour release. Both from a fundamental—food perception and appreciation— and, from an applied—product development—viewpoint, dynamic sensory methods are worthwhile studying and employing.

Multivariate data analysis in sensory and consumer science: an overview of developments

Abstract Data sets resulting from sensory and consumer studies can be quite large. A variety of methods of multivariate data analysis can be very useful in the exploration of the structure that is in such data. Over the past ten years, many methods have been proposed and applied to data generated in sensory and consumer science research. This paper presents the developments in the multivariate statistical analysis of data in four main areas of sensory and consumer science.

Power of experimental designs, estimated by Monte Carlo simulation

What is the optimal size of an experiment? How should the practical experimenter determine this optimal experimental size? The paper presents a conceptually simple method for estimating the statistical power of experimental designs, based on Monte Carlo simulation. In the planning stage of a project, several alternative experimental designs may thereby be compared with respect to their ability to balance the risk of committing Type I and Type II errors against the cost. The Monte Carlo power estimation of a design is based on the following main steps: generate artificial data in a number of (say 5000) hypothetical experiments, based on the design and on certain assumptions; analyse each artificial data set in the same way that the future, real data set is intended to be analysed; from the distributions of the obtained parameter estimates, study the risks associated with the given experimental design. The method is illustrated for a factorial design in sensory analysis concerning warmed‐over flavour development in meat. Here the Monte Carlo simulations indicated that four replicates were needed, given certain assumptions. The real experiment was performed independently twice, each time in four replicates. The resulting analysis of effects gave satisfactory results, indicating that four replicates had given the necessary and sufficient power in each of the two experiments. Copyright

Combined Procrustes analysis and PLSR for internal and external mapping of data from multiple sources

Abstract Generalised Procrustes analysis (GPA) is a method for producing a group average from rotated versions of a set of individual data matrices followed by bi-linear approximation of this group average for graphical inspection. Partial Least Squares Regression (PLSR) is a method for relating one data matrix to another data matrix, via bi-linear low-rank regression modelling. The merger of these methods proposed aims to produce an average (e.g. a sensory group panel average), which balances an “intersubjective”, internal consensus between the individual assessors’ data against an “objective” external correspondence between the sensory data and other types of data on the same samples (e.g. design information, chemical or physical measurements or consumer data). Several ways of merging GPA with PLSR are possible, of which one is selected and applied. The proposed “GP–PLSR” method is compared to a conventional GPA followed by an independent PLSR, using a data set about milk samples assessed by a group of sensory judges with respect to a set of sensory descriptor terms, and also characterised by experimental design information about the samples. The GP–PLSR gave a more design-relevant group average than traditional GPA. The proposed algorithm was tested under artificially increased noise levels.

Multisensory Flavor Priming

Abstract Flavor is multisensory; several interacting sensory systems—taste, smell, and mouthfeel—together comprise “flavor,” making it a cognitively constructed percept rather than a bottom-up sensory one. In this chapter, some of the complications this entails for flavor priming are introduced, along with a taxonomy of different priming situations. In food-related applications of flavor, both bottom-up (sensory) as well as top-down (expectations) processes are at play. Most of the complex interactions that this leads to take place outside the awareness of the perceiving subject. A model is presented where many, past and current, aspects (sensory, surroundings, social, somatic, sentimental) of a (flavor) perception, together result in the perception of a flavor, its liking. or its choice. This model borrows on ideas from priming, situated/embodied cognition, and (food-related) perception.