Sensory profiling data analyses

Hunter, E. A. and Muir, D. D. (1995). A comparison of two multivariate methods for the analysis of sensory profile data. Journal of Sensory Studies, 10, 89-104. 

Brockhoff, P.B., Schlich, P. and Skovgaard, I. (2012). Acconnting for scaling differences in sensory profile data improved mixed model analysis of variance. Presented at the Pangbom Sensory Science Symposium, Toronto, 2011. Mannscript in preparation. 

There are four main types of data that frequently occur in sensory analysis pair-wise differences, attribute profiling, time-intensity recordings and preference data. We will discuss in what situations such data arise and how they can be analyzed. Especially the analysis of profiling data and the comparison of such data with chemical information calls for a multivariate approach. Here, we can apply some of the techniques treated before, particularly those of Chapters 35 and 36. 

Thybo, A. K., Martens, M. (1998). Development of a sensory texture profile of cooked potatoes by multivariate data analysis. Journal of Texture Studies, 29,453 68. 

The published literature on the effects of microbial activities on wine chemical composition is now considerable. Understanding the significance of wine chemistry is, however, heavily dependent on complex analytical strategies which combine extensive chemical characterization and sensory descriptive analysis. However, sensory analysis is extremely resource-intense, requiring many hours of panelists time. This prevents widespread application of these powerful analytical tools. Advanced statistical techniques have been developed that are closing the gap between chemical and sensory techniques. Such techniques allow the development of models, which should ultimately provide a sensory description based on chemical data. For example, Smyth et al. (2005) have developed reasonable models which can reveal the most likely compounds that relate to particular attributes that characterise the overall sensory profile of a wine. For wines such as Riesling and Chardonnay, the importance of several yeast volatile compounds has been indicated. Such information will allow yeast studies to target key compounds better rather than just those that are convenient to measure. 

Gas chromatography-olfactometery (GC-O) provides a sensory profile of odor active compounds present in an aroma extract by sniffing the GC effluent. Several techniques have been developed to collect and process GC-O data and to estimate the sensory contribution of individual odor active compounds, including dilution analysis (29, 30), time intensity (31), and detection frequency (32) methods. GC-O has successfully been used to evaluate the odor active compounds of olive oil (33), soybean oil (34), and fish oil enriched mayonnaise (35). 

In the sections above, the sensory profiling and multidimensional scaling techniques are illustrated using data obtained from the analysis of muguet perfumery materials. From a purely olfactory point of view, the type of odour that best fits the odour described in this brief is cyclamen aldehyde, but with the range of products that need to be considered in this brief the choice of material is not quite so simple. 

Sensory data may contain many important details that will remain just a collection of results unless they can be presented in a concise, nnderstandable manner. The illustration in Figure 18.3 is a spider chart (aka Radar Profile) showing the results from a descriptive ballot evaluating a brewery s India Pale Ale (IPA) that meets the requirements of the brand profile and is considered True to Brand. (Full statistical approaches can be implemented for data analysis, and discussions can be found in the main references cited above and extensively in Lawless, 2013b and O Mahoney, 1986.)... 

The development of rapid sensory profiling methods may have potential consequences on sensory activities themselves, since it broadens the spectrum of available methods and opens way for measurements that were previously not possible. Besides offering new opportunities in the use of sensory data in R D and research projects, this development may also have an organizational impact on sensory services and their relationships with stakeholders. As a result of this evolution, the practice of sensory descriptive analysis certainly becomes richer but also more complex and challenging. 

Besides, respondents have reported that the statistical techniques that would be needed to analyse new types of data are not always available in standard sensory acquisition software (although this situation is changing rapidly). As a consequence, practitioners need to use some statistical software, sometimes with the assistance of well-trained people. Even when routines are available, the data analysis is often seen as more complex than for conventional profiling and thus prevents the use of rapid methods. 

Besides, the sensory descriptions provided by these methods are usually not as accurate as those obtained by conventional profiling, though this criterion may not always apply (e.g. in the case of Free Sorting with consumers). This lower accuracy is notably linked to the more difficult interpretation when using free vocabulary. A direct consequence of this is the need for more advanced data analysis techniques. Eventually, the translation of sensory properties into technical product variables may be more difficult. 

Another threat lies in the way results from rapid sensory profiling methods are communicated to stakeholders, or to anyone else, who are not familiar with these methods. First, it must be noted that multivariate data analysis and sensory maps are not understood by everyone, which may be serionsly hazardous if results are... 

In spite of these reservations, it may be anphasized that when appropriately applied, rapid sensory profiling techniques are powerful tools for clever use of sensory analysis. Many examples and testimonies of successful uses of these methods, both in industry and in acadania, are presented throughout this book. This wiU hopefully spark the interest of students in sensory programmes, as well as that of sensory professionals, sensory scientists and, more generally, all users of sensory data. May this book help them in their daily work, provide than with some solutions and contribute to fostering innovation in sensory science. 

The most striking result from this study is perhaps the fact that it took less than 3h to complete the whole process, from sample preparation to data analysis. Results are consistent and compared fairly well to a conventional sensory profile (quantitative descriptive analysis (QDA )-like) conducted with another group of students (data not... 

Need for communication warnings Sensory analysis has built its reputation on the reliability of its recommendations. We must, of course, work on and control the rehability of the acquisition mode and processing. However, under no circumstances can the quality of the data equal that of the profile data. Explanatory work must therefore he carried out to clearly differentiate this approach from more traditional approaches. 

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