Technique sensory

More recently, studies of wine and beer have initiated techniques of statistically vaUd sensory analysis. Scientific studies involving wine continue in these areas, building on past discoveries. Natural phenols as desirable dietary components and monitors of storage and aging reactions are currently active fields. Viticultural research, as well as enological, continues to improve grapes and the wines made from them (11). 

The development of precise and reproducible methods of sensory analysis is prerequisite to the determination of what causes flavor, or the study of flavor chemistry. Knowing what chemical compounds are responsible for flavor allows the development of analytical techniques using chemistry rather than human subjects to characterize flavor (38,39). Routine analysis in most food production for the quaUty control of flavor is rare (40). Once standards for each flavor quaUty have been synthesized or isolated, they can also be used to train people to do more rigorous descriptive analyses. 

This example demonstrates the most challenging problem of flavor chemistry, ie, each flavor problem may require its own analytical approach however, a sensory analysis is always required. The remaining unknown odorants demand the most sensitive and selective techniques, and methods of concentration and isolation that preserve the sensory properties of complex and often dehcate flavors. Furthermore, some of the subtle odors in one system will be first identified in very different systems, like o-amino acetophenone in weasels and fox grapes. 

The type of food and its processing affect flavoring efficiency therefore, flavor materials must be taste-tested in the food itself. Because there has been a lack of standardization of testing techniques, a committee on sensory evaluation of the Institute of Food Technologists has offered a guide (112) which is designed to help in developing standard procedures. 

Four characteristics of odor are subject to measurement by sensory techniques intensity, detectability, character (quality), and hedonic tone (pleasantness-unpleasantness) (16). Odor intensity is the magnitude of the perceived sensation and is classified by a descriptive scale, e.g., faint-moderate-strong, or a 1-10 numerical scale. The detectability of an odor or threshold limit is not an absolute level but depends on how the odorant is present, e.g., alone or in a mixture. Odor character or qualit) is the characteristic which permits its description or classification by comparison to other odors, i.e., sweet or sour, or like that of a skunk. The last characteristic is the hedonic type, which refers to the acceptability of an odorant. For the infrequent visitor, the smell of a large commercial bread bakery may be of high intensity but pleasant. For the nearby resident, the smell may be less acceptable. 

The sensory technique used for assessing human perception of odors is called olfactometry. The basic technique is to present odorants at different concentrations to a panel of subjects and assess their response. The process favored by the U.S. National Academy of Sciences is dynamic olfactometry (16). This technique involves a sample dilution method in which a flow of clean, nonodorous air is mixed with the odorant under dynamic or constant... 

Much of our present day knowledge of sweetness intensity, both at the threshold level, where taste begins, and above the threshold level, derives from the application of psychophysical techniques. It is now evident that the psychophysical procedure used measure separate aspects of sweetness perception. Hedonic responses cannot be predicted from intensity of discrimination data, and vice versa. The taste-panel evaluation of sweetness is of fundamental importance in the development of worthwhile structure-taste relationships. Therefore, it is vital that the appropriate psychophysical method and experimental procedure be adopted for a particular objective of investigation. Otherwise, false conclusions, or improper inferences, or both, result. This situation results from the failure to recognize that individual tests measure separate parameters of sensory behavior. It is not uncommon that the advocates of a specific method or procedure seldom... 

Epidermal nerve fiber analysis by immunocytochemical techniques using the panaxonal marker protein gene product 9.5 (PGP 9.5) allows the study of epidermal innervation by small fiber C and A5 nerve fibers (McCarthy et al. 1995 Holland et al. 1997). Studies of skin biopsies of HIV infected patients with DSP or ATN showed reduction in the number of epidermal fibers in distal areas of the lower extremities with an inverse correlation between neuropathic pain intensity and epidermal nerve fiber density (Polydefkis et al. 2002) (Fig. 4.3). There were also fewer epidermal fibers in HIV seropositive patients without clinical evidence of neuropathy, suggesting that HIV infection may be associated with the loss of cutaneous innervation even before the onset of sensory symptomatology (McCarthy et al. 1995). 

Three attributes characterize color hue, lighmess (or value), and saturation (or chroma) and they are graphically represented in color solids (e.g., Munsell solid. Hunter solid). The Munsell Color Notation is a rapid, portable, widespread, and economical system of color determination. However, as it depends on sensory evaluation by panels, many laboratories prefer when possible to replace human judgment by instrumental techniques that are easier to handle. The CIELAB established by the Commission International d Eclairage (CIE) has become widely used with the availability of reflectance spectrophotometric instrumentation. 

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. 

A powerful technique which allows to answer such questions is Generalized Procrustes Analysis (GPA). This is a generalization of the Procrustes rotation method to the case of more than two data sets. As explained in Chapter 36 Procrustes analysis applies three basic operations to each data set with the objective to optimize their similarity, i.e. to reduce their distance. Each data set can be seen as defining a configuration of its rows (objects, food samples, products) in a space defined by the columns (sensory attributes) of that data set. In geometrical terms the (squared) distance between two data sets equals the sum over the squared distances between the two positions (one for data set and one for Xg) for each object. 

M. Meilgaard, G.V. Civille and B.T. Carr, Sensory Evaluation Techniques, 2nd Edition. CRC Press, Boca Raton, FL, 1987. 

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