Experimental Design And Sensory Product Optimization

Sensory analysis has achieved its major commercial impact by providing reliable tests results from panelists.

Direct Scales of Sensory Magnitude

More productive uses of scaling have been made with "direct scaling," in which the investigator instructs the panelist to rate the stimulus intensity using a numbering system. The intensity scale may comprise graded categories (eg, from weak to strong, on a set of categories ranging from as few as 3, up to as many as 100 categories or more), or require the panelist to place a mark on a line of fixed length to denote intensity (with one end of the line denoting weak, and the opposite end denoting strong), or even require the panelist to assign numbers so that the ratios of the numbers assigned reflect the ratios of perceived intensity (the method of magnitude estimation (24). The investigator need not use numbers nor line length as the scale measure. The panelists can adjust the intensity of a stimulus such as white noise so that the intensity of the adjusted stimulus matches the perceived intensity of the stimulus being measured (25,26).

Direct scaling methods (in contrast to threshold and discrimination tests) enable the panelist to behave as a true measuring instrument. Data can be collected from many panelists relatively quickly and painlessly. The investigator straightforwardly treats the data by conventional statistical procedures, including computing the average and measures of variability (standard deviation, variance), determining differences among samples ("t" tests, analysis of variance), and modeling the relation between perceived intensity and physical measures by regression analysis (curve fitting).

In practical terms, scaling procedures provide a great deal of useful information. Consider the data shown in Table 10, which shows average consumer ratings of five cereals, on a variety of attributes. (Some of the attributes in Table 10 are sensory attributes, some are liking attributes, whereas some are "image" attributes). Panelists used a 0100 scale. When panelists rate each product on all of the attributes the investigator generates a "report card" for both marketed products and test prototypes.

Profiles of products on many attributes enable the researcher to compare the full profile of one product to the full profile of another and assess the degree of difference. Graphical display of data using the so-called spider plots, (Fig. 1) shows the difference between products. The "spider plot" is not analytical—it does not show the user anything beyond the fact that the two samples being plotted overlap, or do not. (One gets this same information from tables such as Table 10). The Spider plots, however, make the data come alive and increase its impact.

HEDONICS—MEASUREMENT OF LIKING

Acceptance measurement, the third branch of sensory analysis, may well be, the most vital aspect. We select and eat foods on the basis of their visual appeal and palatabil-ity. A large and growing body of scientific literature has been published on the measurement of acceptance. Appropriate measures of liking or purchase intent, and the assessment of possible consumer boredom with a product are critical issues when applying hedonic tests in the world of commercial research.

The Search for the Liking Scale

Buried in the archives of scientific literature are numerous attempts to create scales which measure acceptance, and techniques beyond scaling which probe other aspects of he-donics. Table 11 shows a variety of scales, ranging from classification (like/dislike) up to refinements which go beyond liking to probe other activities such as the expected effort one would make to eat the food (eg, FACT scale) (27,28). Still other efforts have focused on the assessment of the boredom factor in food—how time itself and repeated consumptions of the food item modify acceptance (29).

Directional Liking Scales

Although researchers use many different liking scales, other scales also incorporate aspects of liking. The "directional scale," often used to guide product development is a prime example. Directional scales combine intensity with liking, as shown by the following statement:

"Considering the amount of visible spice in this product, is the amount of spice: Too little, Just right, Too much? (Choose the appropriate answer.)"

Directional questions assume that the panelist knows the "optimal level" or "ideal level" of the attribute. When evaluating the product the panelist also estimates deviations from that optimum. Directional scales appeal to product developers because they appear, superficially, to provide accurate direction. Ostensibly the developer need only know the degree to which the product under-delivers or over-delivers an attribute. Table 12 shows data obtained from directional scales for a complex product—spaghetti and meatballs rated on a variety of directional attributes.

In many cases the panelists cannot accurately gauge the direction of change, however. For attributes which have a strong emotional positive or negative connotation (eg, chocolate intensity, coffee bitterness, etc) the product never has enough or always has more than one desires. As a result chocolate products are often rated as not having enough "chocolate" on the directional scales, whereas coffee is too often rated as having too much bitterness, even when it has too little bitter taste to make it palatable. Panelists do not know their ideal points for these emotion laden attributes. They find it hard to provide direction to product developers, who consequently continue to interpret literally the panelist's desire for more of the attribute, (eg, add more chocolate), never quite reaching the goal of "just right." Furthermore, all too often consumers may request several incompatible changes in the product, making the data less usable. Despite these inadequacies and failings, however, directional scales are widely used in product development to guide consumer driven reformulation. They are easy to use, they incorporate hedonics, and they do provide some guidance.

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