Hildegarde Heymann

Sensory Evaluation of Food

The first € price and the £ and

Preference mapping: relating acceptance of ''creaminess'' to a descriptive sensory map of a semi-solid

price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. H.T. Lawless, H. Heymann Sensory Evaluation of Food

Development and use of time-intensity methodology for sensory evaluation: A review

Abstract Creaminess is used by consumers to describe the texture of many food products. The overall objective of this study was to investigate the underlying sensations to the acceptance of textural creaminess. Eight puddings varying in thickness, mouthcoating, rate of melt and smoothness were developed by altering the amount and type of starch, amount of milk-fat and amount of sodium salts. Puddings were evaluated by descriptive analysis for appearance, texture and flavor characteristics. Concurrently, consumers evaluated the puddings for “liking of creamy texture”. Sensory descriptive data were subjected to principal component analysis, resulting in a multidimensional product space that was related to the consumer acceptance data using the AUTOFIT selection strategy. More than 90% of consumer responses were selected and validated by AUTOFIT. A dimension related to thickness seemed important to consumer acceptance of creamy texture. In general, hedonic scores for creamy texture were higher for samples that were smoother and had more dairy flavor, although, hedonic scores for creamy texture varied considerably on dimensions related to dairy flavor and smoothness.

Principles of Good Practice

Abstract Time-intensity (TI) evaluation has been a developing sensory methodology for some 40 years. During this time, the technology has evolved from the use of a simple paper/pen to computerized data collection. Concomitant with this development has been the refinement of average curve calculation and interpretation. This paper summarizes these developments, cross-references alternate nomenclature, and compiles and reviews the research applications.

Textural characteristics of lowfat, fullfat and smoked cheeses: sensory and instrumental approaches

In later chapters of this textbook, we will often state that a particular method should be performed using standard sensory practices. This chapter will describe what we mean by “standard sensory practices” Table 3.1 provides a checklist of many of the good practice guidelines discussed in this chapter; this table can be used by sensory specialists to ensure that the study has been thought through. It should be remembered that a good sensory specialist will always follow the standard practices because that would help ensure that he or she will obtain consistent, actionable data. However, an experienced sensory scientist will occasionally disregard the standard practice guidelines. When one breaks these rules, one always has to be fully aware of the consequences, the risks entailed, and whether one still can get valid data from the study.

Application of GPA and PLSR in correlating sensory and chemical data sets

Abstract Research on the comparison of textural properties of lowfat, fullfat and smoked cheeses is yet to be reported. The objective of this study was to find textural differences in these cheeses by sensory and instrumental methods and to try to correlate the sensory and the instrumental data. Nine commercial cheeses were evaluated for aroma, flavor, and texture by descriptive analysis and were also subjected to instrumental texture profile analysis. The sensory evaluation by a panel of nine judges separated the cheeses into ‘smoked’ and ‘non-smoked’ based on aroma and flavor. On the basis of texture the judges categorized the cheeses into either ‘dry and crumbly’ or ‘sticky and creamy’. The texture profile analysis segregated the cheeses into ‘smoked’ and ‘non-smoked’. ‘Hardness’ (instrumental) was positively correlated to sensory texture attributes such as ‘dry’, ‘hardness’ and ‘crumbly’, which might indicate hardness of cheeses during mastication. The judges that participated in the panel were found to be very consistent as a group in their evaluation.

Acceptance and Preference Testing

Abstract This paper discusses the application of various multivariate statistical procedures to understand the relationship between sensory and instrumental flavor profiles. Ice cream with varying fat levels was used as the vehicle for the flavor compounds in the experiment. Chemical and sensory flavor profiles were obtained by modified dynamic headspace analysis and descriptive analysis, respectively. Chromatographic peak areas of flavor-volatiles were used as the variables for the chemical data set. Initially, principal component analysis and canonical variate analysis were performed separately on the chemical and sensory data sets to explore the structure of each set. Flavor volatiles were then further studied to investigate their impact on the sensory profile of ice cream using general procrustes analysis and partial least squares regression analysis. The results from the two statistical analysis methods are compared and discussed. Additionally, the effect of log-transformation of chemical data on the overall chemical–sensory relationship was evaluated within each statistical method.

Color and Appearance

Consumer sensory evaluation is usually performed towards the end of the product development or reformulation cycle. At this time, the alternative product prototypes have usually been narrowed down to a manageable subset through the use of analytical sensory tests. Frequently, the sensory testing is followed by additional testing done through market research. The big difference between consumer sensory and marketing research testing is that the sensory test is generally conducted with coded, not branded, products, while market research is most frequently done with branded products (van Trijp and Schifferstein, 1995). Also, in consumer sensory analysis the investigator is interested in whether the consumer likes the product, prefers it over another product, or finds the product acceptable based on its sensory characteristics. The consumer sensory specialist often has no interest in purchase intent, effect of branding, and/or cost factors. Thus, a product will not necessarily be financially successful just because it had high hedonic scores (was well liked) or because it was preferred over another product. Success in the marketplace is also affected by price, market image, packaging, niche, etc. However, a product that does not score well in a consumer acceptance test will probably fail despite great marketing.

Alternatives to data averaging of consumer preference data

In food products, especially meats, fruits, and vegetables, the consumer often assesses the initial quality of the product by its color and appearance. The appearance and color of these products are thus the primary indicators of perceived quality. The importance of color and appearance can also be demonstrated when we think of drinking milk from a Coca-Cola bottle, when we choose bananas in the grocery store (a green-yellow-black continuum that indicates ripeness), when a friend serves green-colored bread and beer on St Patrick’s day, and when someone serves us a watermelon with yellow flesh instead of the more usual red. In food processing and cooking, color serves as a cue for the doneness of foods and is correlated with changes in aroma and flavor. Simple examples include the browning of baked and fried foods. For other foods, color or lightness is important to identity and grading, such as the lightness of canned tuna fish. Scientific studies have also shown that the color of the product affects our perception of other attributes, such as aroma, taste, and flavor. For example, DuBose and Cardello et al. (1980) found that the number of correct identifications of fruit-flavored beverage flavors decreased significantly when the beverage was atypically colored, and that the number of correct identifications increased when the beverages was colored correctly. Christensen (1983) found that when sighted panelists scored the aroma intensity of appropriately and inappropriately colored cheese, soy analog bacon, margarine, raspberry-flavored gelatin, and orange drink, the perceived intensity of the appropriately colored product was higher than for the inappropriately colored product. Interestingly, the bacon analog was a notable exception.

Physiological and Psychological Foundations of Sensory Function

Abstract The relationship between a consumer preference data set and a corresponding sensory profile on eight cooked wheat noodles with different formulas was examined using several multivariate techniques. Individual consumer hedonic responses (100 noodle/pasta consumers) and eight appearance and texture sensory attributes were collected. The consumer preference data were treated in two different ways: mean values averaged across all consumers or principal components extracted from individual responses. The mean preference scores were submitted to both principal component stepwise regression and partial least squares regression (PLS1), whereas the summarized major preference components were subjected to canonical correlation analysis, as well as partial least squares regression (PLS2). The results suggested that in case of complex consumer data, using mean value can only capture the most manifest trends in consumer preference patterns, while studying individual responses and by further categorizing major preference patterns provide an opportunity to discover the hidden information that are masked by data averaging.