Sensory attributes

Vareltzis, K.R and Buck, E.M., Color stability and sensory attributes of chicken frankfurters made with betalains and potassium sorbate versus sodium nitrite, J. Food Protect., 47, 41, 1984. 

Leafy vegetables and some fruits in particular are rich sources of chlorophylls. However, they are ranked among the most perishable post-harvest products and must be consumed within a few days after harvest or subjected to preservation methods to extend their freshness. Their typical green color is, if not the most important sensory attribute, an extremely important parameter of quality. Any discoloration can lead to rejection by consumers as the bright green color is intuitively linked with freshness. 

Food colorants are analyzed either by direct inspection (sensorial analyses) or by physical or physicochemical instrumental methods. Direct inspections determine the sensorial attribute of color, frequently combined with assessments of smells and flavors. Visual color assessment is subjective and may be used with reliable visual evaluations controlling multiple variables. 

Additionally, color may also serve as a key to cataloging a food as safe. Undesirable colors of meats, fruits, and vegetables warn us about potential dangers or at least of the presence of undesirable flavors. Color and other sensory attributes are even misused as indicators for safety. Walker and co-authors demonstrated that in small and medium enterprises, more than 50% of food handlers thought that they could tell whether food was contaminated with food poisoning bacteria by sight, smell, and taste. Color is thus used as a way to identify a food and judge its quality."... 

The overlapping of textural attributes suggests that characterisation of this kind of jellies could be based on the evaluation of a single parameter. The concept of hardness being the easiest to apprehend and due to its close relation with the same sensory attribute, we believe that when jellies are to be appreciated from a textural point of view, hardness may be measured on its own. 

British panel. Note that the sensory attributes are to some extent different. Table 35.3 gives some information on the country of origin and the state of ripeness of the olives. Finally, Table 35.4 gives some physico-chemical data on the same samples that are related to the quality indices of olive oils acid and peroxide level, UV absorbance at 232 nm and 270 nm, and the difference in absorbance at wavelength 270 nm and the average absorbance at 266 nm and 274 nm. 

Given these tables of multivariate data one might be interested in various relationships. For example, do the two panels have a similar perception of the different olive oils (Tables 35.1 and 35.2) Are the oils more or less similarly scattered in the two multidimensional spaces formed by the Dutch and by the British attributes How are the two sets of sensory attributes related Does the... 

Fig. 35.4. Correlations of sensory attributes with principal axes of average configuration after Procrustes matching (Dutch panel lower case British panel upper case).

Some of the results are collected in Table 35.7. Table 35.7a shows that some sensory attributes can be fitted rather well by the RRR model, especially yellow and green (/ == 0.75), whereas for instance brown and syrup do much worse R 0.40). These fits are based on the first two PCs of the least-squares fit (Y. The PCA on the OLS predictions showed the 2-dimensional approximation to be very good, accounting for 99.2% of the total variation of Y. The table shows the PC weights of the (fitted) sensory variables. Particularly the attributes brown , and to a lesser extent syrup , stand out as being different and being the main contributors to the second dimension. 

By combining the coefficients in the two parts of the table one can express each sensory attribute in terms of the explanatory factors. Note that the above regression... 

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. 

It is not strictly required to use the same attributes in each data set. This allows the comparison of independent QDA results obtained by different laboratories or development departments in collaborative studies. Also within a single panel, individual panellists may work with personal lists of attributes. When the sensory attributes are chosen freely by the individual panellist one speaks of Free Choice Profiling. When each panellist uses such a personal list of attributes, it is likely that... 

So far, the nature of the variables was the same for all data sets, viz. sensory attributes. This is not strictly required. One may also analyze sets of data referring to different types of data (processing conditions, composition, instrumental measurements, sensory variables). However, regression-type methods are better suited for linking such diverse data sets, as explained in the next section. 

A table of correlations between the variables from the instrumental set and variables from the sensory set may reveal some strong one-to-one relations. However, with a battery of sensory attributes on the one hand and a set of instrumental variables on the other hand it is better to adopt a multivariate approach, i.e. to look at many variables at the same time taking their intercorrelations into account. An intermediate approach is to develop separate multiple regression models for each sensory attribute as a linear function of the physical/chemical predictor variables. 

Beilken et al. [ 12] have applied a number of instrumental measuring methods to assess the mechanical strength of 12 different meat patties. In all, 20 different physical/chemical properties were measured. The products were tasted twice by 12 panellists divided over 4 sessions in which 6 products were evaluated for 9 textural attributes (rubberiness, chewiness, juiciness, etc.). Beilken etal. [12] subjected the two sets of data, viz. the instrumental data and the sensory data, to separate principal component analyses. The relation between the two data sets, mechanical measurements versus sensory attributes, was studied by their intercorrelations. Although useful information can be derived from such bivariate indicators, a truly multivariate regression analysis may give a simpler overall picture of the relation. 

Figure 38.12 shows the position of the twelve meat patties in the space of the first two PLS dimensions. Such plots reveal the similarity of certain products (e.g. C and D, or E and G) or the extreme position of some products (e.g. A or I or L). Figure 38.13 shows the loadings of the instrumental variables on these PLS factors and Fig. 38.14 the loadings of the sensory attributes. The plot of the products in the... 

In the foregoing we loosely talked about the intensity of a sensory attribute for a given sample, as if the assessors perceive a single (scalar) response. In reality, perception is a dynamic process, and a very complex one. For example, when a food product is taken in the mouth, the product disintegrates, emulsions are broken, flavours are released and transported from the mouth to the olfactory (smell) receptors in the nose. The measurement of these processes, analyzing and interpreting the results and, eventually, their control is of importance to the food... 

Figure 38.19 shows the contour plots of the foaming behaviour, uniformity of air cells and the sweetness of a whipped topping based on peanut milk with varying com syrup and fat concentrations [16]. Clearly, fat is the most important variable determining foam (Fig. 38.19A), whereas com syrap concentration determines sweetness (Fig. 38.19C). It is rather the mle than the exception that more than one sensory attribute are needed to describe the sensory characteristics of a product. An effective way to make a final choice is to overlay the contour plots associated with the response surfaces for the various plots. If one indicates in each contour plot which regions are preferred, then in the overlay a window region of products with acceptable properties is left (see Fig. 38.19D and Sections 24.5 and 26.4). In the...

Johansson L, Haglund A, Berglund L, Lea P and Risvik E (1999) Preference for tomatoes, affected by sensory attributes and information about growth conditions . Food Quality... 

Fresh-cut fruits and vegetables are highly perishable products because of their intrinsic characteristics and the minimal processing (Ayala-Zavala and others 2008a). Microbial growth, decay of sensory attributes, and loss of nutrients are among the... 

Sensory attributes of akara made from the 1 mm screen flour hydrated to a 60% moisture content before cooking were acceptable when compared to traditional akara (H). A major difference in akara prepared from hydrated meal and that prepared from traditional paste is in the fat content of the cooked product. On a dry weight basis, traditional akara contains about 38% fat whereas akara made from meal hydrated to a 60% moisture content contains 29% fat. A frequent comment made by sensory panelists is that akara made from meal has a drier texture and mouthfeel than traditional akara. 

Field Pea Flour in Other Baked Products. When McWatters (44) substituted 8% field pea flour and 4.6% field pea concentrate for milk protein (6%) in baking powder biscuits, sensory attributes, crumb color, and density of the resulting biscuits were adversely affected. No modifications were made in recipe formulation when pea products were incorporated. The doughs were slightly less sticky than control biscuits that contained whole milk. This might be due to lack of lactose or to the different water absorption properties of pea protein or starch. Panelists described the aroma and flavor of these biscuits as harsh, beany and strong. Steam heating the field pea flour improved the sensory evaluation scores, but they were never equivalent to those for the controls. 

Specific product changes proposed include the use of ventilation, filtration, and tobacco rod density to alter draw resistance (Norman 1983 Thome 1994) the introduction of channeled or other unique filter designs to enhance sensory properties such as sensations in the mouth, referred to as mouthful feehng (Brown Williamson 1983 Greig 1987 McMurtrie and SUberstein 1980) and the use of higher nicotine tobaccos, flavor additives, and alkaline additives to increase a range of sensory attributes (Shepperd 1993 Whitehead 1994). 

Hhong et ah (2009) described the sensory attributes of morama oil as fresh, thick, creamy, and smooth with a grassy and earthy aroma and raw nutty flavor and aftertaste. Compared to both sunflower and olive oils, potato chips fried in morama oil were rated as more acceptable by consumers (Tlhong et ah, 2009). Therefore, as a cooking oil, morama oil has great potential in terms of consumer acceptability. However, its acceptability as a salad oil remains imexplored. 

Tlhong, T., Sopejame, M., Mthombeni, F., Mpotokwane, S., and Jackson, J. C. (2009). Sensory attributes of morama oil. Annual Report Marama II Project, Copenhagen, Denmark. 

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