Sensory data

Procrustes analysis has been generalized in two ways. One extension is that more than two data sets may be considered. In that case the algorithm is iterative. One then must rotate, in turn, each data set to the average of the other data sets. The cycle must be repeated until the fit no longer improves. Procrustes analysis of many data sets has been applied mostly in the field of sensory data analysis [4]. Another extension is the application of individual scaling to the various data sets in order to improve the match. Mathematically, it amounts to multiplying all entries in a data set by the same scalar. Geometrically, it amounts to an expansion (or... 

Let us try to relate the (standardized) sensory data in Table 35.1 to the explanatory variables in Table 35.3. Essentially, this is an analysis-of-variance problem. We try to explain the effects of two qualitative factors, viz. Country and Ripeness, on the sensory responses. Each factor has three levels Country = Greece, Italy,... 

As an example we try to model the relation between the sensory data of Table 35.1 and the instmmental measurements of Table 35.4. The PLS analysis results are shown in Table 35.8. The first PLS dimension loads about equally high on... 

The determination and analysis of sensory properties plays an important role in the development of new consumer products. Particularly in the food industry sensory analysis has become an indispensable tool in research, development, marketing and quality control. The discipline of sensory analysis covers a wide spectrum of subjects physiology of sensory perception, psychology of human behaviour, flavour chemistry, physics of emulsion break-up and flavour release, testing methodology, consumer research, statistical data analysis. Not all of these aspects are of direct interest for the chemometrician. In this chapter we will cover a few topics in the analysis of sensory data. General introductory books are e.g. Refs. [1-3]. 

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. 

P. Lea, T. Naes and M. R0dbotton, Analysis of Variance for Sensory Data. Wiley, London, 1997 D. H. Lyon, M. A. Francombe, T. A. Hasdell and K. Lawson, Guidelines for Sensory Analysis in Product Development and Quality Control. Chapman and Hall, London, 1990. 

While ANNs cannot do anything that would be impossible to accomplish using an alternative algorithmic method, they can execute tasks that would otherwise be very difficult, such as forming a model from sensory data or from data that is extracted from a continuous industrial process for which no comprehensive theoretical model exists. Their principal limitation is that the numerical model that they create, while open to inspection, is difficult to interpret. This is in marked contrast to the model used by expert systems, in which the knowledge of the system is expressed in readily interpreted statements of fact and logical expressions. Nevertheless, the power of the ANN is... 

ANNs are built by linking together a number of discrete nodes (Figure 2.5). Each node receives and integrates one or more input signals, performs some simple computations on the sum using an activation function, then outputs the result of its work. Some nodes take their input directly from the outside world others may have access only to data generated internally within the network, so each node works only on its local data. This parallels the operation of the brain, in which some neurons may receive sensory data directly from nerves, while others, deeper within the brain, receive data only from other neurons. 

Burgard, D. R., Kuznicki, J. T. Chemometrics Chemical and Sensory Data. CRC Press, Boca Raton, FL, 1990. 

Fos-like immunoreactivity in the MPOA and lateral preoptic area (LPOA) is associated with maternal behavior in both virgin female rats exposed to pups and lactating postpartum females (Numan and Numan, 1992). The observed induction of fos activity in the POA is likely partially due to the receipt of olfactory and ventral tactile and suckling-related sensory stimulation from pups. The POA also receives suckling-related sensory data related to lactation. However, because maternal behavior occurs in females who have been thelectomized (nipple removal) or olfactory bul-bectomized, a population of POA output cells exists that is essential for the performance of maternal duties in the absence of the usual sensory inputs (Numan, 1994). 

Two kinds of mixed solutions were provided to test the hypothesis. One was a mixed solution of D-phenylalanine and L-phenylalanine. The second was a mixed solution of sucrose and L-phenylalanine. While the chemical properties of d- and l-phenylalanine are the same, for the most part, they do possess different tastes. Sucrose has a structure and taste that is significantly different from L-phenylalanine. The sensory data obtained from a five member sensory panel is shown in Table ni. 

The chemical, instrumental and sensory data presented above indicated that storage of cooked beef affects the lipid composition and concomitantly, the flavor of beef. The data also indicated that primary tastes like bitter and sour are affected by storage. 

The authors wish to thank Chris Gotts for collecting the sensory data Peter Bladon for assistance in calculating molecular descriptors. 

Fischer U (1995) Mass balance of aroma compounds during the dealcoholization of wine correlation of chemical and sensory data. Universitat Hannover, Hannover... 

Our efforts to correlate instrumental and sensory data have initially centered around two areas, both of which are somewhat simpler than the prediction of sensory response to cigarette smoke. The first was aimed at developing a model for a quality... 

Since the sensory data collected involved degree of sample difference from a reference, it was felt that the analytical data should be analyzed in a similar manner. In cases where some peaks making up a multicomponent mixture are known to be specific to that mixture, this is a relatively simple matter. In such cases, the peak areas of the known components can be compared to a reference and average percent difference calculated. However, if it is not possible to pick out peaks that are clearly specific to a single multicomponent mixture, a more sophisticated technique such as factor analysis is required. There are circumstances where all peaks are common to each multicomponent mixture, i.e. qualitatively similar but quantitatively different. Also there are cases where peaks are found only in one of the multicomponent mixtures, but it is not clear to which mixture they belong. In these cases factor analysis is required to extract patterns that are characteristic of the specific multicomponent mixtures. Analytical concentrations of each of the multicomponent mixtures are then calculated as a set of factor scores where each score is directly proportional to the actual concentration of each multicomponent mixture. 

Correlation of Analytical/Sensory Results. Sensory data was correlated with headspace data of tobacco volatiles by factor analysis (BMDP4M) and canonical correlation BMDP6M. Analytical data included factor scores and discriminant analyses scores sensory data included scores from the two MDS dimensions. Sorted rotated factor loadings of combined sensory/analytical data using factor analysis are shown in Table II. Factor one contained those variables from the analytical and sensory data which related to differences between bright (A), burley (B), and oriental (C) (Figure 10). These included dimension 1 in the... 

Figure 10. Factor score plots of analytical/sensory data. Labels are as in Figure T-...

Both factor analysis and canonical correlation techniques were successful in demonstrating that differences between tobacco type and casing could be detected from both analytical and sensory data, and that those differences found analytically were highly correlated to sensory differences. From this type of data correlation, components can be pinpointed which may be responsible for sensory differences between tobacco types. 

The two research investigations reported here - the sensory quality control specification model and the application of sensory and analytical data for defining differences in tobacco aroma - both demonstrate the usefulness of multivariate analysis techniques for analyzing analytical and sensory data as well as correlating these data. Although these tasks do not compare in complexity to that of the prediction of sensory response to analytical data collected on cigarette smoke, our research to date has revealed no element which indicates that this is an impossible task. In fact, the results of these and similar... 

Dupuy and coworkers have reported a direct gas chromatographic procedure for the examination of volatiles in vegetable oils (11). peanuts and peanut butters (12, 13), and rice and com products (14). When the procedure was appTTed to the analysis of flavor-scored samples, the instrumental data correlated well with sensory data (15, 16, 17), showing that food flavor can be measured by instrvmental means. Our present report provides additional evidence that the direct gas chromatographic method, when coupled with mass spectrometry for the identification of the compounds, can supply valid information about the flavor quality of certain food products. Such information can then be used to understand the mechanisms that affect flavor quality. Experimental Procedures... 

Describes a simple and sensitive spectrophotometric method to estimate the content of total carbonyl compounds in rancid fats and foods by trapping them with 2,4-DNPH the technique determines total carbonyls, including those that are nonvolatile, decreasing the ability of the assay to correlate well with sensory data. Although gas chromatographic techniques are better suited for determining volatile carbonyl compounds from lipid oxidation, this is still the classical colorimetric assay. 

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