Multivariate sensory analysis

IRMS LC MDGC MS MSA NIF NMR OAV OSV PCA RAS RP SDE SFE SIM SNIF SPME TIC TLC Stable Isotope Ratio Mass Spectrometry Liquid Chromatography MultiDimensional Gas Chromatography Mass Spectrometry Multivariate Sensory Analysis Nasal Impact Frequency Nuclear Magnetic Resonance spectroscopy Odor Activity value Odor Spectrum Value Principal Component Analysis Retronasal Stimulation Reversed Phase Simultaneous steam Distillation Extraction Supercritical Fluid Extraction Selected Ion Monitoring Surface of Nasal Impact Frequency Solid Phase Micro Extraction Total Ion Current Thin Layer Chromatography... 

Ennis D.M. (1988) Multivariate sensory analysis. Multidimensional mathematical models use momentary psychological magnitudes in judgement functions to predict human sensory responses. Food Technol. (Chicago) 42 (11), 118-22. 

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. 

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. 

T. Naes and E. Risvik (Editors), Multivariate Analysis of Data in Sensory Science. Data Handling in Science and Technology Series, Elsevier, Amsterdam, 1996 J.R. Piggott (Editor), Sensory Analysis of Foods. Elsevier, London, 1984. 

Thybo, A. K., Martens, M. (1998). Development of a sensory texture profile of cooked potatoes by multivariate data analysis. Journal of Texture Studies, 29,453 68. 

Sensory. Although the basis for multivariate analysis was developed in the early 1900 s, its use in sensory analysis is relatively recent. These types of statistics, however, have been valuable in dealing with two fundamental problems which occur in sensory testing. First there are difficulties encountered when one attempts to breakdown complex sensory parameters into single semantic terms which can be rated, and second it is difficult to achieve the goal of every panelist having the same internal understanding of each term. Approaches to minimize these difficulties included 1) evaluation of semantic terms used by the panel to determine if the variables are unique or can be condensed to a new set of unique variables 2) evaluation of the panelists use of semantic terms to determine inconsistencies as well as the relative importance of the terms to food quality or discrimination.(8)... 

Some of the most interesting advances in sensory analysis in the last 20 years have been in the area of statistical evaluation of the results. Multivariate analysis is an example of a new type of statistical analysis applied to food system. Multivariate... 

In a recent study, using multivariate statistical analysis of quantitative sensory descriptive analysis and precise chemical compositional data, Smyth et al. (2005) found that the importance of individual yeast esters to the aroma profile of wine can vary with the type of wine. In the case of unwooded Chardonnay wines, for... 

White (1995), not for a sensory analysis but mainly with a view to determining coffee adulterations, used the data of combined headspace GC and high-performance LC for multivariate analysis. Principal component analysis visualized the relationship between samples, and the outlying samples could be identified. The method could be an additional tool for classification and quality control of coffee products. 

According to Ennis (1988), the application of the various multivariate analysis techniques (factor, cluster, discriminant analysis, multidimensional scaling) to classification in sensory analysis has been very valuable but is of little help for understanding the modes of perception. Mathematical models are proposed for predicting human sensory responses and the author concludes that they need development before they are able to improve the understanding of the complex perceptions associated with foods and beverages . 

Arvanitoyannis, IS. and Vlachos, A. Implementation of physicochemical and sensory analysis in conjunction with multivariate analysis towards assessing olive oil authentication/adulteration. Critical Reviews in Food Science and Nutrition, 47, 441 98. 2007. 

Another threat lies in the way results from rapid sensory profiling methods are communicated to stakeholders, or to anyone else, who are not familiar with these methods. First, it must be noted that multivariate data analysis and sensory maps are not understood by everyone, which may be serionsly hazardous if results are... 

Guedes de Pinho, P. Bertrand, A. Alvarez, P. Wine characterization by multivariate statistical analysis of the sensory and chemical data. In Trends in Flavor Research H. Maarse and D.G. van der Heij, eds. Elsevier Publ. Amsterdam, 1994. p. 229. 

Which food area would require explorative multivariate data analysis tools We have seen in the introduction section that food science today embraces a wide multidisciplinary ambit, involving chemistry, biology/micro-biology, genetics, medicine, agriculture, technology and environmental science, and also sensory and consumer analysis as weU as economy. 

The first reported use of PTR-MS for food, rather than for drink, perception was by Gasperi et al. [19]. This was also the first food study that compared classical sensory analysis with VOC headspace analysis by PTR-MS. The foods chosen for investigation were seven varieties of Italian mozzarella cheese, for which measurements were made while they were held at a temperature of 36°C. By using a multivariate statistical data analysis approach. 

EEG measures were obtained from three sites during a passive sensory conditioning task of 35 trials. The data for more than a third of the subjects (particularly younger ones) were rejected for medical, technical and behavioural reasons, and a slow-wave analysis was carried out on the data obtained from 41 children. Two years later some of these children were followed up and valid data, based on 100 trials, were obtained for 28 children. A multivariate regression analysis was carried out to allow for possible effects of sex, age, SES and IQ, and to test for nonlinear effects of age and blood lead. These analyses showed that slow-wave voltage varied as a linear function of blood lead, but that the slope of this relationship varied with age. In children aged under 5, slow-wave voltage tended to be positive at lead levels below... 

Volume 16 Multivariate Analysis of Data in Sensory Science, edited by T. Naes and E. Risvik... 

Volume 16 Multivariate Analysis of Data in Sensory Science, edited by T. Naes and E. Risvik Volume 17 Data Analysis for Hyphenated Techniques, by E.J. Karjalainen and U.P. Karjalainen Volume 18 Signal Treatment and Signal Analysis in NMR, edited by D.N. Rutledge Volume 19 Robustness of Analytical Chemical Methods and Pharmaceutical Technological Products, edited by M.M.W.B. Hendriks, J.H. de Boer and A.K. Smilde... 

Human perception of flavor occurs from the combined sensory responses elicited by the proteins, lipids, carbohydrates, and Maillard reaction products in the food. Proteins Chapters 6, 10, 11, 12) and their constituents and sugars Chapter 12) are the primary effects of taste, whereas the lipids Chapters 5, 9) and Maillard products Chapter 4) effect primarily the sense of smell (olfaction). Therefore, when studying a particular food or when designing a new food, it is important to understand the structure-activity relationship of all the variables in the food. To this end, several powerful multivariate statistical techniques have been developed such as factor analysis Chapter 6) and partial least squares regression analysis Chapter 7), to relate a set of independent or "causative" variables to a set of dependent or "effect" variables. Statistical results obtained via these methods are valuable, since they will permit the food... 

MULTIVARIATE PRINCIPAL COMPONENTS ANALYSIS OF AGING BEEF Multivariate principal component or factor analysis was performed on data obtained fi-om samples of aging beef (described above). Factor analysis was used since this method facilitates the visual examination of existing relationships (correlations) among the experimental treatments and the sensory, chemied. 

One of the major uses of multivariate techniques has been the discrimination of samples based on sensory scores, which also has been found to provide information concerning the relative importance of sensory attributes. Techniques used for sensory discrimination include factor analysis, discriminant analysis, regression analysis, and multidimensional scaling (8, 10-15). 

While the problem of relating sensory response to a simple mixture is difficult, this is compounded when efforts are made to relate sensory response to the thousands of components contained in cigarette smoke. As with many food systems, the differences are essentially quantitative rather than qualitative. The use of multivariate techniques are essential since they are designed to deal with all the peaks of a chromatographic profile. Fortunately, many of these components are highly correlated with others, and therefore simpler variables can be extracted through techniques such as factor analysis. 

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... 

Multivariate analysis is not a panacea for all flavor problems. It is a valuable tool which should be used in conjunction with other sensory and analytical skills to solve flavor problems. The availability of a programmable chromatographic data system makes implementation of MVA straightforward. 

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