Analyzing Differences Among Products and Panelists by Multidimensional Scaling

Popper, Richard; Heymann, Hildegarde

doi:10.1016/s0922-3487(96)80030-x

Cited by 57 publications

(50 citation statements)

References 39 publications

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“…Several authors have reached the same conclusions when comparing classical and weighed multidimensional scaling methods. 38,39 In Fig 3, individual panellists' weights for each dimension from INDSCAL analysis are plotted. This plot shows the existence of differences among assessors' sensations, perceptions or cognition.…”

Section: Results and Discussion Direct Similarity Measures Cheese Sammentioning

confidence: 99%

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Sensory characterisation of ewe's milk cheeses using direct and indirect similarity measures: a comparison

et al. 2002

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The purpose of the present study is to compare several direct and indirect (pro®le and hedonic scores) similarity measures. Moreover, the main relationships among assessors, sensory attributes and ewe's milk cheeses have been explored using different statistical techniques. For this purpose, visual inspection and correlation coef®cients among cheese sample sensory dimensions have been considered. All the solutions lead to very similar spatial con®gurations, except for preference data. In this case the considered techniques are very useful tools in the study of sensory attributes. Individual differences among assessors have been demonstrated to be quite high. Thus it is clear that methods that consider this variability in the data should be used, to which end, assessors should undergo speci®c training depending on the tests to be performed.

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Section: Results and Discussion Direct Similarity Measures Cheese Sammentioning

confidence: 99%

“…Data were treated as non-metric. 38 The Bidimensional ALSCAL solution presented a very low stress value (0.057) and a quite elevated RSQ value (0.98). Looking at these two indices, the selected multidimensional scaling model must be considered as very good.…”

Section: Indirect Similarity Measures-sensory Profilementioning

confidence: 93%

Sensory characterisation of ewe's milk cheeses using direct and indirect similarity measures: a comparison

et al. 2002

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“…RF graphical outputs are multidimensional scaling projections [35] of the data set that utilise a particular measure of distance among samples based on the internal assignation of classes in the RF ensemble. Granitto et al [36] discussed the use of this tool in the analysis of food data.…”

Section: Ptr-tof-ms Analysismentioning

confidence: 99%

PTR‐TOF‐MS and data‐mining methods for rapid characterisation of agro‐industrial samples: influence of milk storage conditions on the volatile compounds profile of Trentingrana cheese

et al. 2010

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Proton transfer reaction-mass spectrometry (PTR-MS), a direct injection mass spectrometric technique based on an efficient implementation of chemical ionisation, allows for fast and high-sensitivity monitoring of volatile organic compounds (VOCs). The first implementations of PTR-MS, based on quadrupole mass analyzers (PTR-Quad-MS), provided only the nominal mass of the ions measured and thus little chemical information. To partially overcome these limitations and improve the analytical capability of this technique, the coupling of proton transfer reaction ionisation with a time-of-flight mass analyser has been recently realised and commercialised (PTR-TOF-MS). Here we discuss the very first application of this new instrument to agro-industrial problems and dairy science in particular. As a case study, we show here that the rapid PTR-TOF-MS fingerprinting coupled with data-mining methods can quickly verify whether the storage condition of the milk affects the final quality of cheese and we provide relevant examples of better compound identification in comparison with the previous PTR-MS implementations. In particular, 'Trentingrana' cheese produced by four different procedures for milk storage are compared both in the case of winter and summer production. It is indeed possible to set classification models with low prediction errors and to identify the chemical formula of the ion peaks used for classification, providing evidence of the role that this novel spectrometric technique can play for fundamental and applied agro-industrial themes.

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“…In these previous studies, the MDS could work well to obtain a holistic view of relationships between objects, but was dependent on the direct similarity rating between objects. However, the MDS is not limited to direct similarity data, and the direct similarity approach may be inadequate because of no information regarding the relation between objects and features (Popper and Heymann, 1996). In relatively recent times, a pioneering modification of the reconstruction method has been proposed (De Deyne et al, 2008;Dry and Storms, 2009): Similarity data between objects are indirectly derived from outputs in a feature-by-object applicability judgment test, as used in our study.…”

Section: Procedures 2: Feature-animal Matching Verification Testmentioning

confidence: 99%

Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories

Soshi¹,

Fujimaki²,

Matsumoto³

et al. 2017

Front. Commun.

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Accumulating evidence suggests that category representations are based on features. Distinguishing features are considered to define categories, because of all-or-none responses for objects in different categories; however, it is unclear how distinguishing features actually classify objects at various category levels. The present study included 75 animals within three classes (mammal, bird, and fish), along with 195 verbal features. Healthy adults participated in memory-based feature-animal matching verification tests. Analyses included a hierarchical clustering analysis, support vector machine, and independent component analysis to specify features effective for classifications. Quantitative and qualitative comparisons for significant features were conducted between super-ordinate and sub-ordinate levels. The number of significant features was larger for super-ordinate than sub-ordinate levels. Qualitatively, the proportion of biological features was larger than cultural/affective features in both the levels, while the proportion of affective features increased at the sub-ordinate level. To summarize, the two types of features differentially function to establish category representations.

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Analyzing Differences Among Products and Panelists by Multidimensional Scaling

Cited by 57 publications

References 39 publications

Sensory characterisation of ewe's milk cheeses using direct and indirect similarity measures: a comparison

Sensory characterisation of ewe's milk cheeses using direct and indirect similarity measures: a comparison

PTR‐TOF‐MS and data‐mining methods for rapid characterisation of agro‐industrial samples: influence of milk storage conditions on the volatile compounds profile of Trentingrana cheese

Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories

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