Relating chemical/physical and sensory data in food acceptance studies

Food Quality and Preference

Volumn 1, Issue 1, 1988, Pages 25-31

  Williams, A A ^a   Rogerst, C A ^a   Collins, A J ^b  

^a Sensory Research Laboratories Ltd   (United Kingdom)

^b UNIVERSITY OF BATH   (United Kingdom)

Author keywords

chemical data; food acceptance; regression modelling; sensory data

Indexed keywords

EID: 0002107908     PISSN: 09503293     EISSN: None     Source Type: Journal    
DOI: 10.1016/0950-3293(88)90005-5     Document Type: Article

References (35)

1. Chemical indices of wine quality
2. The use of generalised Procrustes techniques in sensory analysis
3. Olfactory thresholds of odour stimuli determined by gas chromatography sniffing technique: structure activity relationships
4. Influence of storage on some sensory and physicochemical characteristics of Beaujolais Nouveau wines
5. Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory
6. Generalised Procrustes analysis
7. Minimum Variance Properties of Principal Component Regression
8. Component selection norms for principal components regression
9. RELATIONS BETWEEN TWO SETS OF VARIATES
10. The optimal set of principal component restrictions on a least-squares regression
11. Data-approximation by PLS methods
12. Partial least squares regression
13. Preference of cauliflower related to sensory descriptive variables by partial least squares (PLS) regression
14. Relating sensory and instrumental data from vegetables using different multivariate techniques
15. A behavioural interpretaton of food acceptability
16. Multicollinearity and imprecise estimation
17. On the optimality of the simultaneous redundancy transformations
18. Interpretation of the sensory significance of chemical data in flavour research, Parts I–III
19. Interpretation of the sensory significance of chemical data in flavour research, Parts I–III
20. Interpretation of the sensory significance of chemical data in flavour research, Parts I–III
21. Defining sensory quality in foods and beverages
22. Measuring the competitiveness of wine, in tartrates and concentrates
23. The use of perceptual space approaches for determining the influence of intrinsic and extrinsic factors on food choice
24. Some new ideas for measuring customer attitudes to wines
25. Procedures and problems in optimizing sensory and attitudinal characteristics in foods and beverages
26. A new approach to the sensory analysis of foods and beverages
27. Characteristics of flavour in alcoholic beverages
28. Multivariate data analysis in chemistry
29. Pattern recognition in finding and using regularities in multivariate data
30. Redundancy analysis, an alternative to canonical correlation analysis