An artificial neural network model for predicting flavour intensity in blackcurrant concentrates

Food Quality and Preference

Volumn 13, Issue 2, 2002, Pages 117-128

  Boccorh, Raymond K ^a   Paterson, Alistair ^a  

^a UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

Artificial neural networks; Blackcurrant; Flavour modelling; Fruit flavour; Multivariate statistical analyses

Indexed keywords

SORBENT;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; FLAVOR; FOOD PROCESSING; FRUIT JUICE; GAS CHROMATOGRAPHY; GEOGRAPHIC DISTRIBUTION; LEARNING; NONHUMAN; NONLINEAR SYSTEM; PERCEPTION; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; RANDOMIZATION; REGRESSION ANALYSIS; SCORING SYSTEM; SEASONAL VARIATION; SIMULATION; TRAINING;

EID: 0036149981     PISSN: 09503293     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0950-3293(01)00072-6     Document Type: Article

References (47)

1. Predicting protein function with artificial neural networks
2. Sensory-instrumental correlations by combining data analysis and neural networks techniques
3. An analysis of the relationships between composition of blackcurrant concentrates and intensity of formulated beverage flavour
4. Quantifying flavour in blackcurrant drinks from fruit concentrates
5. Development if a model for intensity of flavour character in blackcurrant concentrates
6. Sources of variations in aroma-active volatiles, or flavour components in blackcurrant concentrates
7. Extraction of flavour components to quantify overall sensory character in a processed blackcurrant (Ribes nigrum L.) concentrate
8. Optimal minimal neural interpretation of spectra
9. 3. Graphical models for discovering knowledge
10. 6. Bayesian classification (auto-class): Theory and results
11. Neural networks: A review from a statistical perspective
12. Multivariate analysis in practice
13. 1. From data mining to knowledge discovery: An overview
14. Some psychophysical notes on the use of the odour unit number
15. Statistical relationship between methyl sulphide content and aroma intensity in canned tomato juice
16. Comparison of PLS, PCR and MLR for the quantitative determination of foreign oils and fats in butter of several European countries by their triglyceride composition
17. Determination of adulteration of butter fat by its triglyceride composition obtained by GC. A comparison of the suitability of PLS and neural networks
18. An introduction to computing with neural networks
19. Designs to balance the effect of order of presentation and first order carry-over effects in Hall tests
20. Partial least squares regression
21. Multivariate calibration
22. Preference of cauli-flower related to sensory descriptive variables by partial least squares (PLS) regression
23. A layman's guide to multivariate data analysis
24. A study of carbonyl compounds in beer. Part 2. Flavour and flavour thresholds of aldehydes and ketones added to beer
25. Machine learning, neural and statistical classification
26. Product testing and sensory evaluation of foods: Marketing and research and development approaches
27. Transient signal modelling for fast odour classification
28. Food quality prediction with neural networks
29. Predicting cheese quality using neural networks
30. Time to get fired up: In depth, neural networks
31. A quality comparison of frozen and refrigerated cooked sliced beef. 2. Relationships between gas chromatographic data and flavour profiles
32. Prediction of flavour intensity of blackcurrant (Ribes nigrum L.) drinks from compositional data on fruit concentrates by partial least squares regression
33. Multivariate data correlations by combining data analysis and neural networks techniques
34. Pattern recognition and neural networks
35. Principles of aerodynamics
36. Learning internal representations by error propagation
37. Nonlinear multivariate calibration methods in analytical chemistry
38. Factor influencing food preferences and choice
39. Neural networks in sensory perception
40. Correlating instrumental and sensory flavour data
41. Neural computing: Theory and practice
42. A statistical theory of adaptation
43. Using artificial neural networks to develop prediction models for sensory-instrumental relationships; an overview
44. The use of perceptual space approaches for determining the influence of intrinsic and extrinsic factors on food choice
45. Flavour quality-understanding the relationship between sensory responses and chemical stimuli. What are we trying to do? The data, approaches and problems
46. Relating chemical/physical and sensory data in food acceptance studies