ISSN:
1750-3841
Source:
Blackwell Publishing Journal Backfiles 1879-2005
Topics:
Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
,
Process Engineering, Biotechnology, Nutrition Technology
Notes:
: A quantitative procedure was developed to predict the composition of ternary ground spice mixtures using an electronic nose. Basil, cinnamon, and garlic were mixed in different compositions and presented to an e-nose. Nineteen training mixtures were used to build predictive models. Model performance was tested using 5 other mixtures. Three neural network structures—multilayer perceptron (MLP), MLP using principal component analysis as a preprocessor (PCA-MLP), and the time-delay neural network (TDNN)—were used for predictive model building. All 3 neural network models predicted the testing mixtures' compositions with a mean square error (MSE) equal or less than 0.0051 (in a fraction domain where sum of fractions = 1). The TDNN provided the smallest MSE.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1111/j.1365-2621.2005.tb07180.x
Permalink
