Non-destructive egg freshness determination: an electronic nose based approach

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Abstract
An electronic nose (EN) based system, which employs an array of four inexpensive commercial tin-oxide odour sensors, has been used to analyse the state of freshness of eggs. Measurements were taken from the headspace of four sets of eggs over a period of 20–40 days, two 'types of egg data' being gathered using our EN; one type of 'data' related to eggs without a hole in the shells and the other type of 'data' related to eggs wherein we made tiny holes in the shells. Principal component analysis, fuzzy C means, self-organizing maps and 3D scatter plots were used to define regions of clustering in multisensor space according to the state of freshness of the eggs. These were correlated with the 'use by date' of the eggs. Then four supervised classifiers, namely multilayer perceptron, learning vector quantization, probabilistic neural network and radial basis function network, were used to classify the samples into the three observed states of freshness. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to predict egg freshness into one of three states with up to 95% accuracy. This shows good potential for commercial exploitation.