Generalisation Techniques Using a Variational CEAE for Classifying Manuka Honey Quality

This paper presents an integrated architecture of the class embodiment autoencoder (CEAE) and variational autoencoder. The aim is to improve the generalisation of the algorithm and accordingly increase the classification accuracy of unseen samples. The proposed variational CEAE is trained by using hyperspectral images of Manuka honey dataset, then evaluated for generalisation performance on unseen brands of honey. We applied well-known generalisation techniques to this structure, and evaluated the effect of these on our dataset. Our experiment results show that the average validation set performance of the new autoencoder technique on unseen brands is 55.4%, while the average benchmark technique is 48.1% for the same unseen brands. The autoencoder structures are performing feature reduction on our data, which has shown to improve the classification accuracy and generalisation performance. We tested the feature reduction techniques in combination with K-nearest-neighbour classifier, linear support vector machine (SVM), and radial basis function SVM. This work develops an important step toward the automatic classification of Manuka honey quality using hyperspectral imaging and machine learning. This is the first work to evaluate generalisation performance in honey classification, which is crucial for a viable real-world solution.