  The origins of herbal medicines are important for their treatment effect,
which could be potentially distinguished by electronic nose system. As the odor
fingerprint of herbal medicines from different origins can be tiny, the
discrimination of origins can be much harder than that of different categories.
Better feature extraction methods are significant for this task to be more
accurately done, but there lacks systematic studies on different feature
extraction methods. In this study, we classified different origins of three
categories of herbal medicines with different feature extraction methods:
manual feature extraction, mathematical transformation, deep learning
algorithms. With 50 repetitive experiments with bootstrapping, we compared the
effectiveness of the extractions with a two-layer neural network w/o
dimensionality reduction methods (principal component analysis, linear
discriminant analysis) as the three base classifiers. Compared with the
conventional aggregated features, the Fast Fourier Transform method and our
novel approach (longitudinal-information-in-a-line) showed an significant
accuracy improvement(p < 0.05) on all 3 base classifiers and all three herbal
medicine categories. Two of the deep learning algorithm we applied also showed
partially significant improvement: one-dimensional convolution neural
network(1D-CNN) and a novel graph pooling based framework - multivariate time
pooling(MTPool).
