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The origins of herbal medicines are important for their treatment effect,which could be potentially distinguished by electronic nose system. As the odorfingerprint of herbal medicines from different origins can be tiny, thediscrimination of origins can be much harder than that of different categories.Better feature extraction methods are significant for this task to be moreaccurately done, but there lacks systematic studies on different featureextraction methods. In this study, we classified different origins of threecategories of herbal medicines with different feature extraction methods:manual feature extraction, mathematical transformation, deep learningalgorithms. With 50 repetitive experiments with bootstrapping, we compared theeffectiveness of the extractions with a two-layer neural network w/odimensionality reduction methods (principal component analysis, lineardiscriminant analysis) as the three base classifiers. Compared with theconventional aggregated features, the Fast Fourier Transform method and ournovel approach (longitudinal-information-in-a-line) showed an significantaccuracy improvement(p < 0.05) on all 3 base classifiers and all three herbalmedicine categories. Two of the deep learning algorithm we applied also showedpartially significant improvement: one-dimensional convolution neuralnetwork(1D-CNN) and a novel graph pooling based framework - multivariate timepooling(MTPool).

Classifying herbal medicine origins by temporal and spectral data mining of electronic nose