Classification Of Chemicals Present In Essential Oils Using Deep Learning Algorithm

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Bindu Krishnan


Classification of chemical compounds present in the essential oils is considered vital to check the genuineness of the
chemical oils, its smell and odour. Hence, it is essential in utilising gas chromatography to find the retention indices using
the structure of the chemical compounds present in the chemical oil in gaseous state. The utilisation of deep learning
model cloud help in determining the retention index of the gases compounds to test the genuineness. In this paper, a
model is developed using convolutional neural network (CNN) to detect the presence of molecules while processing the
essential oils. In Gas chromatography, the CNN involves in predicting the retention index of the GC on polar and mid-polar
phases. The mean square error is measured over the stationary phases on the test datasets to validate the prediction
accuracy of the model. The comparison with experimental observation shows that the proposed state-of-art model
achieves near optimal training and testing accuracy than other methods.

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