A priori knowledge-incorporating method based on time resolved fluorescence was successfully developed for the determination of polycyclic aromatic hydrocarbons in edible vegetable oils. Specifically, fluorescence dec...
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A priori knowledge-incorporating method based on time resolved fluorescence was successfully developed for the determination of polycyclic aromatic hydrocarbons in edible vegetable oils. Specifically, fluorescence decay functions of polycyclic aromatic hydrocarbons at characteristic emission wavelengths were used as the priorimodels and incorporated into the deep-autoencoder. The priori model-incorporating deep-autoencoder models were shown to be effective for the determination of polycyclic aromatic hydrocarbons in edible vegetable oils and root-mean-square errors of prediction lower than 2% were achieved. The influence of analyte, matrix and proportion of priorimodel were characterized. Increasing the proportion of priorimodel appropriately was beneficial to the performance of models and 16% was shown to be the best incorporated proportion.
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