English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 priori models 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 priori model were characterized. Increasing the proportion of priori model appropriately was beneficial to the performance of models and 16% was shown to be the best incorporated proportion.

analytical letters

A Priori Knowledge-Incorporating Method for the Determination of Polycyclic Aromatic Hydrocarbons (PAHs) in Edible Vegetable Oils by Time Resolved Fluorescence