Publications by authors named "Jia-Wen Wei"
Article Synopsis
- Researchers developed a machine learning-assisted fluorescence sensing method for quickly detecting two pesticides, thiabendazole (TBZ) and fuberidazole (FBZ), in red wine.
- The approach involves collecting fluorescence spectra data and using machine learning to create a prediction model for rapid analysis, achieving high recovery rates for both pesticides without complex sample pretreatment.
- This study highlights the potential of combining machine learning with fluorescence techniques to tackle challenges in analyzing complex mixtures in food safety monitoring.
Early and sensitive detection of δ-aminolevulinic acid (δ-ALA) and porphobilinogen (PBG) is the cornerstone of diagnosis and effective treatment for acute porphyria. However, at present, the quantifying strategies demand multiple solvent extraction steps or chromatographic approaches to separate δ-ALA and PBG prior to quantification. These methods are both time-consuming and laborious.
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