首页|Reports from Xiamen University Advance Knowledge in Machine Learning (Synchronou s Fluorescence Spectra-based Machine Learning Algorithm With Quick and Easy Acce ssibility for Simultaneous Quantification of Polycyclic Aromatic Hydrocarbons In ...)
Reports from Xiamen University Advance Knowledge in Machine Learning (Synchronou s Fluorescence Spectra-based Machine Learning Algorithm With Quick and Easy Acce ssibility for Simultaneous Quantification of Polycyclic Aromatic Hydrocarbons In ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Xiamen, People's Repub lic of China, by NewsRx correspondents, research stated, "Polycyclic aromatic hy drocarbons (PAHs) are one of the leading causes of human cancer. Four typical PA Hs (PAH4) including benzo(a)pyrene (BaP), benzo(b)fluoranthene (BbF), benzo(a)an thracene (BaA), and chrysene (Chr) have been regarded as reasonable indicators f or the occurrence of PAHs in food." Funders for this research include Science and Technology Program of Fujian Provi nce, National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Xiamen University, "In this study, the constant wavelength synchronous fluorescence (CWSF) spectra of P AH4 mixtures were used as the data sets without preprocessing and directly combi ned with the back propagation neural network (BPNN) algorithm to establish a qua ntitative analysis method of PAH4. This method is capable of predicting the conc entrations of PAH4 in edible oil samples without pre-separation. The detection l imits for BaP, BbF, BaA, and Chr were 0.014, 0.068, 0.026, and 0.013 mu g/kg, re spectively. The recoveries in various oil samples for BaP, BbF, BaA, and Chr wer e 99.5 +/- 2.1, 101.0 +/- 4.6, 98.6 +/- 3.2, and 98.5 +/- 4.9 %, re spectively."
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