[Effects of Different Pretreatment Methods on the Phenylketonuria Screening Model by FTIR/ATR Spectroscopy]

To establish a phenylketonuria screening model by FTIR/ATR spectroscopy, and to compare the effects of different pretreatment methods, such as baseline correction, smoothing, derivation, Fourier deconvolution, on the model quality. A consensus partial least squares regression method (cPLS) was used to build the quantitative model of phenylalanine in dried blood spots. The effects of different pretreatment methods on the model performance were investigated, using the correlation coefficient (r), root mean square error of prediction (RMSEP), mean relative error (MRE) and predictive accuracy (Acc). The nine-point smoothing coupled with the first differential was found to perform the best. Compared with the model by the original spectra, its r, RMSEP, MRE and Acc were improved from 0. 822 7, 115. 8, 0. 395 and 94. 6 to 0. 889 9, 102. 2, 0. 286 and 100, respectively. With the advantages of fast speed, easy process, no reagents consumption and environmental protection, the present method is expected to become a simple and green technology for rapidly screening the neonatal phenylketonuria in a large population.