RESUMO
Near infrared model established under a certain condition can be applied to the new samples status, environmental conditions or instrument status through the model transfer. Spectral background correction and model update are two types of data process methods of NIR quantitative model transfer, and orthogonal signal regression (OSR) is a method based on spectra background correction, in which virtual standard spectra is used to fit a linear relation between master batches spectra and slave batches spectra, and map the slave batches spectra to the master batch spectra to realize the transfer of near infrared quantitative model. However, the above data processing method requires the represent activeness of the virtual standard spectra, otherwise the big error will occur in the process of regression. Therefore, direct orthogonal signal correction-slope and bias correction (DOSC-SBC) method was proposed in this paper to solve the problem of PLS model's failure to predict accurately the content of target components in the formula of different batches, analyze the difference between the spectra background of the samples from different sources and the prediction error of PLS models. DOSC method was used to eliminate the difference of spectral background unrelated to target value, and after being combined with SBC method, the system errors between the different batches of samples were corrected to make the NIR quantitative model transferred between different batches. After DOSC-SBC method was used in the preparation process of water extraction and ethanol precipitation of Lonicerae Japonicae Flos in this paper, the prediction error of new batches of samples was decreased to 7.30% from 32.3% and to 4.34% from 237%, with significantly improved prediction accuracy, so that the target component in the new batch samples can be quickly quantified. DOSC-SBC model transfer method has realized the transfer of NIR quantitative model between different batches, and this method does not need the standard samples. It is helpful to promote the application of NIR technology in the preparation process of Chinese medicines, and provides references for real-time monitoring of effective components in the preparation process of Chinese medicines.
RESUMO
OBJECTIVE: Many studies have reported pre-processing effects for brain volumetry; however, no study has investigated whether non-parametric non-uniform intensity normalization (N3) correction processing results in reduced system dependency when using an atlas-based method. To address this shortcoming, the present study assessed whether N3 correction processing provides reduced system dependency in atlas-based volumetry. MATERIALS AND METHODS: Contiguous sagittal T1-weighted images of the brain were obtained from 21 healthy participants, by using five magnetic resonance protocols. After image preprocessing using the Statistical Parametric Mapping 5 software, we measured the structural volume of the segmented images with the WFU-PickAtlas software. We applied six different bias-correction levels (Regularization 10, Regularization 0.0001, Regularization 0, Regularization 10 with N3, Regularization 0.0001 with N3, and Regularization 0 with N3) to each set of images. The structural volume change ratio (%) was defined as the change ratio (%) = (100 x [measured volume - mean volume of five magnetic resonance protocols] / mean volume of five magnetic resonance protocols) for each bias-correction level. RESULTS: A low change ratio was synonymous with lower system dependency. The results showed that the images with the N3 correction had a lower change ratio compared with those without the N3 correction. CONCLUSION: The present study is the first atlas-based volumetry study to show that the precision of atlas-based volumetry improves when using N3-corrected images. Therefore, correction for signal intensity non-uniformity is strongly advised for multi-scanner or multi-site imaging trials.