QPLS

AcronymDefinition
QPLSQuantitative Perfusion Lung Scintigraphy
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References in periodicals archive ?
Determination models of the relative content of Pst DNA in wheat leaves in the incubation period were also built using individual methods including QPLS and SVR based on the same training set and testing set as used for building the optimal kQPLS-SVR model.
The results demonstrated that the best effects were achieved for the kQPLS-SVR model built when the number of the spectral attributes of the randomly selected wavelength points was 700, the number of principal components was 8, and the number of the built QPLS models was 5.
The model built when the number of the spectral attributes randomly selected was 700, the number of principal components was 8, and the number of the built QPLS models was 15 was better than others among the kQPLS-SVR models as shown in Table 3.
Among the kQPLS-SVR models as shown in Table 4, the better effects were obtained for the model built when the number of the spectral attributes randomly selected was 1400, the number of principal components was 4, and the number of the built QPLS models was 15.
As demonstrated in Table 5, the effects of the model built when the number of the spectral attributes randomly selected was 700, the number of principal components was 8, and the number of the built QPLS models was 15 was better than others.
Therefore, the optimal kQPLS-SVR model built based on the original near-infrared spectra (the number of the spectral attributes of the randomly selected wavelength points was 700, the number of principal components was 8, and the number of the built QPLS models was 5) was regarded as the optimal kQPLS-SVR model to quantitatively determine the relative content of Pst DNA in wheat leaves in the incubation period.
Results of the QPLS Models and the SVR Model for Quantification of the Relative Content of Pst DNA in Wheat Leaves in the Incubation Period.
A comparison of the effects of the three models, including the optimal kQPLS-SVR model, the optimal QPLS model, and the optimal SVR model, was conducted according to [R.sup.2], SEC, AARD, and RPD of the training set and [R.sup.2], SEP, AARD, and RPD of the testing set.
For the kQPLS-SVR models, the optimal QPLS model, and the optimal SVR model as shown in Tables 2-6, all the values of [R.sup.2] of the training set and the testing set were more than 0.5.
A modeling method integrated with QPLS and SVR was used to establish the dynamic quantitative detection model of wheat stripe rust pathogen during the incubation period.