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In the analysis of blood component content, spectral method has been widely concerned for its rapid and non-invasive advantages, but there are many interference factors in the process of data collection. This will cause a large deviation in the spectral data of some samples. If it is directly used in modeling analysis, it will introduce bias to model establishment and result in large deviation in prediction. Therefore, it is necessary to screen the samples before establishing the model, in this paper, a method of sample screening using sigma criterion is proposed for noninvasive blood component analysis, The dynamic spectrum (DS) value was extracted by single edge method which is the mean value of the effective slope of the rising edge of the photoplethysmographic (PPG) signal was taken as the DS value. The DS value fluctuates in a certain range due to the change of blood component concentration. The samples beyond the range of 1 standard deviation were excluded and screened. Partial least squares (PLS) linear regression models were established for fasting blood glucose, white blood cell and albumin in all samples before and after screening, and the whole sample was modeled and analyzed. Then the sample set was divided into training set and test set according to the ratio of 10:1, and the three blood components were predicted and analyzed respectively. The experimental results show that the modeling test of three components, respectively, after screening sample of the precision of the sample model, as well as training set and testing set of classified sample sets predict performance after got greatly ascend, improves the correlation coefficient, reduce the root mean square error of the result of the experiment fully demonstrated the effectiveness of the method of sample selection.
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