A new method for abnormal spectrum detection based on the mixed model of samples

A new method for abnormal spectrum detection based on the mixed model of samples is proposed. The method can detect abnormal spectra on the condition that the content values are unknown. The method consists of four steps. Firstly, mixed vector of the prediction sample is calculated according to the mixed model of samples. Secondly, estimated spectrum of the prediction sample is calculated according to the mixed ratio and the spectrum of calibration samples. Thirdly, the difference between the estimated spectrum and the measuring spectrum is calculated. Lastly F-statistical test is carried out to detect the abnormal spectrum according to the variance. The method is compared with the MMS and PLS algorithms. In the experiment, it is assumed that the contents of the prediction samples are unknown for the new method. For MMS and PLS, the contents of the prediction samples are known, and when the prediction error is bigger than three times the root mean square error of prediction (RMSEP), the spectrum is identified as abnormal spectrum. Results from calculations show that the new method has better detection performances for abnormal spectrum caused by measurement background changes, instrumental noise increase, and the condition of detection samples containing non-calibration content than MMS and PLS algorithms. The new method provides a new approach to detect the spectrometer performance including the background changes and noise increase in advance.