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[Study on spectral calibration of discrimination of corn variety using near-infrared spectra based on DS algorithm].

From the perspective of calibration, the present paper studies the model stability problem in qualitative analysis of NIR. Aiming at the issue of model failure caused by different data acquisition time, 13 varieties of corn were used as experimental material, and learning from the idea of model calibration transfer between the two instruments in quantitative analysis of NIR, the DS (direct standardization) algorithm was used to calibrate the spectra acquired at different times with the same instrument, that made the varieties identification model established one time able to be applied to identify the test data at different acquisition time. First, transfer set was selected from the master spectrum set by Kennard/Stone algorithm, the corresponding number spectrums in slave spectrum set were selected, and then DS algorithm was applied to transfer set to calculate the transformation function between the two sets of data. Finally, the remaining slave spectrums were transformed so that they could apply to the model. This study does some experiment to discuss the impact of the number of transfer set and the location of calibration on the calibration results. Respectively, the experiment results were analyzed from two aspects, one is the correct discrimination rate in qualitative analysis, and the other is the distribution distance between master spectrums and slave spectrums before and after calibration. The experiment results indicate that this approach is effective to solve the spectra drift produced by sampling over time, can bring higher recognition rate on different sampling time test sets, also improves the robustness and application scope of the identification model, and the experiment results also indicate that the best result can be obtained with calibration locating after feature extraction.