[Study on a method of selecting calibration samples in NIR spectral analysis].

In the present paper, a simple but novel method based on maximum linearly independent group was introduced into near-infrared (NIR) spectral analysis for selecting representative calibration samples. The experiment materials contained 2,652 tobacco powder samples, with 1,001 samples randomly selected as prediction set, and the others as representative sample candidate set from which calibration sample set was selected. The method of locating maximum linearly independent vectors was used to select representative samples from the spectral vectors of representative samples candidate set. The arithmetic was accomplished by function rref(X,q) in Matlab. The maximum linearly independent spectral vectors were treated as calibration samples set. When different calculating precision q was given, different amount of representative samples were acquired. The selected calibration sample set was used to build regression model to predict the total sugar of tobacco powder samples by PLS. The model was used to analyze 1001 samples in the prediction set. When selecting 32 representative samples, the model presented a good predictive veracity, whose predictive mean relative error was 3.6210%, and correlation coefficient was 0.9643. By paired-samples t-test, we found that the difference between the predicting result of model obtained by 32 samples and that obtained by 146 samples was not significant (alpha=0.05). Also, we compared the methods of randomly selecting calibration samples and maximum linearly independent selection by their predicting effects of models. In the experiment, correspondingly, six calibration sample sets were selected, one of which included 28 samples, while the others included 32, 41, 76, 146 and 163 samples respectively. The method of maximum linearly independent selecting samples turned out to be obviously better than that of randomly selecting. The result indicated that the proposed method can not only effectively enhance the cost-effectiveness of NIR spectral analysis by reducing the number of samples required for cockamamie and expensive chemical measurement, but also improve the analysis accuracy. In conclusion, this method can be applied to select representative samples in near-infrared spectral analysis.