Study on the application of supervised principal component regression procedure to near-infrared spectroscopy quantitative analysis

The present paper introduces the principle of a new modeling method, called supervised principal component regression, with which the model of the near-infrared (NIR) spectroscopy quantitative analysis was established. Usually, there are many difficulties such as collinearity when establishing the quantitative analysis model for the high dimension of the spectral data. Using this new method, firstly according to some criterion, the wavelength information is selected in order to reduce the dimension of spectral data. Then the selected lower dimensional spectral data set is used to establish the principal component regression model. Taking sixty-six wheat samples as experiment materials, forty samples were chosen randomly to establish the predicting model, while the remaining twenty-sixth wheat samples were viewed as prediction set. In this example, 4 wavelengths, 4632, 4636, 5994 and 5997 cm-1, were selected at first according to the coefficients between the response variable and each spectral data. Then two principal components of the spectral data at those four wavelengths were extracted to establish the principal component regression model. The model was used to the prediction set. The coefficient was 0.991 and the average relative error was 1.5% between the model predication results and Kjeldahl's value for the protein content. It is very important to select the most significant part of wavelength information from a large number of spectral data, not only because this procedure can alleviate the influence of collinearity in modeling, but also because it can be used to guide the design of special NIR analysis instrument for analyzing specific component in some samples.