[Near-infrared spectroscopy quantitative analysis model based on inverse regression]

In the present paper, an inverse regression method is used in near infrared (NIR) spectroscopy analysis to reduce dimension of predictor at first, then estimate linear regression function using the new derived low dimensional data. A real data set of 103 corn samples was used for analysis with this new inverse regression method. Taking 103 corn samples as experiment materials, seventy samples were chosen randomly to establish predicting model, the remaining thirty-three corn samples were viewed as prediction set. The new derived model is used to the prediction set. The coefficient is 0.986 and the average relative error is 2.1% between the model predication results and Kjeldahl's value for the protein content, and the resulis of using partial least square regression are 0.978 and 2.5%, respectively. The results demonstrate that the inverse regression method is feasible and has good property in near-infrared spectroscopy quantitative analysis, and also provides a new idea for chemometrics quantitative analysis.