It is important and challenging to select the variable for the spectral information automatically and establish a sparse linear model between the spectrum and the sample content under the circumstance that the near-infrared spectral information is much larger than the sample size. In this paper, Elastic Net was used for the measurement of o-cresol in the polyphenylene ether by utilizing the near infrared spectroscopy and a quantitative calibration model between near infrared spectroscopy and o-cresol content was established. Then, the model prediction effect is compared with the Lasso method. In the case where the number of variables is much larger than that of the samples. Although Lasso method can achieve variable selection, the prediction accuracy of the model is affected due to excessive compression to variable coefficients. Elastic Net avoids excessive censorship by increasing L2 penalty, which can improve model prediction accuracy. In order to verifymodel performance indicators ofElastic Net method, we use the complex correlation coefficient R2 and the adjusted complex correlation coefficient R2a to evaluate the interpretability of the model, meanwhile, the prediction accuracy of the model is evaluated by using the mean relative prediction error MRPE and the prediction correlation coefficient Rp. Lasso method to establish the model performance indicators are: R2=0.94, R2a=0.93, MRPE=4.51％, Rp=0.96; Elastic Net method performance indicators are: R2=0.97, R2a=1, MRPE=3.25％, Rp=0.98. From the result we could draw the conclusion that Elastic Net’s model is better than Lasso method. A sparse linear model with higher interpretability and high prediction accuracy can be obtained by the Elastic Net regression.