中文摘要：

Elastic net是对最小二乘方法的一种改进,在最小二乘法的基础上增加了L1和L2惩罚,具有变量选择和模型可提高预测精度的良好性质。此研究以89个小麦样品为实验材料,通过Elastic net方法优选光谱主成分,建立近红外光谱与小麦中蛋白质含量之间的定量分析模型,考证了Elastic net优选主成分建立定量分析模型的可行性。实验中将89个小麦样品随机分成两组,60个样品做建模集,其余29个做预测集。60个样品所建模型预测29个样品的蛋白质含量,预测值和化学测量值间的相关系数（r）为0.9849,平均相对误差为2.48%。为进一步考察该方法建模的可行性和稳定性,对89个样品分别进行5次随机划分,60个样品做为建模集,29个样品做为预测集,5次建模所选光谱的主成分基本一致;同时与PCR和PLS方法作对比,结果显示5次所建模型的预测效果明显好于PCR,且与PLS方法相近。鉴于Elastic net具有变量选择的功能,且所建模型具有较好的预测效果,表明该方法是一种可行的建立化学计量学定量分析模型的方法。

英文摘要：

Elastic net is an improvement of the least-squares method by introducing in L1and L2 penalties,and it has the advantages of the variable selection.The quantitative analysis model build by Elastic net can improve the prediction accuracy.Using 89 wheat samples as the experiment material,the spectrum principal components of the samples were selected by Elastic net.The analysis model was established for the near-infrared spectrum and the wheat＇s protein content,and the feasibility of using Elastic net to establish the quantitative analysis model was confirmed.In experiment,the 89 wheat samples were randomly divided into two groups,with 60 samples being the model set and 29 samples being the prediction set.The 60 samples were used to build analysis model to predict the protein contents of the 29 samples,and correlation coefficient（R）of the predicted value and chemistry observed value was 0.984 9,with the mean relative error being 2.48%.To further investigate the feasibility and stability of the model,the 89 samples were randomly selected five times,with 60 samples to be model set and 29 samples to be prediction set.The five groups of principal components which were selected by Elastic net for building model were basically consistent,and compared with the PCR and PLS method,the model prediction accuracies were all better than PCR and similar with PLS.In view of the fact that Elastic net can realize the variable selection and the model has good prediction,it was shown that Elastic net is suitable method for building chemometrics quantitative analysis model.