在近红外光谱定量分析中, 许多模型分开考虑各种样品成分含量, 失去了样品成分间潜在的联系。 针对该问题, 文章将建模分析每种样品成分含量的问题看作一个任务, 将同时建模分析所有样品成分含量的问题转换为多任务学习问题。 在LS-SVR的基础上, 提出了多任务LS-SVR(MTLS-SVR), 并给出一种有效的大规模问题求解算法。 最后, 以高粱样品数据集为实验材料, 建立了三种样品成分(蛋白质, 赖氨酸及淀粉)的同时定量分析模型。 三种样品成分的预测值与实际值的平均相对误差分别为1.52%, 3.04%和1.01%, 相关系数分别为0.993 1, 0.894 0和0.940 6, 经分析比较, 发现MTLS-SVR模型优于PLS, LS-SVR以及多因变量LS-SVR(MLS-SVR), 从而验证了MTLS-SVR模型的可行性和有效性。

In near infrared spectral quantitative analysis, many models consider separately each component when modeling sample composition content, disregarding the underlying relatedness among sample compositions. To address this problem, the present paper views modeling each sample composition content as a task, thus one can transform the problem that models simultaneously analyze all sample compositions’ contents to a multi-task learning problem. On the basis of the LS-SVR, a multi-task LS-SVR (MTLS-SVR) model is proposed. Furthermore, an efficient large-scale algorithm is given. The broomcorn samples are taken as experimental material, and corresponding quantitative analysis models are constructed for three sample composition contents (protein, lysine and starch) with LS-SVR, PLS, multiple dependent variables LS-SVR (MLS-SVR) and MTLS-SVR. For the MTLS-SVR model, the average relative errors between actual values and predicted ones for the three sample compositions contents are 1.52%, 3.04% and 1.01%, respectively, and the correlation coefficients are 0.993 1, 0.894 0 and 0.940 6, respectively. Experimental results show MTLS-SVR model outperforms significantly the three others, which verifies the feasibility and efficiency of the MTLS-SVR model.