近红外(NIR)定量分析通常涉及多个组分, 采用遗传算法和自适应建模策略, 建立了能够对多组分同时定量的多目标最小二乘支持向量机(LS-SVM), 并将其应用于玉米中四个组分和连翘中两个活性成分的NIR分析。 结果表明多目标遗传算法配合自适应建模策略可保证优化收敛于全局最优解。 所建玉米多目标LS-SVM模型明显优于PLS1和PLS2模型; 连翘多目标LS-SVM模型与PLS模型均可取得较好的校正和预测效果。 两组数据中, 径向基神经网络(RBFNN)模型均出现过拟合现象。 多目标LS-SVM和单目标LS-SVM性能相近, 但多目标LS-SVM建模运行一次即可得到结果, 在NIR多组分定量分析中具有潜在应用优势。

The near infrared (NIR) spectrum contains a global signature of composition, and enables to predict different properties of the material. In the present paper, a genetic algorithm and an adaptive modeling technique were applied to build a multi-objective least square support vector machine (MLS-SVM), which was intended to simultaneously determine the concentrations of multiple components by NIR spectroscopy. Both the benchmark corn dataset and self-made Forsythia suspense dataset were used to test the proposed approach. Results show that a genetic algorithm combined with adaptive modeling allows to efficiently search the LS-SVM hyperparameter space. For the corn data, the performance of multi-objective LS-SVM was significantly better than models built with PLS1 and PLS2 algorithms. As for the Forsythia suspense data, the performance of multi-objective LS-SVM was equivalent to PLS1 and PLS2 models. In both datasets, the over-fitting phenomena were observed on RBFNN models. The single objective LS-SVM and MLS-SVM didn’t show much difference, but the one-time modeling convenience allows the potential application of MLS-SVM to multicomponent NIR analysis.