CAJ | 학술논문

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。
근홍외（NIR）정량분석통상섭급다개조분，채용유전산법화자괄응건모책략，건립료능구대다조분동시정량적다목표최소이승지지향량궤（LS-SVM ），병장기응용우옥미중사개조분화련교중량개활성성분적NIR분석。결과표명다목표유전산법배합자괄응건모책략가보증우화수렴우전국최우해。소건옥미다목표LS-SVM모형명현우우PLS1화PLS2모형；련교다목표LS-SVM 모형여PLS모형균가취득교호적교정화예측효과。량조수거중，경향기신경망락（RBFNN ）모형균출현과의합현상。다목표 LS-SVM 화단목표LS-SVM성능상근，단다목표LS-SVM건모운행일차즉가득도결과，재NIR다조분정량분석중구유잠재응용우세。
The near infrared (NIR) spectrum contains a global signature of composition ,and enables to predict different proper-ties 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 bet-ter 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 al-lows the potential application of MLS-SVM to multicomponent NIR analysis .