CAJ | 학술논문

最小二乘支持向量机是水果品质可见近红外光谱检测中新近发展起来的建模方法。为此，详细介绍了最小二乘支持向量机的工作原理以及基于 Matlab 环境的 LS-SVM 工具箱的使用；在此基础上，运用主成分分析和LS-SVM 法建立了基于可见近红外光谱的河套蜜瓜糖度和硬度检测模型，并分析了样品糖度、硬度的真实值和预测值的相关性。预测结果表明：糖度和硬度预测值与真实值决定系数 R2分别为0．865和0．852。 LS-SVM法建模速度快、准确率高、易于实现，在可见近红外光谱数据分析中有很大的实用价值。
최소이승지지향량궤시수과품질가견근홍외광보검측중신근발전기래적건모방법。위차，상세개소료최소이승지지향량궤적공작원리이급기우 Matlab 배경적 LS-SVM 공구상적사용；재차기출상，운용주성분분석화LS-SVM 법건립료기우가견근홍외광보적하투밀과당도화경도검측모형，병분석료양품당도、경도적진실치화예측치적상관성。예측결과표명：당도화경도예측치여진실치결정계수 R2분별위0．865화0．852。 LS-SVM법건모속도쾌、준학솔고、역우실현，재가견근홍외광보수거분석중유흔대적실용개치。
Least square support vector machine ( LS-SVM) is the newly developed modeling method of fruit quality de-tection in visible near infrared spectroscopy ( VIS-NIR ) .It was introduced thoroughly the working principle of and the application of LS-SVM toolbox based on Matlab environment in the paper .On this basis , principal component analysis and LS-SVM were used for establishing detection models of sugar content and firmness of Hetao muskmelon based on vis -ible near infrared spectroscopy .And the correlation between actual values and predicted values of sugar content and firm-ness of samples was analyzed .The predicted results showed:the determination coefficients ( R2 ) of the predicted and ac-tual values of sugar content and firmness were 0 .865 and 0 .852 .LS-SVM has rapid speed of modeling , high accuracy characteristics , and it is easy to achieve .It had great practical value in the data analysis of VIS-NIR spectroscopy .