CAJ | 학술논문

为增强模型的适应性，选取了3个不同成熟期（绿熟、半红熟和红熟）的李果实样品建立坚实度指标的近红外检测模型，建模所使用的光谱范围为4000～12492 cm-1。为改善模型性能，比较了最小二乘支持向量机和偏最小二乘法两种建模算法对李果实坚实度指标的建模结果。研究结果表明，所建立的最小二乘-支持向量机模型的预测性能和稳定性均好于偏最小二乘模型，并以前10个潜在变量得分作为输入变量的最小二乘-支持向量机模型为最佳模型，其校正相关系数、校正和预测均方根误差分别为0．989及1．31、1．84kg/cm2，剩余预测偏差为4．79。与以往研究文献相比，获得了较为理想的预测精度和稳定性能。研究结果表明，最小二乘支持向量机算法结合偏最小二乘法提取的潜在变量作为输入变量，可以使李果实坚实度近红外定量模型有较大程度的改善。
위증강모형적괄응성，선취료3개불동성숙기（록숙、반홍숙화홍숙）적리과실양품건립견실도지표적근홍외검측모형，건모소사용적광보범위위4000～12492 cm-1。위개선모형성능，비교료최소이승지지향량궤화편최소이승법량충건모산법대리과실견실도지표적건모결과。연구결과표명，소건립적최소이승-지지향량궤모형적예측성능화은정성균호우편최소이승모형，병이전10개잠재변량득분작위수입변량적최소이승-지지향량궤모형위최가모형，기교정상관계수、교정화예측균방근오차분별위0．989급1．31、1．84kg/cm2，잉여예측편차위4．79。여이왕연구문헌상비，획득료교위이상적예측정도화은정성능。연구결과표명，최소이승지지향량궤산법결합편최소이승법제취적잠재변량작위수입변량，가이사리과실견실도근홍외정량모형유교대정도적개선。
Plum samples of three different maturity stages ( green-maturity , prered-maturity and red-maturity stages ) were chosen to establish near infrared spectroscopy ( NIR ) models to quantify firmness of plums for a wider range of models application .The spectral region used was 4000-12492 cm-1.In order to improve models performance , Least squares-support vector machine (LS-SVM) with latent variables (LVs), extracted by partial least squares (PLS), as input were used to establish calibration models .And the performance were compared with PLS models .LS-SVM models were superior to PLS model in calibration , prediction and robustness .Optimal models were obtained by LS-SVM with the first 10 LVs as input .The correlation coefficients of calibration and root mean square error of calibration and prediction were 0.989, 1.31 kg/cm2 and 1.84 kg/cm2 .The residual predictive deviation was 4.79, which were more satisfied in prediction accuracy and robustness than results reported by previous works .The results indicate that with LVs as input LS-SVM offers more effective quantitative capability for firmness of plum .