CAJ | 학술논문

建立金银花醇沉过程中稳健的近红外光谱( Near infrared spectroscopy,NIR)定量模型,为金银花醇沉过程的快速评价提供方法。研究基于金银花醇沉过程绿原酸的 NIR 数据,通过建立 Bagging 偏最小二乘(Bagging-PLS)模型、Boosting偏最小二乘(Boosting-PLS)模型与偏最小二乘(Partial Least Squares,PLS)模型,实现对模型性能比较；在此基础上,采用组合间隔偏最小二乘法( Synergy interval partial least squares,siPLS)和竞争自适应抽样( Competitive adaptive reweighted sampling,CARS )法分别对光谱进行变量筛选,建立模型,实现了对模型预测性能的考察。实验结果表明, Bagging-PLS和Boosting-PLS(潜变量因子数设为10)的预测性能均优于 PLS 模型。在此基础上,两批样品采用 siPLS 筛选变量,第一个批次金银花筛选波段820~1029.5 nm和1030~1239.5 nm,第二个批次金银花醇沉筛选波段为820~959.5 nm和960~1099.5 nm；采用CARS方法变量筛选,两批样品分别选择5折交叉验证和10折交叉验证,取交叉验证均方根误差( RMSECV)值最小的子集作为最终变量筛选的结果。经过变量筛选的两批金银花醇沉过程中的绿原酸含量Bagging-PLS和Boosting-PLS模型的预测均方根误差(RMSEP)值降低了0.02~0.04 g/L,预测相关系数提高了4%~5%。综上,Baggning-PLS和Boosting-PLS算法可作为金银花醇沉过程NIR定量模型的快速预测方法。
건립금은화순침과정중은건적근홍외광보( Near infrared spectroscopy,NIR)정량모형,위금은화순침과정적쾌속평개제공방법。연구기우금은화순침과정록원산적 NIR 수거,통과건립 Bagging 편최소이승(Bagging-PLS)모형、Boosting편최소이승(Boosting-PLS)모형여편최소이승(Partial Least Squares,PLS)모형,실현대모형성능비교；재차기출상,채용조합간격편최소이승법( Synergy interval partial least squares,siPLS)화경쟁자괄응추양( Competitive adaptive reweighted sampling,CARS )법분별대광보진행변량사선,건립모형,실현료대모형예측성능적고찰。실험결과표명, Bagging-PLS화Boosting-PLS(잠변량인자수설위10)적예측성능균우우 PLS 모형。재차기출상,량비양품채용 siPLS 사선변량,제일개비차금은화사선파단820~1029.5 nm화1030~1239.5 nm,제이개비차금은화순침사선파단위820~959.5 nm화960~1099.5 nm；채용CARS방법변량사선,량비양품분별선택5절교차험증화10절교차험증,취교차험증균방근오차( RMSECV)치최소적자집작위최종변량사선적결과。경과변량사선적량비금은화순침과정중적록원산함량Bagging-PLS화Boosting-PLS모형적예측균방근오차(RMSEP)치강저료0.02~0.04 g/L,예측상관계수제고료4%~5%。종상,Baggning-PLS화Boosting-PLS산법가작위금은화순침과정NIR정량모형적쾌속예측방법。
ToprovidethemethodologyforrapidqualityevaluationofLonicerajaponica,wehaveestablished the stable quantitative model of near infrared spectroscopy ( NIR) . The performance of Bagging partial least squares (Bagging-PLS) model and Boosting partial least squares (Boosting-PLS) model was compared with that partial least squares ( PLS ) model based on the NIR data of ethanol precipitation process of Lonicera japonica. On this basis, the performance of these two models after variables selection was also studied by the methods of siPLS ( synergy interval partial least squares ) and CARS ( competitive adaptive reweighted sampling) . The experimental results showed that the prediction performance of Bagging-PLS and Boosting-PLS models was superior to PLS model with the latent factor of 10 . The band of 820-1029 . 5 nm and 1030-1239. 5 nm for the first batch was selected by the method of siPLS. In addition, the band of 820-1029. 5 nm and 1030-1239. 5 nm was selected for the second batch sample in the same method. Furthermore, the method of CARS was taken to select variables for the two batches samples with 5-fold cross-validation and 10-fold cross-validation. And the lowest RMSECV( root mean square error of cross-validation) values were used to take subset. Compared to the model performance without the method of CARS, the RMSEP value of the Bagging-PLS model and Boosting-PLS model for the concentration of chlorogenic acid reduced by 0 . 02-0 . 04 g/L and rp(correlation coefficient of prediction)value increased by 4%-5%. Generally, Bagging-PLS and Boosting-PLS could be regarded as rapid prediction methodsfor NIR quantitative models of ethanol precipitation process of Lonicera japonica.