CAJ | 학술논문

采用KBr压片法对杉木/聚丙烯( PP)复合材料样品进行了红外光谱分析,确定杉木特征吸收谱带为1740~1730、1610~1590、1270~1260、1060~1050以及1040~1030 cm-1,以 PP 在1377 cm-1处吸收强度( I)为内标,对木塑复合材料( WPC)中木粉含量和杉木特征峰相对吸收强度进行相关性分析,并采用逐步多元线性回归法建立木粉含量与相对峰强间的多元线性回归方程。结果表明,选取I(1060-1050)/I1377、I(1270-1260)/I1377为回归变量建立的二元线性回归方程和以I(1060-1050)/I1377、I(1040-1030)/I1377及I(1270-1260)/I1377为回归变量建立的三元线性回归方程,具有较高的预测精度。木粉含量的预测值和参照值之间具有强烈的相关性,校正决定系数(R2c)超过0.98,验证决定系数(R2p)超过0.96。外部验证结果表明,线性回归方程预测准确性较高,预测相对偏差范围为0.9%至7.4%,其中三元线性回归方程预测准确性稍好于二元线性回归方程。
채용KBr압편법대삼목/취병희( PP)복합재료양품진행료홍외광보분석,학정삼목특정흡수보대위1740~1730、1610~1590、1270~1260、1060~1050이급1040~1030 cm-1,이 PP 재1377 cm-1처흡수강도( I)위내표,대목소복합재료( WPC)중목분함량화삼목특정봉상대흡수강도진행상관성분석,병채용축보다원선성회귀법건립목분함량여상대봉강간적다원선성회귀방정。결과표명,선취I(1060-1050)/I1377、I(1270-1260)/I1377위회귀변량건립적이원선성회귀방정화이I(1060-1050)/I1377、I(1040-1030)/I1377급I(1270-1260)/I1377위회귀변량건립적삼원선성회귀방정,구유교고적예측정도。목분함량적예측치화삼조치지간구유강렬적상관성,교정결정계수(R2c)초과0.98,험증결정계수(R2p)초과0.96。외부험증결과표명,선성회귀방정예측준학성교고,예측상대편차범위위0.9%지7.4%,기중삼원선성회귀방정예측준학성초호우이원선성회귀방정。
The Chinese fir/polypropylene ( PP ) composites samples were analyzed by FT-IR with KBr pellets method. The characteristic adsorption peaks of Chinese fir were assigned to 1740 -1730 cm-1 , 1610 -1590 cm-1 , 1270 -1260 cm-1 , 1060-1050 cm-1 , and 1040 -1030 cm-1 . The peak of PP at 1377 cm-1 was taken as reference. Correlation analysis was performed between wood flour content and the relative intensities of different spectral peaks and the stepwise multiple linear regression was used to establish the equations. The results showed that the binary linear regression equation with I(1060-1050)/I1377 and I(1270-1260)/I1377 as variables and the ternary linear regression equation based on the variables I(1060-1050)/I1377 , I(1040-1030)/I1377 and I(1270-1260)/I1377 possessed better prediction accuracy. There is a strong correlation between FT-IR-predicted wood flour content and referenced wood flour content. The coefficients of determination (R2c) of calibration exceed 0. 98, and the R2p of cross validation were above 0. 96. The results of external validation showed that linear regression equations had good predictabilities, and the relative deviations of prediction ranged from -7. 4% to -0. 9%. The results also showed that the prediction accuracy of the ternary linear regression equation was slightly better than that of the binary linear regression equation.