CAJ | 학술논문

研究利用近红外光谱分析技术定量测定茶叶中咖啡碱的含量,目的是通过变量筛选简化模型并提高预测精度.试验中以135个来自大闽食品公司的茶叶作为研究对象,利用基于小波系数蒙特卡罗无信息变量消除法(WT-MC-UVE)进行变量筛选并结合偏最小二乘法(PLS)建立咖啡碱定量分析模型,选择交互验证均方根误差(RMSECV)和预测集均方根误差(RMSEP)以及预测相关系数(Rp)作为模型的评价指标.应用 WT-MC-UVE筛选的90个变量所建立的模型,交互验证均方根误差,预测卷均方根误差,预测相关系数分别为0.1248、0.1611和0.9574.结果表明,该方法有效可行.
연구이용근홍외광보분석기술정량측정다협중가배감적함량,목적시통과변량사선간화모형병제고예측정도.시험중이135개래자대민식품공사적다협작위연구대상,이용기우소파계수몽특잡라무신식변량소제법(WT-MC-UVE)진행변량사선병결합편최소이승법(PLS)건립가배감정량분석모형,선택교호험증균방근오차(RMSECV)화예측집균방근오차(RMSEP)이급예측상관계수(Rp)작위모형적평개지표.응용 WT-MC-UVE사선적90개변량소건립적모형,교호험증균방근오차,예측권균방근오차,예측상관계수분별위0.1248、0.1611화0.9574.결과표명,해방법유효가행.
In this research, we tested the content of tea caffeine by near-infrared (NIR) spectroscopy to sim-plify the model and increase the prediction accuracy by a method of variable selection. One hundred and thir-ty-five tea samples from Damin Food Company were tested. Monte Carlo uninformative variables elimination based on wavelet coefficient (WT-MC-UVE) method was used for variable selection, and the model of quantita-tive analysis for tea caffeine was established by partial least squares (PLS) . The root mean square error of cross validation(RMSECV),the root mean square error of prediction set(RMSEP)and correlation coefficients (Rp) were chosen for the appraisal criterion of the model. Ninety variables were optimized for modeling, and the model’s RMSEC, RMSEP and Rp were 0.1248, 0.1611 and 0.9564, respectively. The results show that this method is valid and feasible.