基于近红外光谱技术的茶粉中关键指标的快速测定

Rapid Determination of Key Indicators of Tea Powder Based on Near Infrared Spectroscopy

基于近红外光谱技术对茶粉中茶多酚和咖啡碱含量进行定量分析,通过PLS建立定量分析模型,同时采用竞争性自适应重加权法(CARS)、遗传偏最小二乘法(GA-PLS)和无信息变量消除法(UVE)对整个谱区进行光谱波段筛选,并利用标准正态变量变换(SNV)、导数及多元散射校正(MSC)等预处理方法进行模型优化。以决定系数(R^2)、校正标准偏差(RMSEC)、预测标准偏差(RMSEP)及主成分数作为模型质量的评价指标。结果表明:特征波段筛选可对模型起到优化效果,提高模型运算速度。其中GA-PLS优化效果最为明显,茶多酚、咖啡碱模型的R^2分别达到0.958 6和0.967 9;RMSEP分别为0.396 2和0.291 3。经独立验证所建模型效果良好,具有较高的预测精度,可为茶粉中品质成分的快速检测提供理论和实际依据。

The content of tea polyphenols and caffeine in tea powder was quantitatively analyzed based on near infrared spectroscopy,and the quantitative analysis model was established by partial least squares method,while the variables of spectral characteristic wavelength in the entire region of the spectrum were screened through CARS,GA-PLS and UVE.And use the standard normal variable transformation(SNV),derivative and multiple scattering correction(MSC)pretreatment method to optimize the models.R^2,RMSEC,RMSEP and the best main factors were used for model evaluation.The results showed that the characteristic wave band selection was vital to model optimization,and improved the model operating speed.The optimization effect of GA-PLS was the most significant.R^2 of tea polyphones and caffeine reached 0.958 6 and 0.967 9,and the root mean squared error of prediction(RMSEP)were respectively 0.396 2 and 0.291 3.After independent verification,the model had good predictive precision.It provided a theoretical and practical basis for the rapid detection of quality components in the tea powder.