基于中红外光谱技术检测茶叶中非法添加滑石粉的研究

研究傅里叶变化红外透射光谱(FTIR)结合化学计量学方法检测茶叶中非法添加滑石粉的可行性。首先获取掺杂量不同的滑石粉(0.00, 0.15, 0.25, 0.35, 0.50, 0.65, 0.75, 0.85, 1.00, 1.10, 1.25, 1.50 mg·g-1)210个茶叶样本光谱。为了突显出光谱中的细微变化，采用Savitzky-Golay(SG) 平滑、标准化和标准正态变量(SNV)三种方法对原始光谱进行预处理。其中，处理效果最好的是SNV方法。随后探究光谱与掺杂量之间的定量关系，采用反向间隔偏最小二乘法(biPLS)和连续投影算法(SPA)的结合进行特征波数的选择，最终选出来5个特征波数。并利用偏最小二乘回归算法(PLS)和最小二乘支持向量机算法(LS-SVM)建立基于这5个特征波数的回归模型。其中LS-SVM模型具有更高的相关系数(R_P=0.921)和更小的均方根误差(RMSEP=0.131)，所以该模型具有更好的稳定性及更高的预测能力。综上所述，红外光谱技术可以定量地检测出茶叶中非法添加的滑石粉。

Study on Detection of Talcum Powder in Green Tea Based on Fourier Transform Infrared (FTIR) Transmission Spectroscopy

This paper studied the feasibility to detect tal cum powder illegally added in tea based on Fourier transform infrared (FTIR) tra nsmission spectroscopy with chemometric methods.In this study,210 tea samples with 12 dose concentrations of talcum powder were prepared for FTIR spectra acqu irement.Firstly,Savitzky-Golay (SG) smoothing,normalize and standard normal variate (SNV) were used to preprocess the raw spectra.It was shown that SNV pre processing had the best performance.After that,a hybrid method of backward int erval partial least squares (biPLS) regression and successive projections algori thm (SPA) was used to select 5 characteristic wavenumbers,which only accounted for 0.18％ of the whole wavenumbers.Then,PLS regression and least square suppor t vector machine (LS-SVM) were utilized to build linear and nonlinear models ba sed on these 5 characteristic wavenumbers,respectively.Finally,the optimal mo del was achieved with LS-SVM with highRP=0.921 and low RMSEP=0.131.It concluded that talcum powder in green tea could be detected based on FTIR spectr oscopy coupled with chemometrics.