基于近红外的掺糖红茶快速无损检测方法

Rapid Non-Destructive Detection Method for Black Tea With Exogenous Sucrose Based on Near-Infrared Spectroscopy

为实现成品红茶中外源蔗糖含量的快速有效检测,将凤庆大叶种红茶作为研究样本,采用近红外光谱技术,构建了成品红茶中外源蔗糖含量的定量预测模型。首先,制作不同外源蔗糖含量(0、250、500和750 g)成品红茶样品并采集其近红外光谱数据。为提高模型预测精度,选取标准正态变换(SNV)、多元散射校正(MSC)、平滑(Smooth)和中心化(Center)4种不同的预处理方法降噪处理后建立偏最小二乘回归(PLSR)模型,根据模型效果,优选出最佳的SNV预处理方法,其校正集相关系数(R_(c))为0.907,预测集相关系数(R_(p))为0.826,相对标准偏差(RPD)为1.75。为减少光谱中冗余信息对模型运算速度的影响,利用竞争性自适应加权算法(CARS)、混合蛙跳算法(SFLA)、迭代空间收缩算法结合迭代保留信息变量算法(VCPA-IRIV)和变量迭代空间收缩算法(VISSA)等方法从SNV预处理后的光谱中提取对蔗糖敏感的特征波长,利用主成分分析(PCA)将全光谱和所筛选的特征波长降维处理后,分别建立线性PLSR和非线性的支持向量回归(SVR)、随机森林(RF)定量预测模型。结果表明,经过SNV预处理后,非线性的SVR和RF模型性能优于线性的PLSR模型,其中VCPA-IRIV-SVR为最优模型,其R_(c)值为0.950,R_(p)值为0.924,RPD值为2.51。研究表明近红外光谱技术对于红茶加工过程中掺杂蔗糖含量的定量预测是可行的,为实现红茶安全质量的无损检测提供了支撑。

In order to realize the rapid and effective detection of exogenous sucrose content in finished black tea,Fengqing large-leaved species tea was used as a research sample,and a quantitative prediction model for exogenous sucrose content in finished black tea was constructed by using near-infrared spectroscopy.First,near-infrared spectral data were collected during the production of finished black tea samples with different exogenous sucrose contents(0,250,500 and 750 g).When processing the data,in order to improve the prediction accuracy of the model,four different preprocessing methods,standard normal transformation(SNV),multivariate scattering correction(MSC),smoothing(Smooth)and centering(Center),were selected to reduce noise and establish partial least squares regression(PLSR)model,according to the effect of the model,the best SNV preprocessing method was selected,the correction set correlation coefficient(R_(c))was 0.907,the prediction set correlation coefficient(R_(p))was 0.826,and the relative percent deviation(RPD)was 1.75.In order to reduce the impact of redundant information in the spectrum on the model operation speed,the competitive adaptive reweighted sampling(CARS),shuffled frog leaping algorithm(SFLA),variable combination population analysis iteratively retaining informative variables(VCPA-IRIV)and variable iterative space shrinkage algorithm(VISSA)to extract the characteristic wavelengths sensitive to sucrose from the SNV preprocessed spectrum.After the full spectrum and the selected characteristic wavelengths were dimensionally reduced by principal component analysis(PCA),linear PLSR and nonlinear support vector regression(SVR)and random forest(RF)quantitative prediction models were established respectively.The results show that after SNV preprocessing,the performance of the nonlinear SVR and RF models is better than that of the linear PLSR model,among which VCPA-IRIV-SVR is the optimal model,its R_(c)value is 0.950,R_(p)value is 0.924,and RPD value is 2.51.The research shows that near-infrared spectroscopy is feasible for the quantitative prediction of sucrose content in black tea processing,which provides a theoretical support for the non-destructive testing of black tea safety and quality.