摘要
为实现对奶粉中非法食品添加剂三聚氰胺的精确定量检测,使用太赫兹时域光谱系统测定了三聚氰胺(质量分数梯度为0%~20%)与奶粉混合物的太赫兹吸收谱曲线。首先,利用主成分回归、支持向量机回归、偏最小二乘回归和最小二乘支持向量机回归(LSSVR)对混合物中三聚氰胺质量分数进行预测。结果显示,LSSVR的预测精度最高,预测集相关系数RP为0.99838,预测集均方根误差fRMSEP为0.41%。其次,为进一步提升LSSVR预测的精度,使用粒子群算法、遗传算法、布谷鸟算法、灰狼算法对LSSVR中正则化参数C和已确定核函数为高斯核函数后核参数γ进行参数优化。结果表明,经过4种算法优化后LSSVR预测精度均明显提高,其中经灰狼算法优化后的LSSVR对混合物中三聚氰胺的预测精度最高(RP为0.99925,fRMSEP为0.28%)。该算法可为食品添加剂的定量检测提供新的方法和思路。
Objective In recent years,the food safety hazards caused by the illegal use and abuse of food additives during their use have once again attracted much attention from society.The research and development of non-destructive,fast,accurate,and efficient qualitative and quantitative detection technologies and methods for food additives have become a research hotspot for scholars.At present,traditional detection methods for food additives include ion chromatography,liquid chromatography,liquid chromatography-mass spectrometry,gas chromatography,and molecular spectrometry.The traditional methods have obvious shortcomings,such as high equipment cost,long detection cycle,high cost,uneven detection accuracy,high requirements for the operation of technicians and the purity of organic solvents,and complex detection operations.Compared with traditional detection methods,the application of new technologies such as immune detection,biosensor,and spectral analysis has supplemented and improved old technologies,thereby promoting the development of food additive detection technology.The detection method based on terahertz spectroscopy technology is non-destructive,fast,and efficient,and has been widely applied in fields of food,medicine,and environmental detection in recent years.Methods Firstly,we construct melamine samples with concentration gradients and obtain experimental training and testing sets by a transmission terahertz time-domain spectroscopy system.For the high-dimensional spectral data obtained from the experiment,a Savitzky-Golay convolutional filter is adopted for preprocessing to reduce quantitative prediction errors.Secondly,based on the dimensional characteristics of spectral data,we build four regression prediction models including PCR,SVR,PLSR,and LSSVR for data analysis.The obtained experimental spectral data are compared in terms of the predictive performance after linear regression dimensionality reduction(PCR,PLSR)and nonlinear regression dimensionality enhancement(SVR,LSSVR),which are processed at opposite angles.The correlation coefficient RP of the prediction set and the root mean square error of the prediction set(RMSEP)are employed as indicators for model performance evaluation.Finally,according to the optimal evaluation index,we find that the prediction effect of the LSSVR model is optimal.We leverage particle swarm optimization(PSO),genetic algorithm(GA),Cuckoo search algorithm(CS),and grey wolf optimization(GWO)to calculate the regularization parameter C in LSSVR and the kernel parameter after the determined kernel function is Gaussian kernel functionγfor parameter optimization.Results and Discussions The filtering preprocessing operation for spectral data yields sound effect(Fig.1).We employ four different regression models(PCR,PLSR,SVR,and LSSVR)to predict the melamine content in milk powder,and adopt the correlation coefficient of the prediction set and RMSEP as the model evaluation coefficients.After comparing the evaluation coefficients of the four models,it is determined that the minimum correlation coefficient of the linear model PCR's prediction set is 0.99715,the maximum RMSEP is 0.50%,and the nonlinear model LSSVR has the best prediction performance.Its prediction phase set relationship number RP is 0.99838 and RMSEP is 0.41%,which indicates that the nonlinear model has better detection performance for terahertz spectral data(Fig.3).On this basis,we utilize swarm intelligence algorithms(PSO,GA,CS,and GWO)whose performances are significantly better than those of traditional methods to optimize hyperparameter selection of LSSVR model respectively.The prediction accuracy of the model after optimization by the four algorithms has been improved.Among them,the evaluation coefficient of the GWOLSSVR model is the best,with RP of 0.99925 and RMSEP of 0.28%(Fig.4).Conclusions Results show that nonlinear models can be better applied to the detection of food additives by terahertz technology.The optimized GWO-LSSVR model can improve the accuracy of regression models in predicting mixture concentration and the quantitative detection accuracy of melamine based on terahertz spectral data.Additionally,it can promote the application of terahertz spectral technology in food additive detection and provide new methods and ideas for the quantitative analysis of food additives.However,the predictive performance and stability of the model are also relative,and the issues of computational complexity and time consumption should also be considered important factors in algorithm selection.
作者
郭以恒
燕芳
赵渺钰
卓炫
Guo Yiheng;Yan Fang;Zhao Miaoyu;Zhuo Xuan(School of Information Engineering,Inner Mongolia University of Science&Technology,Baotou 014010,Inner Mongolia,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2023年第19期289-295,共7页
Acta Optica Sinica
基金
内蒙古自治区关键技术攻关计划(2021GG0361)
内蒙古自治区直属高校基本科研业务费项目。
关键词
光谱学
太赫兹时域光谱
定量分析
参数优化
食品添加剂
spectroscopy
terahertz time-domain spectroscopy
quantitative analysis
parameter optimization
food additive
