摘要
变量选择技术是光谱建模的重要环节。本研究提出了一种新的变量选择方法——自加权变量组合集群分析法(AWVCPA),首先通过二进制矩阵采样法(BMS)对变量空间进行采样;其次通过对变量出现频率(Fre)和偏最小二乘回归系数(Reg)两种信息向量(IVs)做加权处理,得到了每个光谱变量的贡献值,进而考虑到了Fre和Reg两类IVs对于光谱建模的影响;最后通过指数衰减函数(EDF)删除贡献小的波长点,进而实现特征变量选取。以啤酒和玉米两组近红外光谱数据为例,基于偏最小二乘法(PLS)建立啤酒中酵母浓度预测模型和玉米中油浓度预测模型,对比其它变量选择方法。研究表明,在相同条件下,基于AWVCPA变量选择方法建立的预测模型都取得了最优的预测精度,对啤酒中酵母浓度的预测,相比全光谱PLS模型,RMSEP由0.5348下降到0.1457,预测精度提高了72.7%;对玉米含油量的预测,相比全光谱PLS模型,预测均方根误差(RMSEP)由0.0702下降到了0.0248,预测精度提高了64.7%。
Near-infrared spectroscopy(NIR)is widely used in the area of food quantitative and qualitative analysis.Variable selection technique is a critical step of the spectrum modeling with the development of chemometrics.In this study,a novel variable selection strategy,automatic weighting variable combination population analysis(AWVCPA),was proposed.Firstly,binary matrix sampling(BMS)strategy that gives each variable the same chance to be selected and generates different variable combinations,was used to produce a population of subsets to construct a population of sub-models.Then,the variable frequency(Fre)and partial least squares regression(Reg),which were two kinds of information vector(IVs)were weighted to obtain the value of the contribution of each spectral variables,the influence of two IVs of Rre and Reg was considered to each spectral variable.Finally,it used the exponentially decreasing function(EDF)to remove the low contribution wavelengths so as to select the characteristic variable.In the case of near infrared spectrum of beer and corn,the prediction model based on partial least squares(PLS)was established.Compared with other variable selection methods,the research showed that AWVCPA was the best variable selection strategy in the same situation.It had72.7%improvement compared AWVCPA-PLS with PLS and the predicted root mean square error(RMSEP)decreased from0.5348to0.1457on beer dataset.It had64.7%improvement compared AWVCPA-PLS with PLS and the RMSEP decreased from0.0702to0.0248on corn dataset.
作者
赵环
宦克为
石晓光
郑峰
刘丽莹
刘微
赵春英
ZHAO Huan;HUAN Ke-Wei;SHI Xiao-Guang;ZHENG Feng;LIU Li-Ying;LIU Wei;ZHAO Chun-Ying(College of Science, Changchun University of Science and Technology, Changchun 130022, China)
出处
《分析化学》
SCIE
EI
CAS
CSCD
北大核心
2018年第1期136-142,共7页
Chinese Journal of Analytical Chemistry
基金
国家公益性行业(气象)科研专项课题(No.GYHY201406037)
高等学校博士学科点专项科研基金联合项目(No.20112216110006)资助
关键词
近红外光谱
化学计量学
变量选择
自加权变量组合集群分析法
信息向量
Near infrared spectroscopy
Chemometrics
Variable selection
Automatic weighting variable combination population analysis
Information vector
