基于CARS变量选择方法的小麦硬度测定研究  

Study of wheat hardness determination based on CARS variable selection method

作　　者：姜明伟 王彩红[1] 张庆辉[1] Jiang Mingwei;Wang Caihong;Zhang Qinghui(School of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China)

机构地区：[1]河南工业大学信息科学与工程学院,河南郑州450001

出　　处：《河南工业大学学报（自然科学版）》2020年第6期91-95,105,共6页Journal of Henan University of Technology：Natural Science Edition

基　　金：河南省重点科技攻关项目“基于近红外光谱的小麦硬度测定技术研究”(182102110114);粮食信息处理与控制教育部重点实验室开放课题“基于云计算和SOA架构的粮食产业一体化信息服务组件开发”(KFJJ-2017-102)。

摘　　要：为满足快速测定小麦硬度的需求,实现对未知小麦样本硬度的快速、无损检测,建立了小麦硬度预测模型。利用蒙特卡洛交叉验证统计规律对小麦硬度光谱数据进行识别,剔除异常样本。为获得具有代表性的小麦硬度预测集和校正集,基于光谱理化值共生距离法对小麦光谱数据进行集合划分,并获得预测集样本。对光谱数据进行一阶导数预处理,消除获取的小麦光谱数据中包含的高频噪声、基线漂移、样本背景等无关信息,减弱了各非目标因素对检测模型的影响。基于竞争性自适应重加权算法,筛选对模型有用的波长变量,从而提高预测模型的稳定性和预测性。建立偏最小二乘法的小麦硬度预测模型(CARS-PLS模型),该模型评价参数预测相关系数(R)和预测均方根误差(RMSEP)分别达到0.8843和0.5436,表明基于近红外光谱的CARS-PLS预测模型能够准确预测小麦硬度。This study aimed to achieve rapid and non-destructive testing of unknow n wheat hardness samples’ harness. Monte Carlo cross-validation statistical rules were used to identify wheat hardness spectral data and exclude abnormal samples. First derivative method was used to preprocess the spectral data,to obtain a representative prediction set and correction set,spectral physicochemical value symbiotic distance method was used to divide the set of wheat spectral data,and obtain the prediction set samples. First derivative method was used to preprocess the spectral data to eliminate high-frequency noise,baseline drift,sample background and other irrelevant information in the acquired spectral data,thereby to reduce the impact of various non-target factors on the detection model. Based on competitive adaptive reweighting algorithm,wavelength variables that were useful to the model were screened,thereby improving the stability and predictability of the prediction model. Finally,a partial least squares method of wheat hardness prediction model was established. The results showed that the CARS-PLS model evaluation parameters R and RMSEP reached 0. 884 3 and 0. 543 6,respectively,indicating that this model,based on near infrared spectroscopy,could accurately predict wheat hardness.

关 键 词：预处理 SPXY法 CARS-PLS模型 蒙特卡洛交叉验证法 模型评价参数 

分 类 号：TS210.1[轻工技术与工程—粮食、油脂及植物蛋白工程]