摘要
为了更好地利用近红外光谱分析技术对玉米伏马菌素含量进行预测,减小因玉米产地间的差异对玉米近红外光谱预测模型的影响,以不同产地的玉米作为研究对象,利用x-y共生距的方法将试验样本划分为校正集与验证集,采用经典的偏最小二乘法分别建立不同产地和混合产地的玉米伏马菌素预测模型,并采用验证集样本分别对模型的预测精度进行验证。为了减小建模及预测过程的运算量,采用连续投影算法(SPA)和竞争性自适应加权算法(CARS)对不同产地玉米的近红外光谱的特征波长进行筛选,筛选出22个特征波长变量作为输入,大大降低了建模及预测过程的运算量,同时预测准确度也有所改善,其预测相关系数达到0.954,为快速、无损地实现对玉米伏马菌素的检测提供了可靠的理论依据。
In order to forecast the content of fumonisin in corn using the infrared spectrum analysis technology,and reduce the differences caused by their yield region,the influence of experiment using 4different origin of domestic corn were investigated.The method of using x-yco-occurrence distance could be divided into calibration sample and validation sets,using the classical and different regions based on the partial least squares,and then the prediction model of fumonisin maize hybrid origin,and USES the validation set samples to validate the prediction precision,respectively.In order to reduce the computational complexity of modeling and forecasting process,experiments using continuous projection algorithm（SPA） and competitive adaptive weighting algorithm（CARS）the characteristics of the infrared spectra of different origin corn wavelength filter,and 22 characteristics were filtered out.Then these 22 wavelengths were input as variables,and this greatly reduced the computational complexity of modeling and forecasting process,as well as improved the prediction accuracy,with the correlation coefficient at 0.954.
出处
《食品与机械》
CSCD
北大核心
2017年第2期56-59,共4页
Food and Machinery
基金
商洛学院科学研究项目(编号:16SKY-FWDF005)
商洛市科学技术研究发展计划项目(编号:SK2016-52)
