摘要
为克服板栗近红外光谱变量多、共线性强等缺点,该文对标准正态变量变换预处理后的板栗近红外光谱进行傅里叶变换,并用不同方法建模,提高识别精度。采用试探法提取近红外光谱傅里叶系数,建立了基于最小二乘支持向量机分类器的霉变板栗识别模型。当提取前35点傅里叶系数时,板栗的平均识别正确率为93.56%;构造GA-LSSVM算法,建立的霉变板栗识别模型所用傅里叶系数减少为13点,对测试集中合格板栗、表面霉变板栗和内部霉变板栗的平均识别正确率分别为95.89%、100%和98.25%,板栗的总体平均识别正确率提高到97.54%。为霉变板栗的识别提供了快速鉴别分析方法。
To overcome the shortage of massive data and multi colinearity in near infrared spectroscopy analysis,Fourier transform was employed on chestnut NIR spectra which was preprocessed with standard normal variate（SNV）,and different modeling methods were utilized to improve recognition accuracy.Fourier coefficients were extracted from NIR spectra through trial method,and then discrimination models of moldy chestnuts were established based on least squares support vector machine（LSSVM）.The highest mean accurate recognition rate of 93.56% was obtained when the first 35 Fourier coefficients were selected,and a hybrid algorithm,GA-LSSVM was developed and used to optimize the number of Fourier coefficients.As a result,the number of Fourier coefficients used for building recognition models was successfully reduced to 13,while the mean accurate recognition rate was raised to 97.54% and the discriminating rates of qualified chestnut,surface moldy chestnut and internal moldy chestnut were 95.89%,100% and 98.25% for prediction,respectively.It is suggested that near infrared spectroscopy coupled with Fourier transform and GA-LSSVM methods can be used for rapid detection of moldy chestnut.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2011年第3期331-335,共5页
Transactions of the Chinese Society of Agricultural Engineering
基金
高等学校博士学科点专项科研基金(20090146110018)
作者简介
周竹（1984-），男，湖北汉川人，研究方向为智能化检测技术。武汉华中农业大学工学院，430070。Email：zhouzhuniba@163．com
通信作者；李小昱（1953-），女，河南叶县人，教授，博士生导师，中国农业工程学会高级会员（E041200068S），研究方向为智能化检测技术。武汉华中农业大学工学院，430070。Email：lixiaoyu@mail．hzau．edu．cn
