食品科学

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基于高光谱成像的马铃薯环腐病无损检测

郭红艳1,刘贵珊1,*,吴龙国2,王松磊1,2,康宁波2,陈亚斌1,何建国1,2,贺晓光1   

  1. 1.宁夏大学农学院,宁夏 银川 750021;2.宁夏大学土木水利工程学院,宁夏 银川 750021
  • 出版日期:2016-06-25 发布日期:2016-06-29
  • 通讯作者: 刘贵珊
  • 基金资助:

    2013年度宁夏自然科学基金项目(NZ13005)

Hyper-Spectral Imaging Technology for Nondestructive Detection of Potato Ring Rot

GUO Hongyan, LIU Guishan, WU Longguo, WANG Songlei, KANG Ningbo, CHEN Yabin, HE Jianguo, HE Xiaoguang   

  1. 1. School of Agriculture, Ningxia University, Yinchuan 750021, China;
    2. School of Civil Engineering and Water Conservancy, Ningxia University, Yinchuan 750021, China
  • Online:2016-06-25 Published:2016-06-29
  • Contact: LIU Guishan

摘要:

为探讨高光谱成像技术无损检测马铃薯环腐病的可行性,采用反射高光谱(980~1 650 nm)成像技术,以120 个马铃薯样本(合格60 个,环腐60 个)为研究对象,对比多元散射校正、标准正态变换、卷积+一阶导数等对建模的影响,优选出多元散射校正的光谱预处理方法;然后基于偏最小二乘回归系数法提取9 个特征波长(993、1 005、1 009、1 031、1 112、1 162、1 165、1 225、1 636 nm),建立特征波长下马铃薯环腐病的2 类线性判别分析(linear discriminant analysis,LDA)模型和4 类支持向量机(support vector machine,SVM)模型,即Fisher-LDA、马氏距离-LDA、线性核SVM、径向基核SVM、多项式核SVM和S型核SVM。结果表明,LDA模型中马氏距离法最优,SVM模型中S型核SVM最优,LDA模型整体优于SVM模型,最终确定基于马氏距离LDA的马铃薯环腐病判别模型为最佳模型,校正集、验证集识别率分别为100%和93.33%。实验结果表明高光谱无损检测马铃薯环腐病具有可行性。

关键词: 高光谱成像技术, 马铃薯, 环腐病, 无损检测

Abstract:

To investigate the feasibility of using hyper-spectral imaging technique to detect potato ring rot, hyperspectral
imaging operated in reflectance mode in the wavelength range of 980–1 650 nm was applied to 120 potato samples
(60 qualified and 60 ring rot). The effects of multiple scattering correction, standard normal transformation and savitzky-golay +
first derivative on model performance were compared. Multiple scattering correction was chosen as the best spectral preprocessing
method. Then, 9 characteristic wavelengths (993, 1 005, 1 009, 1 031, 1 112, 1 162, 1 165, 1 225, and 1 636 nm)
were extracted based on the partial least squares method. Two linear discriminant analysis (LDA) models, Fisher-LDA and
Mahalanobis distance-LDA, and four support vector machine (SVM) models, linear kernel SVMs, SVM with radial basis
kernel, polynomial kernel SVM and SVM with Sigmoid kernel, were built for ring rot potato at characteristic wavelengths.
The results showed that Mahalanobis distance-LDA was better than Fisher-LDA while Sigmoid kernel performed best
among all the SVM models. The LDA models were overall better than the SVM models. Thus, the LDA model of potato
ring rot based on Mahalanobis distance was the best model. Its recognition rate was 100% for calibration set and 93.33% for
validation set. This study indicated that hyper-spectral imaging technology can be used to identify potato ring rot.

Key words: hyper-spectral imaging, potato, ring rot, non-destructive detection

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