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。
To investigate the feasibility of using hyper-spectral imaging technique to detect potato ring rot, hyperspectralimaging 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 preprocessingmethod. 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 andMahalanobis distance-LDA, and four support vector machine （SVM） models, linear kernel SVMs, SVM with radial basiskernel, 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 bestamong all the SVM models. The LDA models were overall better than the SVM models. Thus, the LDA model of potatoring rot based on Mahalanobis distance was the best model. Its recognition rate was 100% for calibration set and 93.33% forvalidation set. This study indicated that hyper-spectral imaging technology can be used to identify potato ring rot.
出处
《食品科学》
EI
CAS
CSCD
北大核心
2016年第12期203-207,共5页
Food Science
基金
2013年度宁夏自然科学基金项目(NZ13005)
关键词
高光谱成像技术
马铃薯
环腐病
无损检测
hyper-spectral imaging
potato
ring rot
non-destructive detection
