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当前位置: 首页 > 文章 > 基于高光谱技术及SPXY和SPA的玉米毒素检测模型建立 食品科学 2018 (16) 328-335
Position: Home > Articles > Predictive Model for Detection of Maize Toxins with Sample Set Partitioning Based on Joint x-y Distance(SPXY) Algorithm and Successive Projections Algorithm(SPA) Based on Hyperspectral Imaging Technology FOOD SCIENCE 2018 (16) 328-335

基于高光谱技术及SPXY和SPA的玉米毒素检测模型建立

作  者：
于慧春;娄楠;殷勇;刘云宏
单  位：
河南科技大学食品与生物工程学院
关键词：
玉米;高光谱;无损检测;黄曲霉毒素B1;玉米赤霉烯酮
摘  要：
应用高光谱技术研究和构建霉变玉米黄曲霉毒素B_1(aflatoxin B_1,AFB_1)和玉米赤霉烯酮(zearalenone,ZEN)含量的检测方法,通过建立霉变玉米中这2种毒素含量的预测模型,实现对玉米霉变程度的快速、无损、准确判别。首先,通过对比5种预处理方法,确定标准正态变量校正法对原始光谱数据进行预处理;然后,采用光谱-理化值共生距离算法结合偏最小二乘回归(partial least squares regression,PLSR)法分析不同校正集样本预测AFB_1和ZEN含量的差异,并分别优选出130个和140个校正集样本;在采用均匀光谱间隔法对原始光谱变量进行初降维的基础上,对比连续投影算法(successive projections algorithm,SPA)和竞争性自适应重加权算法2种变量提取法。结果表明:经SPA分别筛选出17个特征波段且基于较少校正集样本和特征波长光谱信息建立的PLSR模型能够获得较优的预测结果,对应AFB_1和ZEN含量预测集的相关系数和均方根误差(root mean square error of prediction,RMSEP)(R_(pre)~2,RMSEP)由最初的(0.994 4,0.984 6)和(0.991 6,2.320 9)分别变为(0.997 3,0.681 5)和(0.997 7,1.144 1),在降低模型复杂度的情况下提高了预测精度,表明该模型对这2种毒素含量能够实现较强的预测能力。因此,利用高光谱技术对玉米AFB_1和ZEN含量实施无损检测具有可行性。
译  名：
Predictive Model for Detection of Maize Toxins with Sample Set Partitioning Based on Joint x-y Distance(SPXY) Algorithm and Successive Projections Algorithm(SPA) Based on Hyperspectral Imaging Technology
作  者：
YU Huichun;LOU Nan;YIN Yong;LIU Yunhong;College of Food and Bioengineering, Henan University of Science and Technology;
单  位：
YU Huichun%LOU Nan%YIN Yong%LIU Yunhong%College of Food and Bioengineering, Henan University of Science and Technology
关键词：
maize;;hyperspectral imaging technology;;non-destructive detection;;aflatoxin B1;;zearalenone
摘  要：
In this paper, a rapid, non-destructive and accurate method for detecting the contents of aflatoxin B_1(AFB_1) and zearalenone(ZEN) in maize using hyperspectral imaging was established by developing predictive models. Among 5 spectral pretreatments tested, standard normal variate(SNV) was found to be the best method for preprocessing the original spectral data. Sample set partitioning based on joint x-y distance(SPXY) algorithm combined with partial least squares regression(PLSR) was used to screen the differences in the predicted contents of AFB_1 and ZEN from different calibration set samples, and 130 and 140 calibration set samples were selected for AFB_1 and ZEN, respectively. On the basis of dimensionality reduction by the uniform spectral spacing(USS) method, the two variable extraction methods: successive projections algorithm(SPA) and competitive adaptive reweighted sampling algorithm(CARS) were compared. The results showed that 17 characteristic wavelengths were selected for AFB_1 and ZEN, respectively and the PLSR model established based on fewer calibration set samples with the characteristic wavelengths had better predictive performance. The correlation coefficients(R_(pre)~2) and root mean square error of prediction(RMSEP) for AFB_1 and ZEN content were 0.997 3 and 0.681 5, and 0.997 7 and 1.144 1, respectively, while the initial R_(pre)~2 and RMSEP values were 0.994 4 and 0.984 6, and 0.991 6 and 2.320 9, respectively. The results indicated that despite reducing the reducing the complexity of the model, the predictive accuracy could be improved. Therefore, it is feasible to non-destructively predict the AFB_1 and ZEN content in maize by applying hyperspectral imaging technology.

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