Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

文献类型： 外文期刊

第一作者： Zhao, Xin

作者： Zhao, Xin;Wang, Wei;Chu, Xuan;Kimuli, Daniel;Li, Chunyang

作者机构：

关键词： hyperspectral imaging;maize kernel;Aspergillus parasiticus;early detection

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.679； 五年影响因子：2.736 ）

ISSN： 2076-3417

年卷期： 2017 年 7 卷 1 期

页码：

收录情况： SCI

摘要： Fungi infection in maize kernels is a major concern worldwide due to its toxic metabolites such as mycotoxins, thus it is necessary to develop appropriate techniques for early detection of fungi infection in maize kernels. Thirty-six sterilised maize kernels were inoculated each day with Aspergillus parasiticus from one to seven days, and then seven groups (D-1, D-2, D-3, D-4, D-5, D-6, D-7) were determined based on the incubated time. Another 36 sterilised kernels without inoculation with fungi were taken as control (DC). Hyperspectral images of all kernels were acquired within spectral range of 921-2529 nm. Background, labels and bad pixels were removed using principal component analysis (PCA) and masking. Separability computation for discrimination of fungal contamination levels indicated that the model based on the data of the germ region of individual kernels performed more effectively than on that of the whole kernels. Moreover, samples with a two-day interval were separable. Thus, four groups, DC, D1-2 (the group consisted of D-1 and D-2), D3-4 (D-3 and D-4), and D5-7 (D-5, D-6, and D-7), were defined for subsequent classification. Two separate sample sets were prepared to verify the influence on a classification model caused by germ orientation, that is, germ up and the mixture of germ up and down with 1:1. Two smooth preprocessing methods (Savitzky-Golay smoothing, moving average smoothing) and three scatter-correction methods (normalization, standard normal variate, and multiple scatter correction) were compared, according to the performance of the classification model built by support vector machines (SVM). The best model for kernels with germ up showed the promising results with accuracies of 97.92% and 91.67% for calibration and validation data set, respectively, while accuracies of the best model for samples of the mixed kernels were 95.83% and 84.38%. Moreover, five wavelengths (1145, 1408, 1935, 2103, and 2383 nm) were selected as the key wavelengths in the discrimination of fungal contamination levels. In general, near-infrared hyperspectral imaging can be used for early detection of fungal contamination in maize kernels.

分类号：

  • 相关文献

[1]Measuring the Moisture Content in Maize Kernel Based on Hyperspctral Image of Embryo Region. Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua. 2016

[2]Differential Expression of miR-125a-5p and let-7e Predicts the Progression and Prognosis of Non-Small Cell Lung Cancer. Le, Han-bo,Zhang, Yong-Kui,Zhu, Wang-Yu,Luo, Bin,He, Jian-ying,Chen, Dong-dong,Xu, Li-Yun,Huang, Yan-Yan,Liu, Xiao-Guang,An, Ji-yuan. 2014

[3]Early Detection of Corynespora Spot on Cucumber Leaves Based on FTIR Spectroscopy. Chai A-li,Li Bao-ju,Shi Yan-xia,Xie Xue-wen. 2011

[4]Identification of seedling cabbages and weeds using hyperspectral imaging. Wei, Deng,Zhao Chunjiang,Xiu, Wang,Huang, Yanbo,Wei, Deng,Zhao Chunjiang,Xiu, Wang,Wei, Deng,Zhao Chunjiang,Xiu, Wang,Wei, Deng,Zhao Chunjiang,Xiu, Wang. 2015

[5]Effectively Predicting Soluble Solids Content in Apple Based on Hyperspectral Imaging. Huang Wen-qian,Li Jiang-bo,Chen Li-ping,Guo Zhi-ming. 2013

[6]Comparative analysis of models for robust and accurate evaluation of soluble solids content in 'Pinggu' peaches by hyperspectral imaging. Chen, Liping. 2017

[7]Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging. Zhang, Dongyan,Zhang, Lifu,Zhang, Dongyan,Wang, Xiu,Zhang, Dongyan,Wang, Xiu,Lin, Fenfang,Huang, Yanbo. 2016

[8]Rapid determination of biogenic amines in cooked beef using hyperspectral imaging with sparse representation algorithm. Yang, Dong,Lu, Anxiang,Wang, Jihua,Yang, Dong,Wang, Jihua,Lu, Anxiang,Ren, Dong,Wang, Jihua,Lu, Anxiang,Wang, Jihua. 2017

[9]Multi-Temporal Hyperspectral and Radar Remote Sensing for Estimating Winter Wheat Biomass in the North China Plain. Hennig, Simon D.,Koppe, Wolfgang,Gnyp, Martin L.,Bareth, Georg,Chen, Xinping,Li, Fei,Miao, Yuxin,Jia, Liangliang. 2012

[10]Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Zhang, Baohua,Huang, Wenqian,Li, Jiangbo,Zhao, Chunjiang,Fan, Shuxiang,Wu, Jitao,Zhang, Baohua,Zhao, Chunjiang,Liu, Chengliang. 2014

[11]Principles and Applications of Hyperspectral Imaging Technique in Quality and Safety Inspection of Fruits and Vegetables. Zhang Bao-hua,Zhang Bao-hua,Li Jiang-bo,Fan Shu-xiang,Huang Wen-qian,Zhang Chi,Wang Qing-yan,Xiao Guang-dong. 2014

[12]Identification of Wheat Cultivars Based on the Hyperspectral Image of Single Seed. Zhu, Dazhou,Wang, Cheng,Wu, Qiong,Zhao, Chunjiang,Pang, Binshuang,Shan, Fuhua. 2012

[13]HYPERSPECTRAL IMAGE FOR DISCRIMINATING APHID AND APHID DAMAGE REGION OF WINTER WHEAT LEAF. Luo Juhua,Huang Wenjiang,Guan Qingsong,Zhao Jinling,Zhang Jingcheng. 2013

[14]Detection of early bruises on peaches (Amygdalus persica L.) using hyperspectral imaging coupled with improved watershed segmentation algorithm. Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian. 2018

[15]Study on the optimal algorithm prediction of corn leaf component information based on hyperspectral imaging. Wu, Qiong,Xu, Tongyu,Wu, Qiong,Wang, Jihua,Wang, Cheng. 2016

[16]Vertical features of yellow rust infestation on winter wheat using hyperspectral imaging measurements. Zhao, Jinling,Zhang, Dongyan,Huang, Linsheng,Zhang, Qing,Liu, Wenjing,Yang, Hao. 2016

[17]Combination of spectral and textural information of hyperspectral imaging for the prediction of the moisture content and storage time of cooked beef. Yang, Dong,Lu, Anxiang,Wang, Jihua,Yang, Dong,Wang, Jihua,He, Dandan,Lu, Anxiang,Ren, Dong,Wang, Jihua,Lu, Anxiang,Wang, Jihua. 2017

[18]Measurement of Light Penetration Depth through Milk Powder Layer in Raman Hyperspectral Imaging System. Liu Chen,Chen Li-ping,Liu Chen,Wang Qing-yan,Huang Wen-qian,Chen Li-ping,Yang Gui-yan,Wang Xiao-bin,Liu Chen,Wang Qing-yan,Huang Wen-qian,Chen Li-ping,Yang Gui-yan,Wang Xiao-bin,Liu Chen,Wang Qing-yan,Huang Wen-qian,Chen Li-ping,Yang Gui-yan,Wang Xiao-bin. 2017

[19]Recognition of wheat preharvest sprouting based on hyperspectral imaging. Wu, Qiong,Wang, Jihua,Wu, Qiong,Zhu, Dazhou,Wang, Cheng,Ma, Zhihong,Wang, Jihua. 2012

[20]Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data. Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi.

作者其他论文 更多>>

微信小程序