[1]沈广辉,曹瑶瑶,刘馨,等.近红外高光谱成像结合特征波长筛选识别小麦赤霉病瘪粒[J].江苏农业学报,2021,(02):509-516.[doi:doi:10.3969/j.issn.1000-4440.2021.02.029]
　SHEN Guang-hui,CAO Yao-yao,LIU Xin,et al.Identification of Fusarium damaged kernels using near infrared hyperspectral imaging and characteristic bands selection[J].,2021,(02):509-516.[doi:doi:10.3969/j.issn.1000-4440.2021.02.029]
点击复制

近红外高光谱成像结合特征波长筛选识别小麦赤霉病瘪粒()
分享到：

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年02期
页码:
509-516
栏目:
加工贮藏·质量安全
出版日期:
2021-04-30

文章信息/Info

Title:
Identification of Fusarium damaged kernels using near infrared hyperspectral imaging and characteristic bands selection
作者:
沈广辉1; 2; 3; 曹瑶瑶4; 刘馨1; 2; 3; 徐剑宏1; 2; 3; 史建荣1; 2; 3; LEE Yin-won5
（1.江苏省农业科学院农产品质量安全与营养研究所，江苏南京210014；2.江苏省食品质量安全重点实验室——省部共建国家重点实验室培育基地，江苏南京210014；3.农业农村部农产品质量安全控制技术与标准重点实验室，江苏南京210014；4.江苏省农业科学院农业资源与环境研究所，江苏南京210014；5.Department of Agricultural Biotechnology, Seoul National University, Seoul, South Korea 08826）
Author(s):
SHEN Guang-hui1; 2; 3; CAO Yao-yao4; LIU Xin1; 2; 3; XU Jian-hong1; 2; 3; SHI Jian-rong1; 2; 3; LEE Yin-won5
(1.Institute of Food Safety and Nutrition, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China;2.Jiangsu Provincial Key Laboratory for Food Quality and Safety-State Key Laboratory Cultivation Base Built by Province and Ministry, Ministry of Science and Technology, Nanjing 210014, China;3.Key Laboratory for Control Technology and Standard for Agro-product Safety and Quality, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China;4.Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China;5.Department of Agricultural Biotechnology, Seoul National University, Seoul 08826, South Korea)
关键词:
高光谱成像; 赤霉病瘪粒; 近红外光谱; 无损检测
Keywords:
hyperspectral imaging; Fusarium damaged kernels; near infrared spectroscopy; non-destructive detection
分类号:
S123；TP391.41
DOI:
doi:10.3969/j.issn.1000-4440.2021.02.029
文献标志码:
A
摘要:
为实现小麦赤霉病瘪粒快速识别，本研究使用主成分分析（Principal component analysis, PCA）结合最大类间方差法（Otsu）对小麦高光谱图像进行背景分割，以赤霉病瘪粒识别正确率为评价指标，探究判别分析方法与竞争性自适应权重取样法（Competitive adaptive reweighted sampling, CARS）的最佳组合方式。结果显示，基于全谱段构建的偏最小二乘判别分析（Partial least squares discrimination analysis, PLS-DA）和支持向量机判别分析（Support vector machine discriminant analysis, SVM-DA）模型预测精度相同，外部验证集健康籽粒和赤霉病瘪粒识别正确率分别为95.2%和100.0%；基于CARS筛选的8个特征波长构建的CARS-PLS-DA模型外部验证集健康籽粒和赤霉病瘪粒识别正确率均为100.0%，预测精度高于CARS-SVM-DA模型，可有效实现赤霉病瘪粒的快速识别。研究结果将为谷物仓储和加工过程中赤霉病瘪粒高通量快速识别提供理论依据和技术支撑。
Abstract:
In order to realize rapid identification of unfilled grain from wheat infected by Fusarium, principal component analysis (PCA) combined with Otsu algorithm was used for background segmentation of wheat hyperspectral imaging. The compound mode of discriminant analysis method and competitive adaptive reweighted sampling (CARS) method were optimized based on the identification accuracy of Fusarium damaged kernels. The results indicated that, the predication accuracy of partial least squares discrimination analysis (PLS-DA) model and support vector machine discriminant analysis (SVM-DA) model constructed based on full spectrum were the same, and the recognition accuracy of healthy and Fusarium damaged kernels in the external validation set were 95.2% and 100.0%, respectively. The recognition accuracy of healthy and Fusarium damaged kernels were both 100.0% in the external validation set of CARS-PLS-DA model which was built based on eight characteristic wavelengths selected by CARS algorithm, and the prediction accuracy was higher than CARS-SVM-DA model, and could rapidly identify Fusarium damaged kernels effectively. The results can provide theoretical basis and technical support for the high throughput and rapid detection of Fusarium damaged kernels during grain storage and processing.

参考文献/References:

[1]VISCONTI A, PASCALE M. An overview on Fusarium mycotoxins in the durum wheat pasta production chain[J]. Cereal Chemistry, 2010,87(1):21-27.
[2]KOUADIO J H, MOBIO T A, BAUDRIMONT I, et al. Comparative study of cytotoxicity and oxidative stress induced by deoxynivalenol, zearalenone or fumonisin B1 in human intestinal cell line Caco-2[J]. Toxicology, 2005,213(1/2):56-65.
[3]PESTKA J J, SMOLINSKI A T. Deoxynivalenol: toxicology and potential effects on humans[J]. Journal of Toxicology and Environmental Health（Part B）, 2005,8(1):39-69.
[4]WANG H W, SUN S L, GE W Y, et al. Horizontal gene transfer of Fhb7 from fungus underlies Fusarium head blight resistance in wheat[J]. Science, 2020，368(6493):e5435.
[5]史建荣,刘馨,仇剑波,等. 小麦中镰刀菌毒素脱氧雪腐镰刀菌烯醇污染现状与防控研究进展[J]. 中国农业科学, 2014,47(18):3641-3654.
[6]ZHAO Y J, GUAN X L, ZONG Y, et al. Deoxynivalenol in wheat from the Northwestern region in China[J]. Food Additives & Contaminants（Part B）, 2018,11(4):281-285.
[7]CIRIO M, VILLARREAL M, LPEZ SEAL TOMS M, et al. Incidence of deoxynivalenol in wheat flour in argentina and GC-ECD method validation[J]. Journal of AOAC International, 2019,102(6):1721-1724.
[8]MCMASTER N, ACHARYA B, HARICH K, et al. Quantification of the mycotoxin deoxynivalenol (DON) in sorghum using GC-MS and a stable isotope dilution assay (SIDA)[J]. Food Analytical Methods, 2019,12(10): 2334-2343.
[9]VENDL O, BERTHILLER F, CREWS C, et al. Simultaneous determination of deoxynivalenol, zearalenone, and their major masked metabolites in cereal-based food by LC-MS-MS[J]. Analytical and Bioanalytical Chemistry, 2009,395(5):1347-1354.
[10]TURNER N W, BRAMHMBHATT H, SZABO-VEZSE M, et al. Analytical methods for determination of mycotoxins: an update (2009-2014)[J]. Analytica Chimica Acta, 2015, 9019(2):12-33.
[11]CLARKE F. Extracting process-related information from pharmaceutical dosage forms using near infrared microscopy[J]. Vibrational Spectroscopy, 2004,34(1):25-35.
[12]CUCCI C, DELANEY J K, PICOLLO M. Reflectance hyperspectral imaging for investigation of works of art: old master paintings and illuminated manuscripts[J]. Accounts of Chemical Research, 2016, 49(10):2070-2079.
[13]黄红娟,郑一平,楼寿松. 傅立叶显微红外化学成像在朱墨时序鉴定中的应用研究[J]. 刑事技术, 2010(4): 29-32.
[14]FERNNDEZ PIERNA J A, BAETEN V, RENIER A M, et al. Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds[J]. Journal of Chemometrics, 2004,18(7/8): 341-349.
[15]LI J G, RAO X Q, YING Y B. Detection of common defects on oranges using hyperspectral reflectance imaging[J]. Computers and Electronics in Agriculture, 2011,78(1):38-48.
[16]CHU X, WANG W, NI X Z, et al. Classifying maize kernels naturally infected by fungi using near-infrared hyperspectral imaging[J]. Infrared Physics & Technology, 2020,105(1):103242.
[17]CHU X, WANG W, YOON S C, et al. Detection of aflatoxin B1 (AFB1) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging[J]. Biosystems Engineering, 2017,157:13-23.
[18]LIANG K, LIU Q X, XU J H, et al. Determination and visualization of different levels of deoxynivalenol in bulk wheat kernels by hyperspectral imaging[J]. Journal of Applied Spectroscopy, 2018,85(5):953-961.
[19]DELWICHE STEPHEN R, KIM MOON S, DONG Y H. Fusarium damage assessment in wheat kernels by Vis/NIR hyperspectral imaging[J]. Sensing and Instrumentation for Food Quality and Safety, 2011,5(2):63-71.
[20]SHAHIN M A, SYMONS S J. Detection of Fusarium damaged kernels in Canada Western Red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis[J]. Computers and Electronics in Agriculture, 2011,75(1):107-112.
[21]梁琨,杜莹莹,卢伟,等. 基于高光谱成像技术的小麦籽粒赤霉病识别[J]. 农业机械学报, 2016,47(2):309-315.
[22]刘爽,谭鑫,刘成玉,等. 高光谱数据处理算法的小麦赤霉病籽粒识别[J]. 光谱学与光谱分析, 2019,39(11): 3540-3546.
[23]SHEN G H, FAN X, YANG Z L, et al. A feasibility study of non-targeted adulterant screening based on NIRM spectral library of soybean meal to guarantee quality: the example of non-protein nitrogen[J]. Food Chemistry, 2016,210:35-42.
[24]ZAREEF M, CHEN Q S, HASSAN M M, et al. An overview on the applications of typical non-linear algorithms coupled with NIR spectroscopy in food analysis[J]. Food Engineering Reviews, 2020,12(2):173-190.
[25]LI H D, LIANG Y Z, XU Q S, et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J]. Analytica Chimica Acta, 2009,648(1): 77-84.
[26]BAURIEGEL E, GIEBEL A, GEYER M, et al. Early detection of Fusarium infection in wheat using hyper-spectral imaging[J]. Computers and Electronics in Agriculture, 2011,75(2):304-312.

相似文献/References:

[1]宋镇,姬长英,张波.基于高光谱技术融合图像信息的杏鲍菇干燥过程中含水率检测[J].江苏农业学报,2019,(02):436.[doi:doi:10.3969/j.issn.1000-4440.2019.02.026]
　SONG Zhen,JI Chang-ying,ZHANG Bo.Visualized determination of moisture content in Pleurotus eryngii during drying process based on hyperspectral imaging technology[J].,2019,(02):436.[doi:doi:10.3969/j.issn.1000-4440.2019.02.026]

备注/Memo

备注/Memo:
收稿日期：2020-09-04基金项目：国家重点研发计划项目（2018YFE026000）；国家自然科学基金项目（31872914）；江苏省农业科技自主创新基金项目[CX(19) 3004]作者简介：沈广辉（1989-），男，山东泰安人，博士，助理研究员，主要从事农产品质量安全快速检测方面的研究。（E-mail）shenguanghui1989@163.com通讯作者：史建荣， （Email）shiji@ jaas.ac.cn
更新日期/Last Update: 2021-05-10