• 安全检测 •

### 基于光谱和图像信息融合的玉米霉变程度在线检测

1. 1.南京财经大学食品科学与工程学院，江苏省现代粮食流通与安全协同创新中心，江苏 南京 210023；2.南京灵山粮食储备库有限公司，江苏 南京 211599；3.浙江农林大学农业与食品科学学院，浙江 杭州 311300
• 出版日期:2019-08-25 发布日期:2019-08-26
• 基金资助:
“十三五”国家重点研发计划重点专项（2017YFC1600601）；国家自然科学基金面上项目（31772061）；浙江省重点研发计划项目（2018C02050）；国家粮油作物产品质量安全风险评估专项（GJFP2018001）；江苏高校优势学科建设工程资助项目（2014-124）；江苏省农业科技自主创新资金项目（CX(19)2005）

### On-Line Detection of Mildew Degree of Maize Based on Spectral and Image Information Fusion

SHEN Fei, HUANG Yi, ZHOU Yuechun, LIU Qin, PEI Fei, LI Peng, FANG Yong, LIU Xingquan

1. 1. Jiangsu Collaborative Innovation Center for Modern Grain Circulation and Safety, College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; 2. Nanjing Lingshan Grain Reserve Co. Ltd., Nanjing 211599, China; 3. College of Agriculture and Food Science, Zhejiang A&F University, Hangzhou 311300, China
• Online:2019-08-25 Published:2019-08-26

Abstract: This work aimed to establish an on-line method for the detection of the mildew degree of maize based on data fusion of visible/near infrared (Vis/NIR) spectroscopy and machine vision technique. Irradiation-sterilized maize kernel samples were inoculated with spore suspensions of 5 different fungal strains, separately. Then the samples were stored in an artificial climate box (28 ℃ and 85% relative humidity) for 15 d until serious mildew occurred. After 0, 6, 9, 12 and 15 d, the characteristic spectral and image information of maize samples were collected online. Both spectral features and image color features were extracted and fused for the development pf qualitative and quantitative analysis models. The results showed that principal component analysis (PCA) could distinguish maize samples according to different mildew degrees. The overall recognition rate obtained by linear discriminant analysis (LDA) for the classification of samples with different mildew degrees was 91.1% based on spectral and image data infusion, which were 4.4% and 8.9% higher than single spectral and image information, respectively. The prediction of colony counts in maize samples using the partial least squares regression (PLSR) based on information fusion was also better and the coefficient of determination for the prediction set (Rp 2), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value obtained were 0.894 1, 0.665 (lg(CFU/g)) and 3.06, respectively. These results indicated that spectral and image data fusion could improve the model precision and was feasible in on-line detection of fungal contamination levels in maize. In further studies, naturally infected maize samples and samples contaminated with more representative fungal strains should be considered to enhance the robustness and applicability of the calibration model.