Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging |
| |
| Institution: | 1. Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada;2. Agriculture and Agri-Food Canada, c/o Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada;3. Canadian Grain Commission, c/o Richardson Centre for Functional Foods and Nutraceuticals, University of Manitoba, Winnipeg, MB R3T 6C5, Canada;1. Embrapa Agricultural Informatics, Campinas, SP, Brazil;2. Embrapa Wheat, Passo Fundo, RS, Brazil;1. College of Engineering, China Agricultural University, No. 17 Tsinghua East Road, Beijing 100083, China;2. Quality & Safety Assessment Research Unit, Richard B. Russell Research Center, USDA-ARS, 950 College Station Road, Athens, GA 30605, USA;3. Crop Genetics and Breeding Research Unit, USDA-ARS, 2747 Davis Road, Tifton, GA 31793, USA;1. Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada R3T 5V6;2. Agriculture and Agri-Food Canada, c/o Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada R3T 5V6;3. Canadian Grain Commission, c/o Richardson Centre for Functional Foods and Nutraceuticals, University of Manitoba, Winnipeg, MB, Canada R3T 2E1;1. INRA, UR1268 Biopolymères Interactions Assemblages, F-44316 Nantes, France;2. data_frame, F-44300 Nantes, France;3. NRA-ONIRIS Unité Statistiques, Sensométrie, Chimiométrie (Stat SC), F-44322 Nantes, France |
| |
| Abstract: | Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns. |
| |
| Keywords: | Near-infrared hyperspectral imaging Principal component analysis OTA Classification models |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
