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18 May 2020 Raman imaging for detection of corn kernels infected with Aspergillus flavus: a preliminary study
Feifei Tao, Haibo Yao, Zuzana Hruska, Kanniah Rajasekaran, Jianwei Qin, Moon Kim
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Proceedings Volume 11421, Sensing for Agriculture and Food Quality and Safety XII; 1142107 (2020) https://doi.org/10.1117/12.2558859
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
Abstract
The potential of line-scan hyperspectral Raman imaging system equipped with a 785 nm line laser was examined for discrimination of healthy, AF36-inoculated and AF13-inoculated corn kernels in this study. The AF36 and AF13 strains were used as representatives for the aflatoxigenic and non-aflatoxigenic A. flavus fungal varieties. A total of 300 kernels were used with 3 treatments, namely, 100 kernels inoculated with the AF13 fungus, 100 kernels inoculated with the AF36 fungus, and 100 kernels inoculated with sterile distilled water as control. The kernels were all incubated at 30 °C for 8 days and then dried and surface wiped to remove exterior signs of mold. The kernels were imaged from endosperm side over the wavenumber range of 103-2831 cm-1. The mean spectrum was extracted from the Raman image of each kernel, and preprocessed with adaptive iteratively reweighted penalized least squares, Savitzky-Golay smoothing and min-max normalization. Based upon the preprocessed group mean spectra, a total of 35 local Raman peaks were identified. With the spectral variables at the identified local peak locations as inputs of discriminant models, the 3-class principal component analysis-linear discriminant analysis (PCA-LDA) models ran 20 random times, achieved a mean overall prediction accuracy of 91.13% along with a standard deviation value of 3.36%.
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Feifei Tao, Haibo Yao, Zuzana Hruska, Kanniah Rajasekaran, Jianwei Qin, and Moon Kim "Raman imaging for detection of corn kernels infected with Aspergillus flavus: a preliminary study", Proc. SPIE 11421, Sensing for Agriculture and Food Quality and Safety XII, 1142107 (18 May 2020); https://doi.org/10.1117/12.2558859
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KEYWORDS
Raman spectroscopy
Image segmentation
Line scan image sensors
Imaging systems
Agriculture
Data modeling
Hyperspectral imaging
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