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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

作者： Zhao, Xin 1 ; Wang, Wei 1 ; Chu, Xuan 1 ; Li, Chunyang 2 ; Kimuli, Daniel 1 ;

作者机构： 1.China Agr Univ, Coll Engn, Beijing 100083, Peoples R China

2.Jiangsu Acad Agr Sci, Inst Food Sci & Technol, Nanjing 210014, Jiangsu, Peoples R China

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.

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