Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

Zhao, Xin; Wang, Wei; Chu, Xuan; Li, Chunyang; Kimuli, Daniel

doi:10.3390/app7010090

Cited by 27 publications

(14 citation statements)

References 40 publications

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“…This study agrees with the findings of others that NIRS is useful for the separation of crops contaminated by aflatoxigenic strains of Aspergillus [33,43]. The separation of the milk stage from the dough and dent stage kernels observed could be attributed to the difference in path length within the grain; this is associated with presentation effects due to its smaller size [44] and difference in chemical compositional properties [38,45].…”

Section: Discussionsupporting

confidence: 91%

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Near Infrared Spectrometry for Rapid Non-Invasive Modelling of Aspergillus-Contaminated Maturing Kernels of Maize (Zea mays L.)

et al. 2017

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Aflatoxin-producing Aspergillus spp. produce carcinogenic metabolites that contaminate maize. Maize kernel absorbance patterns of near infrared (NIR) wavelengths (800-2600 nm) were used to non-invasively identify kernels of milk-, dough-and dent-stage maturities with four doses of Aspergillus sp. contamination. Near infrared spectrometry (NIRS) spectral data was pre-processed using first derivative Savitzky-Golay (1d-SG) transformation and multiplicative scatter correction on spectral data. Contaminated kernels had higher absorbance between 800-1134 nm, while uninoculated samples had higher absorbance above 1400 nm. Dose and maturity clusters seen in Principal Component Analysis (PCA) score plots were due to bond stretches of combination bands, CH and C=O functional groups within grain macromolecules. The regression model at 2198 nm separated uninoculated and inoculated kernels (p < 0.0001, R 2 = 0.88, root mean square error = 0.15). Non-invasive identification of Aspergillus-contaminated maize kernels using NIR spectrometry was demonstrated in kernels of different maturities.

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Section: Discussionsupporting

confidence: 91%

“…This suggests important changes in this region of the spectral data ( Figure 4). These wavelength regions, as reported by others in aflatoxin contamination [26,43], are associated with changes in the macromolecules of the maize grains including starch and proteins and the fungal structures [51].…”

Section: Discussionsupporting

confidence: 65%

Near Infrared Spectrometry for Rapid Non-Invasive Modelling of Aspergillus-Contaminated Maturing Kernels of Maize (Zea mays L.)

et al. 2017

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“…Prediction images were created to mark fungi with different incubation times in different colors. The prediction images were obtained by applying the optimal wavelengths SVM model to every pixel of the fungal hyperspectral image, respectively [17]. The prediction images of A. flavus and A. parasiticus were shown in Figure 9a,b, respectively.…”

Section: Optimal Wavelengths Svm Modelsmentioning

confidence: 99%

“…Zhao et al detected the contamination level of maize kernels inoculated with A. parasiticus by using hyperspectral images with a spectral range of 1000-2500 nm. The studies showed that hyperspectral imaging has become a promising non-destructive technique for detecting the fungal contamination levels and fungal species on cereals [17].…”

Section: Introductionmentioning

confidence: 99%

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Chu

Wang

et al. 2018

Applied Sciences

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Visible/near-infrared (Vis/NIR) hyperspectral imaging (400-1000 nm) was applied to identify the growth process of Aspergillus flavus and Aspergillus parasiticus. The hyperspectral images of the two fungi that were growing on rose bengal medium were recorded daily for 6 days. A band ratio using two bands at 446 nm and 460 nm separated A. flavus and A. parasiticus on day 1 from other days. Image at band of 520 nm classified A. parasiticus on day 6. Principle component analysis (PCA) was performed on the cleaned hyperspectral images. The score plot of the second to sixth principal components (PC 2 to PC 6 ) gave a rough clustering of fungi in the same incubation time. However, in the plot, A. flavus on day 3 and day 4 and A. parasiticus on day 2 and day 3 overlapped. The average spectra of each fungus in each growth day were extracted, then PCA and support vector machine (SVM) classifier were applied to the full spectral range. SVM models built by PC 2 to PC 6 could identify fungal growth days with accuracies of 92.59% and 100% for A. flavus and A. parasiticus individually. In order to simplify the prediction models, competitive adaptive reweighted sampling (CARS) was employed to choose optimal wavelengths. As a result, nine (402, 442, 487, 502, 524, 553, 646, 671, 760 nm) and seven (461, 538, 542, 742, 753, 756, 919 nm) wavelengths were selected for A. flavus and A. parasiticus, respectively. New optimal wavelengths SVM models were built, and the identification accuracies were 83.33% and 98.15% for A. flavus and A. parasiticus, respectively. Finally, the visualized prediction images for A. flavus and A. parasiticus in different growth days were made by applying the optimal wavelength's SVM models on every pixel of the hyperspectral image.

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“…The training samples are used to train the DBN and the SOM; the fine tuning samples are a subset of training samples that have been manually labeled [26,27,32]. Fine tuning is used to learn the category data which is already known from the training process.…”

Section: Network Trainingmentioning

confidence: 99%

The Deep Belief and Self-Organizing Neural Network as a Semi-Supervised Classification Method for Hyperspectral Data

Lan

et al. 2017

Applied Sciences

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Hyperspectral data is not linearly separable, and it has a high characteristic dimension. This paper proposes a new algorithm that combines a deep belief network based on the Boltzmann machine with a self-organizing neural network. The primary features of the hyperspectral image are extracted with a deep belief network. The weights of the network are fine-tuned using the labeled sample. Feature vectors extracted by the deep belief network are classified by a self-organizing neural network. The method reduces the spectral dimension of the data while preserving the large amount of original information in the data. The method overcomes the long training time required when using self-organizing neural networks for clustering, as well as the training difficulties of Deep Belief Networks (DBN) when the labeled sample size is small, thereby improving the accuracy and robustness of the semi-supervised classification. Simulation results show that the structure of the network can achieve higher classification accuracy when the labeled sample is deficient.

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Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

Cited by 27 publications

References 40 publications

Near Infrared Spectrometry for Rapid Non-Invasive Modelling of Aspergillus-Contaminated Maturing Kernels of Maize (Zea mays L.)

Near Infrared Spectrometry for Rapid Non-Invasive Modelling of Aspergillus-Contaminated Maturing Kernels of Maize (Zea mays L.)

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

The Deep Belief and Self-Organizing Neural Network as a Semi-Supervised Classification Method for Hyperspectral Data

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