Rapid screening for Aflatoxin B1 in Single Maize Kernels Using Vis/NIR Hyperspectral Imaging

2015 ◽  
Keyword(s):  
Hyperspectral Imaging ◽  
Aflatoxin B1 ◽  
Rapid Screening ◽  
Maize Kernels

scholarly journals Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis–NIR (400–1000 nm) hyperspectral imaging

2020 ◽  
Author(s):  
Subir Kumar Chakraborty ◽  
Naveen Kumar Mahanti ◽  
Shekh Mukhtar Mansuri ◽  
Manoj Kumar Tripathi ◽  
Nachiket Kotwaliwale ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Aflatoxin B1 ◽  
Maize Kernels ◽  
Non Destructive

<i>A SWIR hyperspectral imaging method for classifying Aflatoxin B1 contaminated maize kernels</i>

2017 ◽  
Author(s):  
Daniel Kimuli ◽  
Kurt Lawrence ◽  
Seung-Chul Yoon ◽  
Wei Wang ◽  
Gerald Heitschmidt ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Aflatoxin B1 ◽  
Imaging Method ◽  
Maize Kernels

scholarly journals Detection of aflatoxin B1 (AFB1) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging

2017 ◽  
Vol 157 ◽  
pp. 13-23 ◽  
Author(s):  
Xuan Chu ◽  
Wei Wang ◽  
Seung-Chul Yoon ◽  
Xinzhi Ni ◽  
Gerald W. Heitschmidt
Keyword(s):  
Hyperspectral Imaging ◽  
Aflatoxin B1 ◽  
Short Wave ◽  
Short Wave Infrared ◽  
Maize Kernels

Feasibility of detecting Aflatoxin B1 in single maize kernels using hyperspectral imaging

2015 ◽  
Vol 166 ◽  
pp. 182-192 ◽  
Author(s):  
Wei Wang ◽  
Xinzhi Ni ◽  
Kurt C. Lawrence ◽  
Seung-Chul Yoon ◽  
Gerald W. Heitschmidt ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Aflatoxin B1 ◽  
Maize Kernels

Near-infrared hyperspectral imaging for detecting Aflatoxin B1 of maize kernels

Food Control ◽  
2015 ◽  
Vol 51 ◽  
pp. 347-355 ◽  
Author(s):  
Wei Wang ◽  
Kurt C. Lawrence ◽  
Xinzhi Ni ◽  
Seung-Chul Yoon ◽  
Gerald W. Heitschmidt ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Aflatoxin B1 ◽  
Near Infrared ◽  
Maize Kernels

Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels

2018 ◽  
Vol 89 ◽  
pp. 351-362 ◽  
Author(s):  
Daniel Kimuli ◽  
Wei Wang ◽  
Wei Wang ◽  
Hongzhe Jiang ◽  
Xin Zhao ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Aflatoxin B1 ◽  
Maize Kernels

scholarly journals Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3078 ◽  
Author(s):  
Lei Feng ◽  
Susu Zhu ◽  
Chu Zhang ◽  
Yidan Bao ◽  
Xuping Feng ◽  
...  
Keyword(s):  
Aging Time ◽  
Hyperspectral Imaging ◽  
Accelerated Aging ◽  
Principal Component ◽  
Seed Vigor ◽  
Support Vector ◽  
Classification Models ◽  
Maize Kernel ◽  
Seed Aging ◽  
Maize Kernels

Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874–1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine−SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.

Sign in / Sign up

Export Citation Format

Share Document