Support Vector Machine Classification of
Fluorescence Hyperspectral Image for
Detection of Aflatoxin in Corn Kernels
Sathi...
Outline
• Introduction
• Current Screening Methods
• Proposed System
• Experimental Results
• Conclusions
Aflatoxin in Corn
• Aflatoxin – produced by Aspergillus
Flavus and Aspergillus Parasiticus
• Invades grain crops that are ...
Introduction
•Grains with high concentrations of Aflatoxin are toxic to
humans and domestic animals when ingested in feed....
Existing Approaches
• Blacklight presumptive & thin layer chromatography
• Mini column test
• Field and laboratory rapid t...
Drawbacks with Existing Approaches
• Lack of quantitative ability
• Time consuming
• Expensive
• Require intensive interpr...
Proposed Approach
• Detection of toxin and classification of toxin levels via
hyperspectral imaging
Corn Kernels
in Bins
C...
Experimental Test Site
• Field plots were infected with
toxigenic AF13
• Corn ears were hand harvested for
imaging and lab...
Hyperspectral Imaging
•Kernels adjacent to the inoculation site were extracted
• Only whole, undamaged kernels were extrac...
Chemical Analysis
for Reference Information
•After imaging, each corn kernel was
crushed and weighed
•Each sample was extr...
Image Processing
•Kernels were imaged under a UV
light source using VNIR100E
hyperspectral sensor (ITD) – 365nm
•Kernels s...
Spectrum Partitioning
NU-RFS Multi-Classifier
Data
D -dimensional
KernelDensity Fusion
NU-RFS NU-RFS NU-RFS
SVM
Classifier 1
Estimate
Density
Co...
Results – Single SVM Classifier
Class1 Class2 Class3 Class4 Producer Acc
Class1 983 8689 181 220 9.8
Class2 151 19769 273 ...
Results - Uniform RFS Multi Classifier
Class1 Class2 Class3 Class4
Producer
Acc
Class1 2376 5020 2149 528 23.6
Class2 495 ...
Results - Non-Uniform RFS Multi Classifier
Class1 Class2 Class3 Class4
Producer
Acc
Class1 5287 3136 1384 266 52.5
Class2 ...
Support Vector Machines Classification of Fluorescence Hyperspectral Image for Detection of Aflatoxin in Corn Kernels
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Support Vector Machines Classification of Fluorescence Hyperspectral Image for Detection of Aflatoxin in Corn Kernels

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Aflatoxin contamination is a real concern for all classes of livestock. They are produced by certain mold fungi, Aspergillus flavus and Aspergillus parasiticus. Aflatoxin in food is hazardous for humans and animals. In this work, we propose a non-invasive system for detecting aflatoxin and classifying corn kernels based on the aflatoxin contamination levels. Fluorescence hyperspectral images of single corn kernels were used for experiments. Single and multi-classifier configurations of support vector machines are used to classify single corn kernels on a per-pixel basis. The performance of SVM classification with and without feature selection is assessed. Confusion matrices of different configurations are used for comparison, demonstrating that the multi-classifier system with non-uniform feature selection performs well, achieving an overall accuracy of 84%.

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  • Test feasibility of HSI to detect and determine level of Aflatoxin contamination of corn
  • ELISA kit takes 20 minutes for test to run.
  • Support Vector Machines Classification of Fluorescence Hyperspectral Image for Detection of Aflatoxin in Corn Kernels

    1. 1. Support Vector Machine Classification of Fluorescence Hyperspectral Image for Detection of Aflatoxin in Corn Kernels Sathishkumar Samiappan, Lori Mann Bruce, Haibo Yao, Zuzana Hruska, Robert L. Brown*, Deepak Bhatnagar*, and Thomas E. Cleveland* Mississippi State University, MS, USA *USDA-ARS, Southern Regional Research Center, New Orleans, LA, USA bruce@engr.msstate.edu June 2013
    2. 2. Outline • Introduction • Current Screening Methods • Proposed System • Experimental Results • Conclusions
    3. 3. Aflatoxin in Corn • Aflatoxin – produced by Aspergillus Flavus and Aspergillus Parasiticus • Invades grain crops that are stressed by heat and drought • In US, FDA regulates alfatoxin levels up to 20ppb in food and 100 ppb in feed. • Contamination of food causes financial loss to farmers due to rejection and disposal of grain. [farmprogress.com]
    4. 4. Introduction •Grains with high concentrations of Aflatoxin are toxic to humans and domestic animals when ingested in feed. •Carcinogen associated with liver and lung cancer in humans •Need a rapid, non-invasive screening method for Aflatoxin in food and feed crops [farmprogress.com]
    5. 5. Existing Approaches • Blacklight presumptive & thin layer chromatography • Mini column test • Field and laboratory rapid test kits • Enzyme linked immuno assay (ELISA) kits • High performance liquid chromatography • Mass spectrometry with HPLC ELISA Plate Kit by Beacon Kits, Inc.
    6. 6. Drawbacks with Existing Approaches • Lack of quantitative ability • Time consuming • Expensive • Require intensive interpretation • Invasive in nature • Require destruction of samples Technician Cesar Ambrogio sets up the kernel screening assay to measure amount of Aflatoxin. USDA-ARS [USDA.gov]
    7. 7. Proposed Approach • Detection of toxin and classification of toxin levels via hyperspectral imaging Corn Kernels in Bins Conveyer Belt UV Excitation & Hyperspectral Imager < 0.1 ppb 20 < ppb < 0.1 100 < ppb < 20 > 100 ppb Hyperspectral Image 0 20 40 60 80 100 120 140 160 180 200 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
    8. 8. Experimental Test Site • Field plots were infected with toxigenic AF13 • Corn ears were hand harvested for imaging and lab analysis
    9. 9. Hyperspectral Imaging •Kernels adjacent to the inoculation site were extracted • Only whole, undamaged kernels were extracted and imaged • Several, control kernels from the same ear away from the inoculation site were also extracted 365nm Spectral Band
    10. 10. Chemical Analysis for Reference Information •After imaging, each corn kernel was crushed and weighed •Each sample was extracted with methanol/water (80/20%) •Extracted samples were diluted and passed through Aflatest affinity columns •Samples were eluted with pure methanol and measured with a fluorometer (VICAM)
    11. 11. Image Processing •Kernels were imaged under a UV light source using VNIR100E hyperspectral sensor (ITD) – 365nm •Kernels segmented from background •Each kernel was assigned a unique signature
    12. 12. Spectrum Partitioning
    13. 13. NU-RFS Multi-Classifier Data D -dimensional KernelDensity Fusion NU-RFS NU-RFS NU-RFS SVM Classifier 1 Estimate Density Compute Class Score SVM Classifier 2 Estimate Density Compute Class Score SVM Classifier z Estimate Density Compute Class Score …. Final Prediction
    14. 14. Results – Single SVM Classifier Class1 Class2 Class3 Class4 Producer Acc Class1 983 8689 181 220 9.8 Class2 151 19769 273 659 94.8 Class3 132 7856 2025 682 18.9 Class4 2 604 37 21676 97.1 User Acc 77.5 53.6 80.5 93.3 69.5
    15. 15. Results - Uniform RFS Multi Classifier Class1 Class2 Class3 Class4 Producer Acc Class1 2376 5020 2149 528 23.6 Class2 495 17387 2226 744 83.4 Class3 550 3998 4369 1778 40.9 Class4 48 723 175 21373 95.8 User Acc 68.5 64.1 49.0 87.5 71.2
    16. 16. Results - Non-Uniform RFS Multi Classifier Class1 Class2 Class3 Class4 Producer Acc Class1 5287 3136 1384 266 52.5 Class2 2087 16172 2201 392 77.6 Class3 1660 2413 5764 858 53.9 Class4 348 609 536 20826 93.3 User Acc 56.4 72.4 58.3 93.1 75.1

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