Research Output
   

Intelligent Decision Support Systems

› French Fry

› Oat Kernal
 
Sensor Sensing Systems

› Quality of Beans

› Sunflower

› Wheat Protein

› Bacterial Contamination in Packaged Food

Spectroscopic Techniques for Discriminating Confectionary Sunflower

Needs

  • Portable
  • Instrument-sample
  • Fast & Accurate
  • Cost-effective
 

Parameters

  • Good (edible)
  • Moldy
  • Rancid
 

Objectives

  • Develop a portable sensing system for quality evaluation of sunflower kernels
  • Evaluate the capability of the sensing system for discriminating good, moldy, and rancid kernel
   
 

Methodology
 

Sensing System Development

Hardware

  • NIR technology
  • Portable spectroscopy
  • 700-1050nm, 900-1700nm
  • Material handling system
  • Signal analysis and prediction software

Samples and Spectral Signals

Samples

  • 40 good sunflower confectionary
  • 40 moldy
  • 40 rancid

Spectral signals

  • 700-1050nm, PC based Ocean Optics
  • 900-1700nm, PC based CDI
 
 
   
 

Conclusion

  • Both (700-1050nm) and (900-1700nm) NIR spectroscopic techniques showed good potential
  • Neural network-based classifier showed higher classification accuracies than statistical classifiers
  • Spectral information in 900-1700 nm with NN classifier showed highest accuracies (100%) for classifying good, moldy, and rancid kernels as compared to these in 700-1050 nm
 

Other Contributions

  • Establish fatty acids profile
  • Oil content prediction
The Bioimaging and Sensing Center is a multi-disciplinary research facility in the College of Agriculture, Food Systems and Natural Resources at North Dakota State University, Fargo ND.
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