Determination of physical contaminants in wheat using hyperspectral imaging
| dc.contributor.author | Lankapalli, Ravikanth | |
| dc.contributor.examiningcommittee | White, Noel (Biosystems Engineering) Singh, Chandra (Biosystems Engineering) Fields, Paul (Entomology) Mallikarjunan, Kumar (Biological Systems Engineering,Virginia Tech) | en_US |
| dc.contributor.supervisor | Jayas, Digvir (Biosystems Engineering) | en_US |
| dc.date.accessioned | 2015-10-09T13:22:47Z | |
| dc.date.available | 2015-10-09T13:22:47Z | |
| dc.date.issued | 2015-04-22 | en_US |
| dc.degree.discipline | Biosystems Engineering | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | Cereal grains are an important part of human diet; hence, there is a need to maintain high quality and these grains must be free of physical and biological contaminants. A procedure was developed to differentiate physical contaminants from wheat using NIR (1000-1600 nm) hyperspectral imaging. Three experiments were conducted to select the best combinations of spectral pre-processing technique and statistical classifier to classify physical contaminants: seven foreign material types (barley, canola, maize, flaxseed, oats, rye, and soybean); six dockage types (broken wheat kernels, buckwheat, chaff, wheat spikelets, stones, and wild oats); and two animal excreta types (deer and rabbit droppings) from Canada Western Red Spring (CWRS) wheat. These spectra were processed using five spectral pre-processing techniques (first derivative, second derivative, Savitzky-Golay (SG) smoothing and differentiation, multiplicative scatter correction (MSC), and standard normal variate (SNV)). The raw and pre-processed data were classified using Support Vector Machines (SVM), Naïve Bayes (NB), and k-nearest neighbors (k-NN) classifiers. In each experiment, two-way and multi-way classifications were conducted. Among all the contaminant types, stones, chaff, deer droppings and rabbit droppings were classified with 100% accuracy using the raw reflectance spectra and different statistical classifiers. The SNV technique with k-NN classifier gave the highest accuracy for the classification of foreign material types from wheat (98.3±0.2%) and dockage types from wheat (98.9±0.2%). The MSC and SNV techniques with SVM or k-NN classifier gave perfect classification (100.0±0.0%) for the classification of animal excreta types from wheat. Hence, the SNV technique with k-NN classifier was selected as the best model. Two separate model performance evaluation experiments were conducted to identify and quantify (by number) the amount of contaminant type present in wheat. The overall identification accuracy of the first degree of contamination (one contaminant type with wheat) and the highest degree of contamination (all the contaminant type with wheat) was 97.6±1.6% and 92.5±6.5%, for foreign material types; 98.0±1.8% and 94.3±6.2%r for dockage types; and 100.0±0.0% and 100.0±0.0%, respectively for animal excreta types. The canola, stones, deer, and rabbit droppings were perfectly quantified (100.0±0.0%) at all the levels of contaminations. | en_US |
| dc.description.note | February 2016 | en_US |
| dc.identifier.citation | Ravikanth L., Singh C. B., Jayas D. S., & White N. D. G. (2015). Classification of contaminants from wheat using near-infrared hyperspectral imaging. Biosystems Engineering, 135, 73–86. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/30902 | |
| dc.language.iso | eng | en_US |
| dc.publisher | Biosystems Engineering | en_US |
| dc.rights | open access | en_US |
| dc.subject | Foreign material, Dockage, Animal excreta, Near-infrared hyperspectral imaging, Spectral pre-processing, Statistical classification | en_US |
| dc.title | Determination of physical contaminants in wheat using hyperspectral imaging | en_US |
| dc.type | doctoral thesis | en_US |