Smart Agricultural Technology | |
Assessment of mechanical damage to flaxseeds using radiographic imaging and tomography | |
Jitendra Paliwal1  Mohammad Nadimi2  Georgia Loewen2  | |
[1] Corresponding author.;Department of Biosystems Engineering, University of Manitoba, Winnipeg MB R3T 5V6, Canada; | |
关键词: Flaxseed; Mechanical damage; Processing; Impact energy; | |
DOI : | |
来源: DOAJ |
【 摘 要 】
The emergence of flax as a nutritional superfood has brought to the forefront the challenges its producers face in the crop's post-harvest quality preservation. Mechanical damage during harvesting, handling, and transportation can severely impair flaxseed quality causing economic losses to producers and processors. Therefore, this study explored the effect of moisture content and impact stress on the breakage susceptibility of this important oilseed. Impact energies (IEs) of 0 mJ, 2 mJ, 4 mJ, and 6 mJ were imparted to flaxseed samples at moisture contents (MCs) of 6%, 8%, and 11.5% and internal as well as external damage to seeds was assessed using 2D X-ray imaging. Furthermore, 3D X-ray tomography was deployed to characterize the nature of damage to the internal seed tissue. Our results indicate that increasing the IE and decreasing MC (specifically under low IE) leads to higher breakage susceptibility in flaxseeds. Furthermore, a regression analysis of the external and internal damage parameters indicated that while external and internal damage to seeds is closely correlated at high IE (6 mJ), the same relationship doesn't always hold at medium to low impact (IE ≤ 4 mJ). To evaluate the extent of internal damage, the gray levels distribution of seeds’ 2D X-ray images were compared, and percentile scores were determined as a promising candidate. Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers were employed to classify seeds into two broad groups of nil/low and medium/high damage using the percentile scores. The SVM and LDA classifiers achieved 87.2% and 79.6% classification accuracies, respectively. The developed model confirms that radiographic imaging has the potential to detect mechanical damages of seeds in a rapid, automated, and non-destructive fashion.
【 授权许可】
Unknown