In this research, suitable imaging methods were used for acquiring single compound images of biology samples of chicken pectorales tissue section, tobacco dry leaf, fresh leaf and plant glandular hair, respectively. The adverse effects caused by the high water content and the thermal effect of near infrared (NIR) light were effectively solved during the experiment procedures and the data processing. PCA algorithm was applied to the NIR micro-image of chicken pectorales tissue. Comparing the loading vector of PC3 with the NIR spectrum of dry albumen, the information of PC3 was confirmed to be provided mainly by protein, i.e., the 3rd score image represents the distribution trend of protein mainly. PCA algorithm was applied to the NIR micro-image of tobacco dry leaf. The information of PC2 was confirmed to be provided by carbohydrate including starch mainly. Compared to the 2nd score image of tobacco dry leaf, the compared correlation image with the reference spectrum of starch had the same distribution trend as the 2nd score image. The comparative correlation images with the reference spectra of protein, glucose, fructose and the total plant alkaloid were acquired to confirm the distribution trend of these compounds in tobacco dry leaf respectively. Comparative correlation images of fresh leaf with the reference spectra of protein, starch, fructose, glucose and water were acquired to confirm the distribution trend of these compounds in fresh leaf. Chemimap imaging of plant glandular hair was acquired to show the tubular structure clearly.
Sparse Decomposition Technique for Segmentation and Compression of Compound Images
