کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6540128 | 158852 | 2016 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Comparison of UV and visible autofluorescence of wheat grain tissues in macroscopic images of cross-sections and particles
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Comparison of UV and visible autofluorescence of wheat grain tissues in macroscopic images of cross-sections and particles Comparison of UV and visible autofluorescence of wheat grain tissues in macroscopic images of cross-sections and particles](/preview/png/6540128.png)
چکیده انگلیسی
Many plant tissues can be observed thanks to the autofluorescence of their cell wall components. Multispectral autofluorescence imaging at the macroscopic scale is a rapid efficient way of observing samples with a large field of view (>1 cm2) and a nice resolution (<3 μm per pixel). The objective of this work was to evaluate autofluorescence multispectral imaging at the macroscopic scale to identify the tissular origin of particles through their autofluorescence profiles. The pericarp and aleurone layer of wheat grain were used to compare contrasted autofluorescence profiles measured in cross sections and particles. Air and water mounting media were tested. Principal component analysis and variance analysis showed that the autofluorescence properties were retained from cross sections to particles. The mounting media modified the autofluorescence profiles and water was found to be better to observe both particles and sections. These results show that multispectral autofluorescence imaging at the macroscopic scale can be used to identify the histological origin of particles.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Electronics in Agriculture - Volume 127, September 2016, Pages 281-288
Journal: Computers and Electronics in Agriculture - Volume 127, September 2016, Pages 281-288
نویسندگان
Mathias Corcel, Marie-Françoise Devaux, Fabienne Guillon, Cécile Barron,