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RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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Analysis of pregerminated barley using hyperspectral image analysis.

Morten Arngren¹, Per Waaben Hansen, Birger Eriksen

¹DTU Informatics, Technical University of Denmark, Richard Petersens Plads Bldg 321, DK-2800 Kgs. Lyngby, Denmark. ma@imm.dtu.dk

Journal of Agricultural and Food Chemistry

|September 22, 2011

Summary

Near-infrared hyperspectral imaging can rapidly detect early pregermination in barley, a key quality degradation. This technology offers a faster alternative to traditional methods for sorting malting barley by quality.

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Metabolomic Analysis of Barley by Gas Chromatography/Mass Spectrometry

Metabolomic Analysis of Barley by Gas Chromatography/Mass Spectrometry

Published on: November 8, 2024

Area of Science:

Agricultural Science
Food Science
Spectroscopy

Background:

Pregermination significantly degrades barley quality for malting, reducing its value to animal feed.
Early identification of pregerminated barley is crucial for quality segregation.
Current methods like visual inspection and embryo staining are time-consuming.

Purpose of the Study:

To develop a rapid method for identifying early pregermination in barley kernels.
To utilize near-infrared (NIR) hyperspectral imaging and mathematical modeling for this purpose.
To distinguish between normal, delayed, and limited germination in barley.

Main Methods:

Utilized a near-infrared (NIR) hyperspectral imaging system.
Employed a mathematical modeling framework with supervised classification.
Analyzed over 750 Rosalina barley kernels pregerminated at various durations (0-60 hours).

Main Results:

Achieved an out-of-sample classification error of 32% for single kernels and 3% for bulk kernels.
The model effectively categorizes kernels into normal, delayed, and limited germination groups.
Identified early pregermination at approximately 12 hours.

Conclusions:

NIR hyperspectral imaging offers a promising, faster alternative to traditional methods for detecting pregerminated barley.
The developed model can assist in achieving homogeneous germination profiles by providing class probabilities.
Further development could lead to an automated system for barley quality assessment.