Analysis of Pregerminated Barley Using Hyperspectral Image Analysis

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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Analysis of Pregerminated Barley Using Hyperspectral Image Analysis. / Arngren, Morten; Hansen, Per Waaben; Eriksen, Birger; Larsen, Jan; Larsen, Rasmus.

In: Journal of Agricultural and Food Chemistry, Vol. 59, No. 21, 2011, p. 11385-11394.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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Arngren, Morten; Hansen, Per Waaben; Eriksen, Birger; Larsen, Jan; Larsen, Rasmus / Analysis of Pregerminated Barley Using Hyperspectral Image Analysis.

In: Journal of Agricultural and Food Chemistry, Vol. 59, No. 21, 2011, p. 11385-11394.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

Bibtex

@article{3516ae6e75b147b6bf556079922d0a8f,
title = "Analysis of Pregerminated Barley Using Hyperspectral Image Analysis",
publisher = "American Chemical Society",
author = "Morten Arngren and Hansen, {Per Waaben} and Birger Eriksen and Jan Larsen and Rasmus Larsen",
year = "2011",
doi = "10.1021/jf202122y",
volume = "59",
number = "21",
pages = "11385--11394",
journal = "Journal of Agricultural and Food Chemistry",
issn = "0021-8561",

}

RIS

TY - JOUR

T1 - Analysis of Pregerminated Barley Using Hyperspectral Image Analysis

A1 - Arngren,Morten

A1 - Hansen,Per Waaben

A1 - Eriksen,Birger

A1 - Larsen,Jan

A1 - Larsen,Rasmus

AU - Arngren,Morten

AU - Hansen,Per Waaben

AU - Eriksen,Birger

AU - Larsen,Jan

AU - Larsen,Rasmus

PB - American Chemical Society

PY - 2011

Y1 - 2011

N2 - Pregermination is one of many serious degradations to barley when used for malting. A pregerminated barley kernel can under certain conditions not regerminate and is reduced to animal feed of lower quality. Identifying pregermination at an early stage is therefore essential in order to segregate the barley kernels into low or high quality. Current standard methods to quantify pregerminated barley include visual approaches, e.g. to identify the root sprout, or using an embryo staining method, which use a time-consuming procedure. We present an approach using a near-infrared (NIR) hyperspectral imaging system in a mathematical modeling framework to identify pregerminated barley at an early stage of approximately 12 h of pregermination. Our model only assigns pregermination as the cause for a single kernel’s lack of germination and is unable to identify dormancy, kernel damage etc. The analysis is based on more than 750 Rosalina barley kernels being pregerminated at 8 different durations between 0 and 60 h based on the BRF method. Regerminating the kernels reveals a grouping of the pregerminated kernels into three categories: normal, delayed and limited germination. Our model employs a supervised classification framework based on a set of extracted features insensitive to the kernel orientation. An out-of-sample classification error of 32% (CI95%: 29–35%) is obtained for single kernels when grouped into the three categories, and an error of 3% (CI95%: 0–15%) is achieved on a bulk kernel level. The model provides class probabilities for each kernel, which can assist in achieving homogeneous germination profiles. This research can further be developed to establish an automated and faster procedure as an alternative to the standard procedures for pregerminated barley.

AB - Pregermination is one of many serious degradations to barley when used for malting. A pregerminated barley kernel can under certain conditions not regerminate and is reduced to animal feed of lower quality. Identifying pregermination at an early stage is therefore essential in order to segregate the barley kernels into low or high quality. Current standard methods to quantify pregerminated barley include visual approaches, e.g. to identify the root sprout, or using an embryo staining method, which use a time-consuming procedure. We present an approach using a near-infrared (NIR) hyperspectral imaging system in a mathematical modeling framework to identify pregerminated barley at an early stage of approximately 12 h of pregermination. Our model only assigns pregermination as the cause for a single kernel’s lack of germination and is unable to identify dormancy, kernel damage etc. The analysis is based on more than 750 Rosalina barley kernels being pregerminated at 8 different durations between 0 and 60 h based on the BRF method. Regerminating the kernels reveals a grouping of the pregerminated kernels into three categories: normal, delayed and limited germination. Our model employs a supervised classification framework based on a set of extracted features insensitive to the kernel orientation. An out-of-sample classification error of 32% (CI95%: 29–35%) is obtained for single kernels when grouped into the three categories, and an error of 3% (CI95%: 0–15%) is achieved on a bulk kernel level. The model provides class probabilities for each kernel, which can assist in achieving homogeneous germination profiles. This research can further be developed to establish an automated and faster procedure as an alternative to the standard procedures for pregerminated barley.

U2 - 10.1021/jf202122y

DO - 10.1021/jf202122y

JO - Journal of Agricultural and Food Chemistry

JF - Journal of Agricultural and Food Chemistry

SN - 0021-8561

IS - 21

VL - 59

SP - 11385

EP - 11394

ER -