Development of a method for assessing the quality of barley for brewing using hyperspectral imaging

Piotr Zapotoczny

doi:10.31648/ts.10111

Development of a method for assessing the quality of barley for brewing using hyperspectral imaging

Piotr Zapotoczny

WNT UWM w Olsztynie

DOI: https://doi.org/10.31648/ts.10111

Abstract

The paper discusses the utilization of hyperspectral imaging in the process of assessing the quality of barley grain intended for brewing purposes. A specialized research setup comprising a spectrophotometer coupled with a CCD camera was employed. During measurements, the spectral distribution of each pixel in the image was recorded within the range of 400 to 1000 nm, enabling the extraction of homogeneous areas on the grain surfaces. Subsequently, surface texture parameters were computed on the designated areas. Prior to engaging in classification analyses, variable reduction was performed utilizing: (a) Fisher's coefficient, (b) classification error coefficient along with the averaged correlation coefficient POE+ACC, and (c) mutual information coefficient MI. The research material consisted of grain categorized into rain-soaked (B), mold -infested (M), and healthy (H). The best classification results were obtained for a wavelength of 800 nm from the extracted homogeneous areas. The classification accuracy reached 100% across all quality groups.

Keywords:

vision systems, grain infestation, Fusarium, quality control, hyperspectral image

References

BAURIEGEL E., GIEBEL A., GEYER M., SCHMIDT U., HERPPICH W. 2011. Early detection of Fusarium infection in wheat using hyper-spectral imaging. Computer and Electronics in Agriculture, 75(2): 304-312. https://doi.org/10.1016/j.compag.2010.12.006
Crossref Google Scholar

COGDILL R.P., HURBURGH C.R., RIPPKE G.R., BAJIC S.J.R., JONES W., MCCLELLAND J.F.T., JENSEN C., LIU J. 2004. Single-kernel maize analysis by near-infrared hyperspectral imaging. Transactions of the ASAE, 47(1): 311-320. https://doi.org/10.13031/2013.15856
Crossref Google Scholar

DELWICHE S.R., KIM M.S. 2000. Hyperspectral imaging for detection of scab in wheat. In: Biological Quality and Precision Agriculture II. Eds. J.A. DeShazer, G.E. Meyer. Proceedings Volume, 4203. Environmental and Industrial Sensing. https://doi.org/10.1117/12.411752
Crossref Google Scholar

DELWICHE S.R., KIM M.S., DONG Y. 2010. Damage and quality assessment in wheat by NIR hyperspectral imaging. In: Sensing for agriculture and food quality and safety II. Eds. M.S. Kim, S.-I. Tu, K. Chao. Proceedings Volume, 7676. SPIE Defense, Security, and Sensing. https://doi.org/10.1117/12.851150
Crossref Google Scholar

DELWICHE S.R., KIM M.S., DONG Y. 2011. Fusarium damage assessment in wheat kernels by Vis/NIR hyperspectral imaging. Sensing and Instrumentation for Food Quality and Safety, 5(2): 63-71. https://doi.org/10.1007/s11694-011-9112-x
Crossref Google Scholar

DOWELL F.E., RAM M.S., SEITZ L.M. 1999. Predicting scab, vomitoxin, and ergosterol in single wheat kernels using near-infrared spectroscopy. Cereal Chemistry, 76(4): 573-576. https://doi.org/10.1094/CCHEM.1999.76.4.573
Crossref Google Scholar

GĄSIOROWSKI H. 1997. Jęczmień. Chemia i technologia. Wyd. I. Państwowe Wydawnictwo Rolnicze i Leśne, Poznań. Google Scholar

GIROLAMO A. DE, LIPPOLIS V., NORDKVIST E., VISCONTI A. 2009. Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-Transform Near Infrared (FT-NIR) spectroscopy. Food Additives & Contaminants. Part A. Chemistry, Analysis, Control, Exposure and Risk Assessment, 26(6): 907-917. https://doi.org/10.1080/02652030902788946
Crossref Google Scholar

NG H.F., WILCKE W.F., MOREY R.V., LANG J.P. 1998. Machine vision evaluation of corn kernel mechanical and mold damage. Transactions of the ASAE, 41(2): 415-420. https://doi.org/10.13031/2013.17166
Crossref Google Scholar

PEARSON T.C., WICKLOW D.T. 2006. Detection of corn kernels infected by fungi. Transactions of the ASABE, 49(4): 1235-1245. https://doi.org/10.13031/2013.21723
Crossref Google Scholar

POLDER G., HEIJDEN G.W.A.M. VAN DER, WAALWIJK C., YOUNG I.T. 2005. Detection of Fusarium in single wheat kernels using spectral imaging. Seed Science & Technology, 33(3): 655-668. https://doi.org/10.15258/sst.2005.33.3.13
Crossref Google Scholar

SINGH C.B., JAYAS D.S., PALIWAL J., WHITE N.D.G. 2012. Fungal damage detection in wheat using short-wave near-infrared hyperspectral and digital colour imaging. International Journal of Food Properties, 15(1): 11-24. https://doi.org/10.1080/10942911003687223
Crossref Google Scholar

SINGH C.B., JAYAS D.S., PALIWAL J., WHITE N.D.G. 2007. Fungal detection in wheat using near-infrared hyperspectral imaging. Transactions of the ASABE, 50(6): 2171-2176. https://doi.org/10.13031/2013.24077
Crossref Google Scholar

TADEUSIEWICZ R., KOROHODA P. 1997. Komputerowa analiza i przetwarzanie obrazów. Wydawnictwo Fundacji Postępu Telekomunikacji, Kraków. Google Scholar

THOMAS S., WAHABZADA M., KUSKA M.T., RASCHER U., MAHLEIN A.K. 2017. Observation Google Scholar

of plant–pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements. Functional Plant Biology, 44(1): 23-34. https://doi.org/10.1071/FP16127
Crossref Google Scholar

Download

Published

2024-12-02

Cited by

Zapotoczny, P. (2024). Development of a method for assessing the quality of barley for brewing using hyperspectral imaging. Technical Sciences, 27(27), 357–375. https://doi.org/10.31648/ts.10111