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Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.
Anal Chim Acta. 2009 Oct 27; 653(2):121-30.AC

Abstract

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images, PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

Authors+Show Affiliations

Department of Food Science, Stellenbosch University, Private Bag X1, Matieland (Stellenbosch), 7602, South Africa.No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

19808104

Citation

Williams, Paul, et al. "Maize Kernel Hardness Classification By Near Infrared (NIR) Hyperspectral Imaging and Multivariate Data Analysis." Analytica Chimica Acta, vol. 653, no. 2, 2009, pp. 121-30.
Williams P, Geladi P, Fox G, et al. Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Anal Chim Acta. 2009;653(2):121-30.
Williams, P., Geladi, P., Fox, G., & Manley, M. (2009). Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Analytica Chimica Acta, 653(2), 121-30. https://doi.org/10.1016/j.aca.2009.09.005
Williams P, et al. Maize Kernel Hardness Classification By Near Infrared (NIR) Hyperspectral Imaging and Multivariate Data Analysis. Anal Chim Acta. 2009 Oct 27;653(2):121-30. PubMed PMID: 19808104.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. AU - Williams,Paul, AU - Geladi,Paul, AU - Fox,Glen, AU - Manley,Marena, Y1 - 2009/09/06/ PY - 2009/05/15/received PY - 2009/08/29/revised PY - 2009/09/02/accepted PY - 2009/10/8/entrez PY - 2009/10/8/pubmed PY - 2010/2/3/medline SP - 121 EP - 30 JF - Analytica chimica acta JO - Anal Chim Acta VL - 653 IS - 2 N2 - The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images, PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses. SN - 1873-4324 UR - https://www.unboundmedicine.com/medline/citation/19808104/Maize_kernel_hardness_classification_by_near_infrared__NIR__hyperspectral_imaging_and_multivariate_data_analysis_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0003-2670(09)01191-X DB - PRIME DP - Unbound Medicine ER -