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Application of Image Texture Analysis for Evaluation of X-Ray Images of Fungal-Infected Maize Kernels

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Abstract

The feasibility of image texture analysis to evaluate X-ray images of fungal-infected maize kernels was investigated. X-ray images of maize kernels infected with Fusarium verticillioides and control kernels were acquired using high-resolution X-ray micro-computed tomography. After image acquisition and pre-processing, several algorithms were developed to extract image textural features from selected two-dimensional (2D) images of the kernels. Four first-order statistics (mean, standard deviation, kurtosis and skewness) and four grey level co-occurrence matrix (GLCM) features (correlation, energy, homogeneity and contrast) were extracted from the side, front and top views of each kernel and used as inputs for principal component analysis (PCA). The first-order statistical image features gave a better separation of the control from infected kernels on day 8 post-inoculation. Classification models were developed using partial least squares discriminant analysis (PLS-DA), and accuracies of 67 and 73% were achieved using first-order statistical features and GLCM extracted features, respectively. This work provides information on the possible application of image texture as method for analysing X-ray images.

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References

  • Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdisciplinary Reviews. Comput Stat 2:433–459

    Article  Google Scholar 

  • Afolabi C, Ojiambo P, Ekpo E, Menkir A, Bandyopadhyay R (2007) Evaluation of maize inbred lines for resistance to Fusarium ear rot and fumonisin accumulation in grain in tropical Africa. Plant Dis 91:279–286

    Article  CAS  Google Scholar 

  • Baker DR, Mancini L, Polacci M, Higgins MD, Gualda GAR, Hill RJ, Rivers ML (2012) An introduction to the application of X-ray microtomography to the three-dimensional study of igneous rocks. Lithos 148:262–276. https://doi.org/10.1016/j.lithos.2012.06.008

    Article  CAS  Google Scholar 

  • Basset O, Buquet B, Abouelkaram SD, Delachartre P, Culioli J (2000) Application of texture image analysis for the classification of bovine meat. Food Chem 69:437–445. https://doi.org/10.1016/s0308-8146(00)00057-1

    Article  CAS  Google Scholar 

  • Bharati MH, Liu JJ, MacGregor JF (2004) Image texture analysis: methods and comparisons. Chemometrics Intellig Lab Syst 72:57–71. https://doi.org/10.1016/j.chemolab.2004.02.005

    Article  CAS  Google Scholar 

  • Bourne M (2002) Food texture and viscosity: concept and measurement. Academic press, 2nd edition, Cornell University, Geneva, New York

  • Brereton RG, Lloyd GR (2014) Partial least squares discriminant analysis: taking the magic away. J Chemom 28:213–225

    Article  CAS  Google Scholar 

  • Brosnan T, Sun DW (2004) Improving quality inspection of food products by computer vision––a review. J Food Eng 61:3–16

    Article  Google Scholar 

  • Chang C (1988) Measuring density and porosity of grain kernels using a gas pycnometer. Cereal Chem 65:13–15

    Google Scholar 

  • Cnudde V, Boone M (2013) High-resolution X-ray computed tomography in geosciences: a review of the current technology and applications. Earth Sci Rev 123:1–17

    Article  Google Scholar 

  • Dolezal AL, Obrian GR, Nielsen DM, Woloshuk CP, Boston RS, Payne GA (2013) Localization, morphology and transcriptional profile of Aspergillus flavus during seed colonization. Mol Plant Pathol 14:898–909

    Article  CAS  PubMed  Google Scholar 

  • Dombrink-Kurtzman M, Knutson C (1997) A study of maize endosperm hardness in relation to amylose content and susceptibility to damage. Cereal Chem 74:776–780

    Article  CAS  Google Scholar 

  • Du CJ, Sun DW (2004) Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci Technol 15:230–249

    Article  CAS  Google Scholar 

  • Duncan EK, Howard JR (2010) Biology of maize kernel infection by Fusarium verticilliodes. Am Phytopathol Soc 23:6–16

    CAS  Google Scholar 

  • Esbensen KH, Guyot D, Westad F, Houmoller LP (2002) Multivariate data analysis: in practice: an introduction to multivariate data analysis and experimental design. Multivariate Data Analysis, 5th edition, CAMO process AS, Oslo, Norway

  • Evers T, Millar S (2002) Cereal grain structure and development: some implications for quality. J Cereal Sci 36:261–284. https://doi.org/10.1006/jcrs.2002.0435

    Article  Google Scholar 

  • Fandohan P, Hell K, Marasas W, Wingfield M (2003) Infection of maize by Fusarium species and contamination with fumonisin in Africa. Afr J Biotechnol 2:570–579

    Article  CAS  Google Scholar 

  • Fernandez L, Castillero C, Aguilera J (2005) An application of image analysis to dehydration of apple discs. J Food Eng 67:185–193

    Article  Google Scholar 

  • Gadkari D (2004) Image quality analysis using GLCM. University of Central Florida, MSc thesis

    Google Scholar 

  • Gonzales-Barron U, Butler F (2008) Discrimination of crumb grain visual appearance of organic and non-organic bread loaves by image texture analysis. J Food Eng 84:480–488

    Article  Google Scholar 

  • Guelpa A, du Plessis A, Kidd M, Manley M (2015) Non-destructive estimation of maize (Zea mays L.) kernel hardness by means of an X-ray micro-computed tomography (μCT) density calibration. Food Bioprocess Technol 8:1419–1429

    Article  CAS  Google Scholar 

  • Guelpa A, du Plessis A, Manley M (2016) A high-throughput X-ray micro-computed tomography (μCT) approach for measuring single kernel maize (Zea mays L.) volumes and densities. J Cereal Sci 69:321–328

    Article  Google Scholar 

  • Gunasekaran S (1996) Computer vision technology for food quality assurance. Trends Food Sci Technol 7:245–256

    Article  CAS  Google Scholar 

  • Gustin JL, Jackson S, Williams C, Patel A, Armstrong P, Peter GF, Settles AM (2013) Analysis of maize (Zea mays) kernel density and volume using microcomputed tomography and single-kernel near-infrared spectroscopy. J Agric Food Chem 61:10872–10880

    Article  CAS  PubMed  Google Scholar 

  • Haralick RM, Shanmugam K (1973) Textural features for image classification. IEEE Trans Syst, Man,Cybern 3:610–621

    Article  Google Scholar 

  • Li J, Tan J, Martz F, Heymann H (1999) Image texture features as indicators of beef. Meat Sci 53:17–22

    Article  CAS  PubMed  Google Scholar 

  • Li X, He Y, Fang H (2007) Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy. J Food Eng 81:357–363

    Article  CAS  Google Scholar 

  • Lim KS, Barigou M (2004) X-ray micro-computed tomography of cellular food products. Food Res Int 37:1001–1012

    Article  Google Scholar 

  • Liu HF, Wu BH, Fan PG, Li SH, Li LS (2006) Sugar and acid concentrations in 98 grape cultivars analyzed by principal component analysis. J Sci Food Agric 86:1526–1536

    Article  CAS  Google Scholar 

  • Magwaza LS, Opara UL (2014) Investigating non-destructive quantification and characterization of pomegranate fruit internal structure using X-ray computed tomography. Postharvest Biol Technol 95:1–6

    Article  CAS  Google Scholar 

  • Majumdar S, Jayas D (2000) Classification of cereal grains using machine vision: III. Texture models. Trans ASAE 43:1681–1687

    Article  Google Scholar 

  • Marin S, Magan N, Ramos AJ, Sanchis V (2004) Fumonisin-producing strains of Fusarium: a review of their ecophysiology. J Food Prot 67:1792–1805

    Article  CAS  PubMed  Google Scholar 

  • Materka A, Strzelecki M (1998) Texture analysis methods–a review. Technical University of Iodz, Institute of Electronics, COST B11 report, Brussels:9–11

  • Mendoza F, Aguilera J (2004) Application of image analysis for classification of ripening bananas. J Food Sci 69:E471–E477

    Article  CAS  Google Scholar 

  • Mohoric A, Vergeldt F, Gerkerma E, Gv D, JRvd D, LJv V, As HV, Jv D (2009) The effect of rice kernel microstructure on cooking behaivour:a combined microCT and MRI study. Food Chem 115:1491–1499

    Article  CAS  Google Scholar 

  • Munkvold GP (2003) Cultural and genetic approaches to managing mycotoxins in maize. Annu Rev Phytopathol 41:99–116

    Article  CAS  PubMed  Google Scholar 

  • Naresh M, David A, Sanchis V (2004) The role of spoilage fungi in seed deterioration. In: Fungal biotechnology in agricultural, food and environmental application. Marcel Dekker, new York City, pp 311–322

  • Narvankar DS, Singh DS, White NDG (2009) Assessment of soft X-ray imaging for detection of fungal infection in wheat. Biosyst Eng Postharvest Technol 81:49–56

    Article  Google Scholar 

  • Orina I, Manley M, Williams PJ (2017) Use of high-resolution X-ray micro-computed tomography for the analysis of internal structural changes in maize infected with Fusarium verticillioides. Food Anal Methods 10:2919–2933

    Article  Google Scholar 

  • Paliwal J, Visen N, Jayas D, White N (2003) Cereal grain and dockage identification using machine vision. Biosyst Eng 85:51–57

    Article  Google Scholar 

  • Patel KK, Kar A, Jha S, Khan M (2012) Machine vision system: a tool for quality inspection of food and agricultural products. J Food Sci Technol 49:123–141

    Article  PubMed  Google Scholar 

  • Pearson T, Wicklow D (2006) Detection of corn kernels infected by fungi. Trans ASABE 49:1235–1245

    Article  Google Scholar 

  • Popovski S, Celar FA (2013) The impact of environmental factors on the infection of cereals with Fusarium species and mycotoxin production-a review. Acta Agric Slov 101:105–116

    Article  Google Scholar 

  • Prats-Montalbán J, De Juan A, Ferrer A (2011) Multivariate image analysis: a review with applications. Chemom Intell Lab Syst 107:1–23

    Article  CAS  Google Scholar 

  • Schoeman L, du Plessis A, Manley M (2016a) Non-destructive characterisation and quantification of the effect of conventional oven and forced convection continuous tumble (FCCT) roasting on the three-dimensional microstructure of whole wheat kernels using X-ray micro-computed tomography (μCT). J Food Eng 187:1–13

    Article  Google Scholar 

  • Schoeman L, Williams P, du Plessis A, Manley M (2016b) X-ray micro-computed tomography (μCT) for non-destructive characterisation of food microstructure. Trends Food Sci Technol 47:10–24. https://doi.org/10.1016/j.tifs.2015.10.016

    Article  CAS  Google Scholar 

  • Schoeman L, du Plessis A, Verboven P, Nicolaï BM, Cantre D, Manley M (2017) Effect of oven and forced convection continuous tumble (FCCT) roasting on the microstructure and dry milling properties of white maize. Innovative Food Sci Emerg Technol 44:54–66

    Article  Google Scholar 

  • Scott EU (2010) Digital image processing and analysis: human and computer vision applications with CVIPtools. CRC press, pp:456–463

  • Seitz L, Sauer D, Mohr H, Aldis D (1982) Fungal growth and dry matter loss during bin storage of high-moisture corn. Cereal Chem 59:9–14

    CAS  Google Scholar 

  • Skorton DJ, Melton HE, Pandian NG, Nichols J, Koyanagi S, Marcus ML, Collins SM, Kerber RE (1983) Detection of acute myocardial infarction in closed-chest dogs by analysis of regional two-dimensional echocardiographic gray-level distributions. Circ Res 52:36–44

    Article  CAS  PubMed  Google Scholar 

  • Suresh A, Neethirajan S (2015) Real-time 3D visualisation and quantitative analysis of internal structure of wheat kernels. J Cereal Sci 63:81–87

    Article  CAS  Google Scholar 

  • Tournier C, Grass M, Zope D, Salles C, Bertrand D (2012) Characterization of bread breakdown during mastication by image texture analysis. J Food Eng 113:615–622. https://doi.org/10.1016/j.jfoodeng.2012.07.015

    Article  Google Scholar 

  • Umbaugh SE (2010) Digital image processing and analysis: human and computer vision applications with CVIP tools. CRC press, Boca, Raton, pp 456–463

    Google Scholar 

  • Watson S, White P, Johnson L (2003) Description, development, structure, and composition of the corn kernel corn: chemistry and technology: 2nd edition. American Association of Cereal Chemists, St. Paul, pp 69–10

    Google Scholar 

  • Williams PJ, Geladi P, Britz TJ, Manley M (2012) Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis. J Cereal Sci 55:272–278

    Article  Google Scholar 

  • Winston PW, Bates DH (1960) Saturated solutions for the control of humidity in biological research. Ecology 41:232–237

    Article  Google Scholar 

  • Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometrics Intellig Lab Syst 2:37–52. https://doi.org/10.1016/0169-7439(87)80084-9

    Article  CAS  Google Scholar 

  • Zheng C, Sun D-W, Zheng L (2006a) Recent applications of image texture for evaluation of food qualities-a review. Trends Food Sci Technol 17:113–128

    Article  CAS  Google Scholar 

  • Zheng C, Sun D-W, Zheng L (2006b) Recent developments and applications of image features for food quality evaluation and inspection–a review. Trends Food Sci Technol 17:642–655

    Article  CAS  Google Scholar 

  • Zhu L-J, Hulya D, Gajula H, Gu M-H, Qiao-Quan L, Yong-Cheng S (2012) Study of kernel structure of high-amylose and wild-type rice by X-ray microtomography and SEM. J Cereal Sci 51:1–5

    Article  CAS  Google Scholar 

  • Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Paul SH (ed) Graphics gems IV. Academic press professional, Inc., pp 474–485

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Acknowledgements

This work is based on the research supported in part by the National Research Foundation of South Africa for the grant, Unique Grant No. 94031. The authors acknowledge the Department of Plant Pathology at Stellenbosch University for providing the materials necessary for accomplishing this work. The authors also wish to thank Agri-Hope Project and Schlumberger Foundation for their financial assistance.

Funding

This study was funded by the National Research Foundation of South Africa, Unique Grant No. 94031.

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Authors and Affiliations

  1. Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, (Stellenbosch), 7602, South Africa

    Irene Orina, Marena Manley & Paul J. Williams

  2. Department of Food Science and Technology, Jomo Kenyatta University of Agriculture and Technology, P.O BOX 62000-00200, Nairobi, Kenya

    Irene Orina

  3. Department of Chemistry and Bioscience, Aalborg University, Esbjerg, Denmark

    Sergey Kucheryavskiy

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  1. Irene Orina
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Correspondence to Paul J. Williams.

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Irene Orina declares that she has no conflict of interest. Marena Manley declares that she has no conflict of interest. Sergey Kucheryavskiy declares that he has no conflict of interest. Paul J. Williams declares that he has no conflict of interest.

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Orina, I., Manley, M., Kucheryavskiy, S. et al. Application of Image Texture Analysis for Evaluation of X-Ray Images of Fungal-Infected Maize Kernels. Food Anal. Methods 11, 2799–2815 (2018). https://doi.org/10.1007/s12161-018-1251-9

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