Three-Dimensional Image Reconstruction Procedure for Food Microstructure Evaluation

Ding, Kexiang; Gunasekaran, Sundaram

doi:10.1007/978-94-011-5048-4_13

Cited by 9 publications

(11 citation statements)

References 11 publications

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“…The resolution in the z ‐direction is the most important in acquiring a series of 2‐D layered images. The resolution in the z ‐direction depends on the numerical aperture of the lens, the degree to which the pinhole is open, and the wavelength of the laser light (Ding and Gunasekaran 1998). The minimum z ‐axial resolution and maximum observation depth achieved by CLSM used in this study was 50 nm and 200 μm, respectively.…”

Section: Resultsmentioning

confidence: 99%

Effect of Surface Roughness on Retention and Removal of Escherichia coli O157:H7 on Surfaces of Selected Fruits

Wang

Feng

Liang

et al. 2009

Journal of Food Science

135

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This study was undertaken to evaluate the effect of surface roughness on the attachment and removal of Escherichia coli O157:H7 on selected fruit and metal surfaces. A new method to determine surface roughness was developed using confocal laser scanning microscopy (CLSM). A series of 2-D layered images were taken by CLSM optical slicing of the surfaces of Golden Delicious apples, navel oranges, avocadoes, and cantaloupes. The average roughness (R a ) of the fruit surfaces was assessed by reconstructing a series of 2-D images into 3-D images. A cocktail of 5 E. coli O157:H7 strains were spot inoculated onto fruit skin surfaces with different R a . The fruits were then treated with acidic electrolyzed water (AEW), peroxyacetic acid (POAA), and sterilized deionized water. Aluminum stubs with different R a values as a model system were also spot inoculated with E. coli O157:H7 and subjected to a sonication treatment. Test results indicated that there was a positive linear correlation between R a and adhesion rate of E. coli O157:H7, and a negative correlation between R a and the efficacy of inactivation by AEW and POAA, respectively, on fruit surfaces. A linear increase of residual bacteria population with increased surface roughness of aluminum stubs was also observed. The relationship between surface roughness and surface hydrophobicity was negative linear for the aluminum stubs, but was quadratic for the 4 fruits. The environmental scanning electron microscopy images showed that bacteria tended to attach to or be entrapped in the grooves or cavities of fruits, which provided protection to the cells against washing treatments.

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Section: Resultsmentioning

confidence: 99%

Effect of Surface Roughness on Retention and Removal of Escherichia coli O157:H7 on Surfaces of Selected Fruits

Wang

Feng

Liang

et al. 2009

Journal of Food Science

135

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“…However, such techniques do not allow a quantitative determination of microstructure parameters that would enable the characteristics of the cheese structure and its irregularities to be described. Ding and Gunasekaran (1998) developed a 2-dimensional to 3-dimensional image reconstruction procedure for the study of Cheddar cheese microstructure by confocal scanning laser microscopy, quantifying with good precision the variation in particle size and shape between the 2-dimensional and the reconstructed 3-dimensional images. Later, Everett and Auty (2008) pointed to the need to use 3-dimensional images to describe the complex protein matrix structure in a more real and complete way because 2-dimensional images provide information only on the particle shape and surface in the first plane, without showing how the particles link together or their distribution along the x-, y-, and z-axes.…”

Section: Introductionmentioning

confidence: 99%

Hot topic: Microstructure quantification by scanning electron microscopy and image analysis of goat cheese curd

Rovira

López

Ferrandini

et al. 2011

Journal of Dairy Science

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Five microstructural parameters of goat cheese curd (number of pores, their area and perimeter, strand thickness, and porosity) were studied by scanning electron microscopy and image analysis. Image analysis was used to characterize and quantify differences in all parameters and to provide a procedure for the measurement of strand thickness. The micrographs provided visual evidence of differences in the protein matrix and were quantified by image analysis at 3 production times: 34 ± 1 min (cutting), 154 ± 6 min (before molding), and 293 ± 35 min (after pressing). The data showed that this procedure is an adequate tool for quantifying differences in the parameters analyzed in industrial samples despite their natural heterogeneity. The procedure was reproducible and repetitive for the first 2 production times because no significant intragroup differences were observed. Significant differences were found when comparing the values of the microstructure parameters analyzed at 34 ± 1 min and those corresponding to 154 ± 6 min and 293 ± 35 min, but no significant differences between samples analyzed at 154 ± 6 min and 293 ± 35 min were found. All microstructure parameters analyzed were related at a significance level of at least 95%. This procedure enables the characterization of the microstructure of industrial goat cheese curd.

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“…For example, cell analysis is another important application field of the CBMIA approaches, referring to cell migration analysis (Boucher et al 1998), multiple cell detection (Yao et al 2005;Pasquale and Stander 2009), blood cell classification (Tuzel et al 2007), cell segmentation (Korzynska et al 2007), semen cell quality analysis (Witkowski 2013), cell nucleus detection (Nogueira and Teofilo 2014;John et al 2016), stem cell analysis (Huang et al 2016;Zhang et al 2016;Li et al 2017), antibody analysis (Soda et al 2009;Neuman et al 2013), pathological tissue analysis (Tasoulis et al 2014;Jothi and Rajam 2017) and so on. Material analysis is also an important application domain of the CBMIA methods, including food microstructure analysis (Ding and Gunasekaran 1998), collagen fiber analysis (Elbischger et al 2004), metallography image analysis (Grzegorzek 2010), membranes porosity evaluation (Chwojnowski et al 2012), cement quality analysis (Wang et al 2014) and so on. Furthermore, because medial image analysis needs to solve many similar technical problems as the CBMIA tasks , it can also share a lot of research strategies from the CBMIA field and extend them to related application tasks, like cerebral cortical analysis (Suri et al 2002), MRI image analysis (Balafar et al 2010a, b), retinal vessel analysis ), brain network analysis (Qi et al 2015(Qi et al , 2016 and so on.…”

Section: Potential Application Fields Of Cbmia Methodologymentioning

confidence: 99%

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Wang

2017

Artif Intell Rev

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Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semior full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future devel-

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Three-Dimensional Image Reconstruction Procedure for Food Microstructure Evaluation

Cited by 9 publications

References 11 publications

Effect of Surface Roughness on Retention and Removal of Escherichia coli O157:H7 on Surfaces of Selected Fruits

Effect of Surface Roughness on Retention and Removal of Escherichia coli O157:H7 on Surfaces of Selected Fruits

Hot topic: Microstructure quantification by scanning electron microscopy and image analysis of goat cheese curd

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

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