Quantitative analysis of nanostructures’ shape and distribution in micrographs using image analysis

Impoco, G.; Fucí, N.; Pasta, Catia; Caccamo, M.; Licitra, G.

doi:10.1016/j.compag.2012.02.013

Cited by 13 publications

(8 citation statements)

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“…Micrograph images of dairy samples can be quantified through the morphometry of individual objects or particles (Fuc a, Pasta, Impoco, Caccamo, & Licitra, 2013;Gunasekaran & Ding, 1999;Impoco, Fuc a, Pasta, Caccamo, & Licitra, 2012;Rovira et al, 2011). When a protein network cannot be easily segmented, gray level texture analysis can be used to discern the variations of gray levels from confocal images (Ercili-Cura et al, 2013;Fenoul et al, 2008;Morand et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Characterization of the microstructure of dairy systems using automated image analysis

et al. 2015

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International audienceA sound understanding of the microstructure of dairy products is of great importance in order to predict and control their properties and final quality. The aim of this study was to develop an automated image analysis procedure to characterize the microstructure of different dairy systems. A high pressure freezing coupled with freeze-substitution (HPF-FS) protocol was applied prior to transmission electron microscopy(TEM) in order to minimize any modification of the microstructure of the dairy systems investigated. The developed image analysis procedure was first validated on synthetic images of suspensions, and then on two types of concentrated milk suspensions. Microstructural data relating to casein micelles in milk suspensions were taken from the literature. The established procedure was then applied to the two corresponding rennet-induced milk gels, prepared from the same milk concentrates used for suspensions preparation. The automated image analysis procedure allowed the reliable estimation of several characteristic microstructural parameters including area fraction, porosity, boundary length per unit area, particle aggregate size, inter-particle aggregate distance and tortuosity. The relative ease of estimating these microstructural parameters from the automated image analysis method could make it useful for routine measurements of milk gels. Moreover, the method enabled a useful discrimination between two different types of milk gels. This novel approach can contribute to a better understanding of the effects of processing on the structure-property relationships in dairy products, and may be applied to other food systems

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Section: Introductionmentioning

confidence: 99%

Characterization of the microstructure of dairy systems using automated image analysis

et al. 2015

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“…Extraction with watermiscible organic solvents, such as methanol or dilution on buffer was used. (20,83,91) In these procedures the cleanup is important. The methanolic extract can be purified just by allowing it to stand for a few minutes to remove larger impurities.…”

Section: Sample Preparation For Spectroscopic Spectrometry and Chromentioning

confidence: 99%

“…A set of mathematical descriptors was devised to describe structural relationships between nanostructures in micrographs. (91) The ability of these descriptors to measure the size, shape, and distribution of nanostructures was assessed on confocal laser scanning microscopic (CLSM) images of treated milk, in which fat was stained. Comparisons of scores obtained from automatic descriptors and visual observation showed that these methods generate similar groupings among milk treatments.…”

Section: Imaging Techniquesmentioning

confidence: 99%

“…Impoco et al(91) CARS, coherent anti-Stokes Raman scattering microscopy; ESEM/EDS, environmental scanning electron microscopy/X-ray microprobe of an energy dispersive system;FA 4 , asymmetrical flow field-flow fractionation; ICP-MS, inductively coupled-plasma mass spectrometry; LC-MS/MS, liquid chromatography-tandem mass spectroscopy; MALS, multi-angle light scattering detection; PDI: polydispersity index; TEM, transmission electron microscopy; XRD, X-ray diffraction. Encyclopedia of Analytical Chemistry, Online © 2006-2013 John Wiley & Sons, Ltd.…”

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confidence: 99%

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Nanoparticles in Foods, Determination of

Blasco

Picó

2013

Encyclopedia of Analytical Chemistry

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This article discusses the state of the art in the identification and determination of nanoparticles in food. It is of utmost importance to determine, in addition to the particle‐size distribution, the chemical composition as well as the physical and chemical properties of the nanomaterials within the sample. After a brief description of what nanomaterials are and how to classify them, sources and routes of nanomaterials to food are discussed. The article then follows the debate on legislative consideration, which has emerged very recently. Its main aim is to deliver information on how nanomaterials are determined in food because of the number of challenges that arise when analyzing food and beverages for nanomaterials. No single technique can provide all relevant information. Therefore, a range of analytical techniques is required for detection and characterization of nanomaterials in foods. The principles of the techniques together with their advantages and drawbacks, and reported applications concerning nanoparticles or related nanocompounds are discussed.

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“…The image analysis method in thin-layer chromatography was developed for the rapid determination of sibutramine hydrochloride adulterated in herbal slimming products (Phattanawasin, Sotanaphun, Sukwattanasinit, Akkarawaranthorn, & Kitchaiya, 2012). It was reported that the image analysis was employed to the quantitative analysis of nanostructures' shape and distribution in micrographs (Impoco, Fucà, Pasta, Caccamo, & Licitra, 2012). Wavelet texture in image analysis was applied to the classification of paper surface quality (Reis & Bauer, 2010).…”

Section: Introductionmentioning

confidence: 99%