Description of food surfaces and microstructural changes using fractal image texture analysis

Quevedo, Roberto; Carlos, López-G.; Aguilera, José Miguel; Cadoche, Laura

doi:10.1016/s0260-8774(01)00177-7

Cited by 122 publications

(76 citation statements)

References 35 publications

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“…This has been possible because the texture of images is usually determined by analyzing the surface intensity obtained by plotting the (x, y) pixel coordinates against the gray level of each pixel (z axis). As a result, the changes in pixel valué intensity reflect the texture of the image, which might contain information about the color and the geometric structure of the objects in the image (Quevedo et al, 2002;Du and Sun, 2004;Zheng et al, 2006;Gonzales-Barron and Butler, 2008). Until now, image texture analysis has been employed to quantify the nonhomogenous distribution of the L* color in fresh-cut products with a cubical shape (2 cm x 2 cm x 2 cm) (Quevedo et al, 2009a,b,c).…”

Section: Introductionmentioning

confidence: 99%

A multispectral vision system to evaluate enzymatic browning in fresh-cut apple slices

Lunadei

Galleguillos

Diezma

et al. 2011

Postharvest Biology and Technology

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Keywords:Fresh-cut apples Enzymatic browning CIÉ L*a*f>* color space Multispectral images Image analysisThe main objective of this study was to develop a visión system that is able to classify fresh-cut apple slices according to the development of enzymatic browning. The experiment was carried out on 'Granny Smith' apple slices stored at 7.5 °C for 9 days (n = 120). Twenty-four samples were analyzed per day: at zero time and after storage for 1, 3, 7 and 9 days, which corresponds to treatments to, ti, h, ti and t% respectively. Multispectral images were acquired from the samples by employing a 3-CCD camera centered at the infrared (IR, 800nm), red (R, 680nm) and blue (B, 450nm) wavelengths. Apple slices were evaluated visually according to a visual color scale of 1 -5 (where 1 corresponds to fresh samples without any browning and 5 to samples with severe discoloration), to obtain a sensory evaluation Índex (¡SE) for each sample. Finally, for each sample and for each treatment, visible (VIS) relative reflectance spectra (360-740 nm) were obtained. In order to identify the most related wavelengths to enzymatic browning evolution, unsupervised pattern recognition analysis of VIS reflectance spectra was performed by principal components analysis (PCA) on the autoscaled data. Máximum loading valúes corresponding to the B and R áreas were observed. Therefore, a classification procedure was applied to the relative histograms of the following monochromatic images (virtual images), which were computed pixel by pixel: (R-B)/(R + B),R-B and B/R. Inall cases,a non-supervised classification procedure was able to genérate three image-based browningreference classes (BRC): Cluster A (corresponding to the to samples), ClusterB (ti andt3 samples) and Cluster C(t7 and tg samples). Aninternal and anexternal validation (n = 120) were carried out, and the best classifications were obtained with the (R -B)/(R + B) and B/R image histograms (internal validation: 99.2% of samples correctly classified for both virtual images; external validation: 84% with (R -B)/(R + B) and 81% with B/R). The camera classification was evaluated according to the colorimetric measurements, which were usually utilized to evalúate enzymatic browning development (CIÉ L'a'b* color parameters and browning Índex, BI) and according to ¡SE-For both validation phases a*, b*, BI and ¡SE increased while I* valúes decreased with image-based class number, thereby reflecting their browning state.

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Section: Introductionmentioning

confidence: 99%

A multispectral vision system to evaluate enzymatic browning in fresh-cut apple slices

Lunadei

Galleguillos

Diezma

et al. 2011

Postharvest Biology and Technology

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“…The spatial distribution of the β-carotene introduced by impregnation was observed using a tri-ocular light microscope (American Optical Series 10 Trinocular Microscope, NY, USA) coupled to a Nikon SMZ-2T camera (Nikon Japan), and was used to generate images at intervals along the distance of diffusion after 30, 60, 90, and 120 min of impregnation. Observations were made using light source (6 V, 15 W incandescent bulbs) with an illumination angle of 60°with respect to the horizontal surface (Quevedo et al 2002). The objects being observed were magnified 4X, and the exploration area was 5×0.3 mm.…”

Section: Diffusion Coefficients In the Impregnation Processmentioning

confidence: 99%

“…A distinctive feature of non-steady-state diffusion processes is the presence of non-linear concentration gradients for the species that is diffusing. Accordingly, image analysis and fractal geometry can describe processes associated with irregularly shaped structures, and thereby explain phenomena associated with the drying and impregnation of biological tissues with solutes (Quevedo et al 2002).…”

Section: Introductionmentioning

confidence: 99%

“…According to Quevedo et al (2008), the texture of images usually called the texture feature (TF) provides an important means for pattern recognition to characterize the arrangement of basic constituents of a surface material. The TF is useful for the analysis and description of natural scenes and environments and for the classification or categorization of pictorial data (Quevedo et al 2002;Santacruz-Vázquez et al 2007;Valous et al 2009). The TF can be determined by analyzing the surface intensity (SI), which is in turn obtained by plotting the pixel coordinates (x and y axes) against the gray level of each pixel (z axis) (Valous et al 2009).…”

Section: Introductionmentioning

confidence: 99%

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The spatial distribution of β-carotene impregnated in apple slices determined using image and fractal analysis

Santacruz-Vázquez

2013

J Food Sci Technol

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Changes in the concentration profiles of β-carotene caused by diffusion through parenchymatic dried apple tissue were characterized by image and fractal analysis. Apple slices were dried by convection, and then impregnated with an aqueous β-carotene solution. Scanning electron microscopy images of dried apple slices were captured and the fractal dimension (FD) values of the textures of the images were obtained (FD SEM ). It was observed that the microstructure of the foodstuff being impregnated have an important effect on the impregnation phenomenon, generating irregular concentration profiles of β-carotene, which are numerically described by the fractal dimension FD PROFILES and are related to the diffusion process during impregnation in dried edible tissue.

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“…Higher-order statistics estimate the properties of three or more pixels occurring at specific locations relative to each other. However, several texture description methods are based on the Fourier spectrum, wavelet transform and fractal dimension (Li et al, 2001;Quevedo et al, 2002). The success of these techniques lies in the type of transform used to extract textural features from the image (Bharati et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Application of image texture analysis for varietal classification of barley

Zapotoczny

2012

International Agrophysics

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A b s t r a c t. This paper presents the results of a study into the use of the texture parameters of barley kernel images in varietal classification. A total of more than 270 textures have been calculated from the surface of single kernels and bulk grain. The measurements were performed in four channels from a 24 bit image. The results were processed statistically by variable reduction and general discriminant analysis. Classification accuracy was more than 99%.K e y w o r d s: digital image analysis, automated kernel grading, image texture analysis, discrimination, barley

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Description of food surfaces and microstructural changes using fractal image texture analysis

Cited by 122 publications

References 35 publications

A multispectral vision system to evaluate enzymatic browning in fresh-cut apple slices

A multispectral vision system to evaluate enzymatic browning in fresh-cut apple slices

The spatial distribution of β-carotene impregnated in apple slices determined using image and fractal analysis

Application of image texture analysis for varietal classification of barley

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