Non-destructive Evaluation of Bread Staling Using Gray Level Co-occurrence Matrices

Nouri, Mohammad Damghani; Nasehi, Behzad; Goudarzi, Mostafa; Mehdizadeh, Saman Abdanan

doi:10.1007/s12161-018-1319-6

Cited by 16 publications

(12 citation statements)

References 15 publications

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“…Recently, different GLCM-based analysis methods have been introduced for the extraction of textural features from the digital images of food samples (Andino, Pieniazek, & Messina, 2019;Perez Alvarado, Hussein, & Becker, 2016). Nouri, Nasehi, Goudarzi, and Abdanan Mehdizadeh (2018) proposed a texture-based image analysis method to successfully evaluate the staling rate of baguette bread during 5 days of storage. In another attempt, the effects of processing parameters (proofing time) on the quality parameters and texture features of bread were investigated with the help of an image-based textural technique.…”

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confidence: 99%

Nondestructive evaluation of baking parameters on pogácsa texture

Amani

Firtha

Jakab

et al. 2021

Journal of Texture Studies

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This study aimed to investigate the potential application of image texture processing method on visible crumb structure of salty cake pog acsa, which was prepared with different baking times (5 and 7 min) and temperatures (200, 215, and 230 C). For this purpose, changes in gray level co-occurrence matrix (GLCM) features including energy, contrast, correlation, homogeneity, and entropy were monitored and their relationship with the instrumental texture parameters (hardness, adhesiveness, cohesiveness, springiness, gumminess, and chewiness) were assessed. The pore ratios were also extracted and visualized using image processing technique. Texture profile parameters indicated strong correlation (p < .01) with the image pattern parameters in different pog acsa groups. Gumminess showed strong correlation with contrast (0.503), correlation (À0.498), and homogeneity (0.401). Hardness also exhibited correlation with contrast (0.517), entropy (0.341), and correlation (À0.476). The pore ratio showed marked variation in crumb structure when different times and temperatures were used. Baking at 230 C for 7 min maximized the pore ratio (0.56). Penalty analysis revealed that oiliness, pore structure, and color of products were linked with baking time and temperature. Overall, the results suggested that the GLCM-based technique had the potential to be used as a nondestructive method for rapid quality assessment of pog acsa.

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confidence: 99%

Nondestructive evaluation of baking parameters on pogácsa texture

Amani

Firtha

Jakab

et al. 2021

Journal of Texture Studies

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“…The method of extracting texture features based on GLCM is extremely classical in statistical analysis field, which was first proposed by Haralick in 1973. It gradually turned into a commonly used method and measurement technology in dealing with texture feature . GLCM gives the number of times between two related pixels with grayscale intensity values i and j in the image, which is actually a statistical form of the joint distribution of such two pixels.…”

Section: Methodsmentioning

confidence: 99%

Gingivitis identification via multichannel gray‐level co‐occurrence matrix and particle swarm optimization neural network

Wen

Jiang

Sun

et al. 2019

Int J Imaging Syst Tech

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The oral maintenance of patients with periodontal disease mainly depends on clinical examination. However, insufficient number of medical workers cannot carry out detailed oral health education for a large number of patients within limited time and provide these patients with proper and effective oral health nursing methods. In this research, our study put forward a new Artificial Intelligence (AI) based method to diagnose chronic gingivitis, which is based on multichannel gray-levelco-occurrence matrix (MGLCM) and particle swarm optimization neural network(PSONN). Meanwhile, different training algorithms were used as comparison groups.The data set contains 800 images: 400 chronic gingivitis images and 400 healthy gingiva images. The results certify that the specificity, sensitivity, precision, accuracy and F1 Score of MGLCM (PSONN as a classifier) method is 78.17%, 78.23%, 78.24% ,78.20%and 78.17%, respectively. The association of MGLCM and PSONN is more accurate and efficient than approaches: NBC, WN+SVM,ELM and CLAHE+ELM. K E Y W O R D Sartificial neural network, gingivitis identification, multichannel gray-level co-occurrence matrix, particle swarm optimization, pattern recognition

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“…Recently, Seul and Okarma [26] proposed to use the GLCM method as a texture classification approach for cleaning robots. Nouri et al [27] used GLCM texture features as indices for non-destructively assessing bread staling progress. The method has also been used for the detection of channel by seismic texture analysis [28].…”

Section: ) Examples Of Applicationsmentioning

confidence: 99%

Texture Feature Extraction Methods: A Survey

2019

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Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract-from raw images-meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropybased approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

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Non-destructive Evaluation of Bread Staling Using Gray Level Co-occurrence Matrices

Cited by 16 publications

References 15 publications

Nondestructive evaluation of baking parameters on pogácsa texture

Nondestructive evaluation of baking parameters on pogácsa texture

Gingivitis identification via multichannel gray‐level co‐occurrence matrix and particle swarm optimization neural network

Texture Feature Extraction Methods: A Survey

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