Cheese quality evaluation by image segmentation

Danev, Angel; Bosakova-Ardenska, Atanaska; Boyanova, Petya; Panayotov, P.; Lena, Kostadinova-Georgieva

doi:10.1145/3345252.3345258

Cited by 5 publications

(2 citation statements)

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“…It is concluded that the 4 × 4 grid size (i.e., the image is divided into 16 pieces) and one of algorithms for automatic thresholdingmetric, clustering or inter-class variance are preferred in order to achieve a very high (above 90%) correlation with experts' assessment regarding the quantity and distribution of Penicillium roqueforti mold in blue cheese. A special characteristic of Bulgarian white cheese in brine related to its structure is effectively evaluated using images processing in HSI (Hue-Saturation-Intensity) color space [27]. This characteristic is named "Porcelanov lom", and it is related to the presence of specific areas that look like parts of a broken porcelain cup on the broken surface of the block of cheese.…”

Section: Using Digital Images For Cheese Quality Evaluationmentioning

confidence: 99%

Recent Trends in Computer Vision for Cheese Quality Evaluation

Bosakova-Ardenska

2024

Ciees 2023

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Section: Using Digital Images For Cheese Quality Evaluationmentioning

confidence: 99%

Recent Trends in Computer Vision for Cheese Quality Evaluation

Bosakova-Ardenska

2024

Ciees 2023

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“…This review discusses advances in the analysis of the microstructure of cheese, including new methods and how they can be applied to understand and improve the quality of cheese. Simultaneous assessment of the texture and color of cheeses can be performed using the segmentation algorithm of the cheese fracture image in the color space [19]. For expert evaluation, a specially developed Likert scale was used.…”

Section: Introductionmentioning

confidence: 99%

Milk curd self-segmentation in cheesemaking tank

Smykov¹

2022

Food systems

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The purpose of this work is to describe and study the previously unknown phenomenon of self-segmentation of a milk curd in an open-type cheesemaking tank. Based on the analysis of the kinetics of gel formation, it has been determined that self-segmentation of the gel begins near the gel point, develops over several tens of seconds, and becomes stable as the gel condenses. The segments in the milk curd do not have a definite regular shape; their average size varies from 5 to 50 cm. The shape and size of the segments do not repeat and do not correlate with the type of cheese being produced. The displacement of the segments of the milk curd in the cheesemaking tank relative to each other in height is from 0.5 to 2 mm. The width of the boundary layer between the curd segments increases during the secondary phase of gelation from 3 to 10 mm. As a result of experimental studies, it has been shown that self-segmentation of milk gel is caused by thermogravitational convection, which forms Benard convection cells. A description of a possible mechanism of milk gel self-segmentation in open-type cheesemaking tanks is proposed. The effective role of fat globules in the mechanism of self-segmentation of the milk curd has been noted. It has been suggested that self-segmentation of the milk curd in the cheesemaking tank may cause some organoleptic defects in the finished cheese, in particular inhomogeneity of texture and color.

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Cut surface of Bulgarian white brined cheese evaluation by image analysis in HSI color space

Bosakova-Ardenska

Andreeva

Danev

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

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This paper presents a research over possibilities to evaluate a cut surface of white brined cheese by images analysis. Samples from seven trademarks of Bulgarian white cheese in brine are evaluated by experts and their cut surfaces are captured with digital camera. A Java application for color images processing in HSI color space is developed. The application executes multilevel thresholding and produces grayscale images with four colors – white, black, light gray and dark gray. The images of all tested cheese samples are processed with this application and there are calculated ratios of pixels with the same color to all pixels in the processed images (Kdiv for white, black, light gray and dark gray colors). The cut surfaces of cheese samples are evaluated by experts and it is performed a correlation analysis between experts’ estimation and the coefficients Kdiv. The results show that there are a significant correlation between experts’ estimation and the pixels with dark gray color (their coefficient Kdiv) in the processed images.

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Cheese quality evaluation by image segmentation

Cited by 5 publications

References 5 publications

Recent Trends in Computer Vision for Cheese Quality Evaluation

Recent Trends in Computer Vision for Cheese Quality Evaluation

Milk curd self-segmentation in cheesemaking tank

Cut surface of Bulgarian white brined cheese evaluation by image analysis in HSI color space

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