An application for the classification of egg quality and haugh unit based on characteristic egg features using machine learning models

Sehirli, Eftal; Arslan, Kübra

doi:10.1016/j.eswa.2022.117692

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…In traditional practice, HU are used as an indicator of egg freshness. This measurement requires the breaking of egg samples to measure the height of the albumen layer (Narushin et al, 2021;Sehirli & Arslan, 2022). The HSI has been used to non-destructively determine egg freshness to categorize eggs (Özdoğan et al, 2021;Suktanarak & Teerachaichayut, 2017;Xu et al, 2022;Zhang et al, 2022aZhang et al, , 2015.…”

Section: Hyperspectral Imagingmentioning

confidence: 99%

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Ahmed,

Hossainy,

Khaliduzzaman

et al. 2023

Comp Rev Food Sci Food Safe

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The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground‐breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non‐destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non‐destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.

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Section: Hyperspectral Imagingmentioning

confidence: 99%

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Ahmed,

Hossainy,

Khaliduzzaman

et al. 2023

Comp Rev Food Sci Food Safe

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“…Yolk color was evaluated using a Roche color range consisting of 15 yellow tones. Haugh unit (Sehirli & Arslan, 2022) is related to the albumen height and egg weight. Haugh unit score was calculated using the following Formula 2, where the height of the albumen in mm and the weight of the egg in g (Monira et al, 2003) (2)…”

Section: Albumen Weight Egg Weight Yolk Weight Shell Weightmentioning

confidence: 99%

Quality evaluation of table eggs under different rearing systems in China

et al. 2023

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Because of the impact of the COVID-19 pandemic, more and more people are choosing to buy food online, including eggs. Although this mode of shopping is very modern, many Chinese consumers lack scientific knowledge when selecting eggs. In this study, we used the multivariate statistical analysis and sensory analysis to evaluate and compare the qualities of online sale of free-range and cage eggs. How feeding conditions influence the quality of eggs and how physical characteristics influence the price of eggs were also studied in this work. Our research showed that there is a lack of scientific support for distinguish freerange eggs from cage eggs on the appearance, the color of the yolk does not represent the amount of protein in yolk which may actually be affected by fodders. Moreover, the nutritional quality of free-range eggs is no better than that of cage eggs. Sensory analysis showed that free-range eggs taste better, which is likely because of the higher yolk ratio. Multivariate linear regression analysis showed that Rearing systems, fodder type and yolk color have significant impact on the price (Price = 0.428 × Rearing system −0.235 × Fodder type + 0.191 × Yolk color).

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“…In this study, four commonly used supervised classification algorithms such as SVM, KNN, LDA, and LR were applied. These four classification models were commonly used in the supervised classification of hyperspectral images and high computational efficiency was obtained [48][49][50]. The four classification algorithms described above were used in MATLAB 2020b (The MathWorks, Natick, USA) to distinguish between normal and ischaemic necrotic sites of small intestinal tissue.…”

Section: Classifiers and Metricsmentioning

confidence: 99%

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning

Zhang,

Xue,

Xie

et al. 2023

Journal of Biophotonics

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Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter‐patient variability. Building an automatic identification model using a small dataset presents a crucial challenge in obtaining a strong generalization of the model. This study aimed to explore the performance of hyperspectral imaging and transfer learning techniques in the automatic identification of normal and ischemic necrotic sites in small intestinal tissue. Hyperspectral data of small intestinal tissues were collected from eight white rabbit samples. The transfer component analysis (TCA) method was performed to transfer learning on hyperspectral data between different samples and the variability of data distribution between samples was reduced. The results showed that the TCA transfer learning method improved the accuracy of the classification model with less training data. This study provided a reliable method for single‐sample modelling to detect necrotic sites in small intestinal tissue .

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An application for the classification of egg quality and haugh unit based on characteristic egg features using machine learning models

Cited by 12 publications

References 13 publications

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Quality evaluation of table eggs under different rearing systems in China

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning

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